detection of sand ingress locations at the bottom of a well

专利摘要:
  It is a method for detecting sand ingress into a well hole which includes obtaining a sample data set, determining a plurality of frequency domain resources from the sample data set through a plurality of depth ranges , determine apresence of sand ingress in a first depth range of the plurality of depth ranges within the well hole based on the determination that the plurality of frequency domain resources through the first depth range is compatible with aingress of sand, and determine a presence of sand migration over a second depth range of the plurality of depths within the well hole based on the determination that the plurality of frequency domain resources across the second depth range is compatible with a sand migration signature. The sample data set is a sample of an acoustic signal that originates within a well bore that comprises a fluid, and where the sample data set is representative of the acoustic signal over a spectrumfrequency.
公开号:BR112020003742A2
申请号:R112020003742-7
申请日:2018-08-23
公开日:2020-09-01
发明作者:Tommy LANGNES；Pradyumna Thiruvenkatanathan
申请人:Bp Exploration Operating Company Limited；
IPC主号:

专利说明:

[0001] [0001] Within a hydrocarbon production well, various fluids such as hydrocarbons, water, gas and the like can be produced from the formation into the well bore. Fluid production can result in the movement of fluids in various downhole regions, including with underground formation, from formation into the well hole, and into the well hole itself. For example, some underground formations can release solids, generally called "sand", which can be produced together with the fluids in the well bore. These solids can cause a variety of problems, which include erosion, clogging of wells, contamination and damage to surface equipment, and the like. Sand production tends to be present when production formations are formed from weakly consolidated sand stones with weak unconfined compressive strength. In such formations, sand control failures can result in significant sand production, which may result in the need to interrupt well production to bring sand production down to acceptable levels. This can result in reduced oil production, and potentially result in a deferral of more than 75% of the well's production.
[0002] [0002] Efforts have been made to detect the movement of various fluids including those with particles in them within the well bore. For example, efforts were made to detect sand using acoustic point sensors placed on the surface of the well and attached to the production pipe. The produced sand particles that pass through the production pipe, together with the produced fluids (for example, oil, gas or water), come into contact with the pipe walls, especially at the curvatures and elbows of the production pipe. Such contact creates voltage waves that are captured as sound signals by the acoustic sensors mounted on the pipe wall. However, these detection methods only capture the presence of sand on or near surface equipment and are qualitative at most (for example, indicating the presence of sand only). BRIEF SUMMARY OF THE REVELATION
[0003] [0003] In some embodiments, a method for detecting ingress of sand into a well hole that includes obtaining a sample data set, determining a plurality of frequency domain resources from the sample data set through a plurality of depth ranges, determine a presence of ingress of sand in a first depth range of the plurality of depth ranges within the well hole based on the determination that the plurality of frequency domain resources across the first depth range is compatible with a sand ingress signature, and determine a presence of sand migration over a second depth range of the plurality of depths within the wellbore based on the determination that the plurality of frequency domain resources through the second depth range is compatible with a sand migration signature. The sample data set is a sample of an acoustic signal that originates within a well bore that comprises a fluid, and in which the sample data set is representative of the acoustic signal over a frequency spectrum.
[0004] [0004] In some embodiments, a system for determining a sand migration path comprises a receiver unit comprising a processor and a memory. The receiving unit is configured to receive a first signal from a sensor arranged in a well hole, and a processing application is stored in memory. The processing application, when run on the processor, configures the processor to: receive the first signal from the sensor, where the signal comprises an indication of an acoustic signal received in a plurality of depth zones within the well bore, determines the presence of sand ingress in the well hole in a first depth zone of the plurality of depth zones with the use of the first sign, determines the presence of sand migration between the first depth zone and a second depth zone of the plurality of depth zones using the first signal, and identifies a location of sand ingress from the formation as being within the second depth zone.
[0005] [0005] In some embodiments, a method for correcting a well comprises determining the presence of ingress of sand at a first depth in a well hole, identifying, using secondary information data, a depth of a fluid barrier in the well bore, identify, using secondary information data,
[0006] [0006] These and other resources will be more clearly understood from the following detailed description taken in conjunction with the attached drawings and claims.
[0007] [0007] The modalities described in this document comprise a combination of resources and advantages designed to solve several disadvantages associated with certain devices, systems and previous methods. The aforementioned outlined the features and technical advantages of the invention in a very broad way so that the following detailed description of the invention can be better understood. The various features described above, as well as other resources, will be readily apparent to those skilled in the art by reading the following detailed description, and by reference to the attached drawings. It should be noted by those skilled in the art that the specific design and modalities disclosed can be readily used as a basis for modifying or designing other structures to carry out the same purposes as the invention. It should also be noted by those skilled in the art that such equivalent constructions do not depart from the spirit and scope of the invention as presented in the appended claims. BRIEF DESCRIPTION OF THE DRAWINGS
[0008] [0008] For a detailed description of the preferred embodiments of the invention, reference will now be made to the accompanying drawings in which:
[0009] [0009] FIG. 1 is a schematic cross-sectional illustration of a downhole well hole environment according to one embodiment.
[0010] [0010] FIG. 2 is a schematic view of an embodiment of a well bore pipe with sand inlet according to an embodiment.
[0011] [0011] FIGS. 3A and 3B are seen in schematic cross section of a well's modalities with a well hole tube that has an optical fiber associated with it.
[0012] [0012] FIG. 4 is a graph of acoustic intensity filtered by exemplary frequency versus time over three frequency bands.
[0013] [0013] FIG. 5 is another graph of acoustic intensity filtered by exemplary frequency versus time over five frequency bands.
[0014] [0014] FIG. 6 illustrates an embodiment of a schematic processing flow for an acoustic signal.
[0015] [0015] FIG. 7 illustrates an exemplary graph of acoustic power versus frequency for a plurality of rock bottom events.
[0016] [0016] FIG. 8 is a generic representation of a sand record, according to a modality.
[0017] [0017]
[0018] [0018] FIG. 9 schematically illustrates a flowchart of a method for detecting the ingress of sand into a well hole, according to one modality.
[0019] [0019] FIG. 10 schematically illustrates a computer that can be used to perform various stages, according to one modality.
[0020] [0020] FIGS. 11A-11C schematically illustrate a well bore environment, according to certain methods disclosed in this document. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0021] [0021] Otherwise, unless otherwise specified, any use of the terms "connect", "engage", "engage", "fix" or any other term describing an interaction between elements in any form is not intended to limit interaction to drive interaction between elements and may also include indirect interaction between the elements described. In the following discussion and in the claims, the terms "including" and "comprising" are used in an open manner and thus must be interpreted to mean "including, but not limited to. . . Reference to ascending or descending will be made for the purposes of description with "ascending", "superior", "upwards", "upstream" or "above" which mean towards the surface of the well hole and with "descending", “Bottom”, “down”, “downstream” or “down” that mean towards the terminal end of the well, regardless of the well hole orientation. Reference to internal or external will be made for purposes of description with “inside”, “internal” or “inward” which mean towards the central longitudinal geometric axis of the well hole and / or well hole tube, and “outside ”,“ External ”or“ outward ”which mean towards the well hole wall. As used herein, the term "longitudinal" or "longitudinally" refers to a geometric axis substantially aligned with the central geometric axis of the well bore pipe, and "radial" or "radially" refers to a direction perpendicular to the axis longitudinal geometric. The various features mentioned above, as well as other features and characteristics described in greater detail below, will be readily apparent to those skilled in the art with the aid of the present disclosure by reading the following detailed description of the modalities, and by reference to the attached drawings.
[0022] [0022] A new real-time signal processing architecture is revealed in this document that allows the identification of several downhole events that includes gas inflow detection, downhole leak detection, barrier integrity monitoring well flow, fluid inlet flow, and the identification of sand ingress zones in real time or near real time. In some modalities, the system allows the quantitative measurement of several fluid flows, such as a relative concentration of ingress of sand into a well. As used in this document, the term "real time" refers to a time that takes into account various latency and communication delays within a system, and can include actions taken in about ten seconds, in about thirty seconds, in about a minute, about five minutes, or about ten minutes of the action that takes place. Various sensors (for example, distributed optical fiber acoustic sensors, etc.) can be used to obtain acoustic sampling at various points along the well bore. The acoustic sample, then, can be processed using signal processing architecture with various resource extraction techniques (for example, spectral resource extraction techniques) to obtain a measure of one or more frequency domain resources that make it possible to selectively extract the acoustic signals of interest from background noise and consequently assist in improving the accuracy of the identification of the movement of fluids and / or solids (for example, sand ingress locations, gas inflow locations, fluid flow locations restricted, etc.) in real time. As used in this document, several frequency domain resources can be obtained from the acoustic signal, and in some contexts the frequency domain resources can also be termed as spectral resources or descriptors. The signal processing techniques described in this document can also help to solve the problem of big data through intelligent data extraction (instead of crude decimation techniques) to considerably reduce real-time data volumes at the collection site and processing (eg reduction by more than 100 times, more than 500 times, or more than 1000 times, or more
[0023] [0023] The acoustic signal can be obtained in a way that allows a signal to be obtained over the entire well hole or a portion of interest. Although acoustic surface pressure detectors can provide an indication that certain events, such as downhole sanding, occur, they do not provide information about the depth in the production zone that contributes to the events, such as sanding. Furthermore, the methodology adopted to process the pressure detector data to identify the events from another “background” acoustic noise yield only qualitative and often inconsistent results. A variety of other technical limitations currently undermine the direct application of technology for real-time well acoustic detection. Acoustic sensors distributed by optical fiber (DAS) capture acoustic signals that result from downhole events, such as gas inflow, fluid flow after restrictions, sand ingress, and the like, as well as other background acoustics. This forces the need for a robust signal processing procedure that distinguishes sand ingress signals from other noise sources to avoid false positives from the results. This, in turn, results in a need for a clearer understanding of the acoustic fingerprint of an event of interest in the well (for example, sand ingress, etc.) so that noise resulting from an event of interest can be segregated. another ambient acoustic background noise. As used in this document, the acoustic fingerprint resulting from a particular event can also be called a spectral signature, as described in greater detail in this document.
[0024] [0024] Furthermore, reducing deferrals that result from one or more events, such as sand ingress and facilitating effective remediation depends on near real-time decision support to inform the operator of the events. There is currently no DAS signal technology / processing that successfully distinguishes and extracts event locations, much less in near real time.
[0025] [0025] In terms of data processing and loads, DAS acquisition units produce large volumes of data (typically around 1 TB / hour) creating complexities in data handling, data transfer, data processing and storage. There is currently no method for intelligently extracting useful information to reduce data volumes in real time for immediate decision support. This imposes complexity in real-time data transmission to the coast and data integration for existing IT platforms due to data bandwidth limitations and the data needs to be stored on hard drives that are sent back to the coast for interpretation and analysis. In addition, this increases the turnaround time for interpretation (typically, a few weeks to months) before any remediation efforts can be carried out, resulting in deferred production.
[0026] [0026] The ability to identify multiple events at the well hole can allow multiple actions to be taken (remediation procedures) in response to the events. For example, a well can be deactivated, production can be increased or decreased and / or palliative measures can be taken at the well bore, as appropriate based on the identified event (or events). An effective response, when needed, benefits, not from a binary yes / no output from an identification of events in the well, but also from a measure of the relative amount of fluids and / or solids (eg sand concentrations, amount of gas inflow, amount of fluid flow plus a restriction, etc.) of each of the identified zones, so that one can act first through the zones that contribute to the largest amounts of fluid and / or solid to improve or optimize production. For example, when a leak is detected in addition to a restriction, a relative flow rate of the leak may allow identification of the timing at work to plug the leak (for example, small leaks may not need to be corrected, larger leaks may need to be corrected. high priority, etc.).
[0027] [0027] As described in this document, spectral descriptors can be used with real-time acoustic DAS data processing to provide various downhole surveillance applications. More specifically, data processing techniques can be applied for various downhole fluid profiling, such as inlet flow / fluid outflow detection, fluid phase segregation, well integrity monitoring, in well leakage (eg leakage detection of casing and downhole piping, leakage phase identification, etc.), annular fluid flow diagnosis; overload monitoring, detection of fluid flow behind an enclosure, detection of fluid-induced hydraulic fracture at overload, and the like. Application of the DAS signal processing technique for downhole surveillance provides several benefits that include improving reservoir recovery by monitoring effective drainage of reserves through downhole fluid monitoring (well integrity and flow monitoring) production input), improve well-operating envelopes by identifying extraction levels (eg gas, sand, water, etc.), facilitate the desired remedial action for effective sand management and well integrity, reduce operational risk through the clear identification of abnormalities and / or failures in well barrier elements.
[0028] [0028] In some modalities, the use of the systems and methods described in this document can provide knowledge of the zones that contribute to the sanding and its relative concentrations, therefore, potentially allowing for improved remediation actions based on the results of processing. The methods and systems disclosed in this document can also provide information on the variability of the amount of sand that is produced by different sand inflow zones as a function of different production rates, different production bottlenecks and downhole pressure conditions. therefore allowing choke control (for example, automated choke control) to control sand production. The modalities of the systems and methods revealed in this document also allow computation of the relative concentrations of sand ingress into the well hole, therefore, offering the potential for the most desired and effective remediation.
[0029] [0029] As disclosed in this document, the modalities of data processing techniques use a sequence of digital signal processing steps in real time to isolate and extract the acoustic signal that results from the ingress of sand from background noise, and allows real-time detection of downstream sand ingress zones with the use of distributed optical fiber acoustic sensor data as the input data feed.
[0030] [0030] Now with reference to Figure 1, an example of a well bore 100 operating environment is shown. As will be described in greater detail below, the modalities of completion sets comprising the distributed acoustic sensor system (DAS) according to the principles described in this document can be positioned in environment 100.
[0031] [0031] As shown in Figure 1, the exemplary environment 100 includes a well bore 114 that passes through an underground formation 102, enclosure 112 that lines at least a portion of the well bore 114 and a tube 120 that extends through the borehole. well 114 and housing 112. A plurality of spaced screen elements or assemblies 118 are provided along the tube 120. Additionally, a plurality of spaced zonal insulation devices 117 and gravel packages 122 are provided between the tube 120 and the wall side of the well bore 114. In some embodiments, the operating environment 100 includes a reconditioning and / or drilling probe positioned on the surface and extending over the well bore 114.
[0032] [0032] In general, well bore 114 can be drilled in the underground formation 102 using any suitable drilling technique. Well bore 114 may extend substantially vertically from the surface of the earth over a vertical portion of the borehole, deviate from the vertical portion in relation to the surface of the earth over a deviated portion of the borehole and / or transition to a horizontal portion of a borehole. In general, the whole or portions of a well bore can be vertical, offset at any suitable angle, horizontal and / or curved. In addition, well bore 114 can be a new borehole, an existing borehole, a straight borehole, an extended reach borehole, an offset borehole,
[0033] [0033] Tube 120 can be lowered into well hole 114 to perform an operation such as drilling, completion, reconditioning, treatment and / or production processes. In the embodiment shown in FIG. 1, tube 120 is a completion assembly column that includes a distributed acoustic sensor (DAS) coupled thereto. However, in general, tube modalities 120 can function as a different type of structure in a well bore including, without limitation, such as a drill string, casing, liner, joint tubing and / or coiled tubing. In addition, tube 120 can operate in any portion of well bore 114 (e.g., vertical, offset, horizontal and / or curved section of well bore 114). The modalities of DAS systems described in this document can be coupled to the outside of the tube 120 or, in some embodiments, arranged inside an interior of the tube 120, as shown in FIGS. 3A and 3B. When the DAS is coupled to the outside of the tube 120, the DAS can be positioned inside a control line, control channel or recess in the tube 120. In some embodiments, a sand control system may include an external blanket to contain the tube 120 and protect the system during installation. A control line or channel can be formed on the blanket and the DAS system can be placed on the control line or channel.
[0034] [0034] Tube 120 extends from the surface to the production zones and generally provides a conduit for fluids to travel from formation 102 to the surface. A completion kit including tube 120 may include a variety of other downhole tools or equipment to facilitate the production of forming fluids from production areas. For example, zonal isolation devices 117 are used to isolate the various zones within well bore 114. In this embodiment, each zonal isolation device 117 can be an obstructor (for example, production obstructor, gravel pack obstructor, frac-pac type obstructor, etc.). The zonal isolation devices 117 can be positioned between the screen assemblies 118, for example, to isolate different zones or gravel packet intervals along the well bore 114 from each other. In general, the space between each pair of adjacent zonal isolation devices 117 defines a production interval.
[0035] [0035] Screen sets 118 provide sand control capability. In particular, sand control screen elements 118, or other filter means associated with wellbore tube 120 can be designed to allow fluids to flow through them, however,
[0036] [0036] The gravel packages 122 are formed in the ring 119 between the screen elements 118 (or tube 120) and the side wall of the well hole 114 in an open hole completion. In general, the gravel packages 122 comprise relatively coarse granular material placed in the annulus to form a rough canvas against the ingress of sand into the well hole while also supporting the well hole wall. The gravel package 122 is optional and may not be present in all conclusions.
[0037] [0037] The fluid flowing into the tube 120 may comprise more than one fluid component. Typical components include natural gas, oil, water, steam and / or carbon dioxide. The relative proportions of these components can vary over time based on conditions within formation 102 and well bore 114. Similarly, the composition of the fluid that flows into the sections of tube 120 over the entire length of the production column can vary significantly from section to section at any given time.
[0038] [0038] Since the fluid is produced in the well bore 114 and in the completion set columns, several solid particles present in the formation can be produced together with a fluid (for example, oil, water, natural gas, etc.) . Such solid particles are referred to herein as "sand", and may include any solids that originate within the underground formation regardless of size or composition. Since the sand enters the well hole 114, it can create acoustic sounds that can be detected with the use of an acoustic sensor, such as a DAS system. Similarly, the flow of the various fluids to well bore 114 and / or through well bore 114 can create acoustic sounds that can be detected using the acoustic sensor such as the DAS system. Each type of event like the different fluid flows and fluid flow locations can produce an acoustic signature with unique frequency domain features.
[0039] [0039] In Figure 1, the DAS comprises an optical fiber 162 based on the acoustic detection system that uses the optical backscatter component of light injected into the optical fiber to detect acoustic disturbances (eg dynamic effort) along the fiber length 162. Light can be generated by a generator or light source 166 like a laser, which can guarantee pulses of light. Optical fiber 162 acts as the sensor element without additional transducers in the optical path, and measurements can be taken along the length of the entire optical fiber 162. The measurements can then be detected by an optical receiver such as sensor 164 and selectively filtered to obtain measurements from a given point or depth range, thereby providing a distributed measurement that has selective data for a plurality of zones along the optical fiber 162 at any given time. In this way, optical fiber 162 functions effectively as a distributed array of microphones dispersed along the entire length of optical fiber 162, which typically covers at least the production area 150 of well bore 114, to detect background acoustics. well.
[0040] [0040] Light reflected back up from optical fiber 162, as a result of backscattering, can travel back to the source, where the signal can be collected by a sensor 164 and processed (for example, using of a 168 processor). In general, the time it takes the light to return to the collection point is proportional to the distance shifted along the optical fiber 162. The resulting backscattered light that appears along the length of the optical fiber 162 can be used to characterize the environment around around optical fiber 162. The use of a controlled light source 166 (for example, which has a controlled spectral width and frequency) can allow backscattering to be collected and any disturbances along the length of optical fiber 162 to be analyzed. In general, any dynamic or acoustic stress disturbances along the length of the optical fiber 162 can result in a change in the properties of the backscattered light, allowing for a distributed measurement of the acoustic magnitude, frequency and, in some cases, the relative phase of the disturbance.
[0041] [0041] An acquisition device 160 can be coupled to an end of the optical fiber 162. As discussed in this document, the light source 166 can generate the light (e.g., one or more light pulses), and the sensor 164 it can collect and analyze the backscattered light that bounces back up from the optical fiber 162. In some contexts, the acquisition device 160 including the light source 166 and the sensor 164 can be called an interrogator. In addition to the light source 166 and the sensor 164, the acquisition device 160 generally comprises a processor 168 in signal communication with the sensor 164 to perform several stages of analysis described in more detail in the present document. Although shown as being inside the acquisition device 160, the processor can also be located outside the acquisition device 160 including being located remotely in relation to the acquisition device 160. Sensor 164 can be used to obtain data at various rates and can obtain data at a rate sufficient to detect the acoustic signals of interest with sufficient bandwidth. In one embodiment, depth resolution ranges of between about 1 meter and about 10 meters can be obtained. Although the system 100 described in this document can be used with a DAS system to acquire an acoustic signal for a location or depth range in well bore 114, in general, any suitable acoustic signal acquisition system can be used with the processing steps revealed in this document. For example, several microphones or other sensors can be used to provide an acoustic signal at a given location based on the acoustic signal processing described in this document. The benefit of using the DAS system is that an acoustic signal can be obtained through a plurality of locations and / or through a continuous length of well bore 114 instead of distinct locations.
[0042] [0042] Specific spectral signatures can be determined for each event considering one or more frequency domain resources. The resulting spectral signatures can then be used in conjunction with the processed beep data to determine whether an event is occurring at a depth range of interest. Spectral signatures can be determined by considering the different types of movement and flow that occur in a well bore and characterizing the frequency domain resources for each type of movement.
[0043] [0043] Sand ingress can be considered first. As illustrated schematically in Figure 2 and shown in the cross-sectional illustrations in FIGS. 3A and 3B, sand 202 can flow from formation 102 to well bore 114 and then to tube 120. Since sand 202 flows to tube 120, it can collide against the inner surface 204 of tube 120 , and with the fiber itself in cases where the fiber is displaced inside the tube, in a random manner.
[0044] [0044] Sand 202 entering well bore 114 can be transported within a transport fluid 206, and transport fluid 206 can also generate high-intensity acoustic background noise when entering well bore 114 due to turbulence associated with the fluid flowing into the tube 120. This background noise generated by the turbulent fluid flow is generally expected to be predominantly in a lower frequency region. For example, the acoustic signals of fluid inflow can be between about 0 Hz and about 500 Hz, or alternatively between about 0 Hz and about 200 Hz. An increased power intensity can be expected at low frequencies resulting from turbulence increased transport fluid flow. Background noise can be detected as signals superimposed on the broadband acoustic signals produced by sand 202 when sand ingress occurs.
[0045] [0045] Numerous acoustic signal sources can also be considered together with the types of acoustic signals that these sources generate. In general, a variety of signal sources can be considered including fluid flow with or without sand through formation 102, fluid flow with or without sand 202 through a gravel pack 122, fluid flow with or without sand inside or through tube 120 and / or sand screen 118, fluid flow with sand 202 inside or through tube 120 and / or sand screen 118, fluid flow without sand 202 in tube 120 and / or sand screen 118 , gas / liquid inflow, hydraulic fracture, fluid leaks beyond restrictions (eg gas leaks, liquid leaks, etc.) mechanical instrumentation and geophysical acoustic noise and potential point reflection noise within the fiber caused by cracks in the fiber optic cable / conduit under investigation.
[0046] [0046] For fluid flow 206, with the potential for sand 202 to be carried with fluid fluid 206, in formation 102, the probability that any resulting acoustic signal would be captured by optical fiber 162 is considered low. In addition, the resulting acoustic signal would likely be dominated by the low frequencies that result from the turbulent fluid flow. Similarly, the fluid flowing within the gravel pack 122 would likely flow at a low flow rate and therefore would limit the generation and intensity of any acoustic signals created by sand 202. In this way, the acoustic response would be expected occur in the lower frequency range.
[0047] [0047] For fluid flow 206 with or without sand 202 through a gravel pack 122, the probability that any resulting acoustic signal would be captured by the acoustic sensor is also considered low. In addition, the resulting acoustic signal would likely be dominated by the low frequencies that result from the turbulent fluid flow.
[0048] [0048] For fluid flow 206 with or without sand 202 inside or through tube 120, the probability of capturing an acoustic signal is considered high due to the proximity of the acoustic signal source to optical fiber 162 coupled to tube 120. This type of flow can occur as the fluid 206 containing sand 202 flows into the tube 120. Such a flow would result in any sand that generally flows parallel to the inner surface 204 of the tube 120, which would limit the generation of high frequency sounds as well as the intensity of any high frequency sounds that are generated. It is expected that the acoustic signals generated from the fluid flow 206 through the tube 120 and / or sand screen 118 can be dominated by low frequency acoustic signals that result from the turbulent fluid flow.
[0049] [0049] In one embodiment, the acoustic signal due to the 206 fluid containing sand 202 within the tube can be expected to increase in acoustic intensity from about 0 Hz to about 50 Hz, with an attenuation in power between about 20 Hz to about 50 Hz. An example of a signal from a fluid 206 containing sand 202 is shown in Figure 4, which illustrates the frequency-filtered acoustic intensity in depth versus time graphs for three frequency bins. As illustrated, three frequency bins represent 5 Hz to 20 Hz, 20 Hz to 50 Hz, and 50 Hz to 100 Hz. The acoustic intensity can be seen in the first bin and the second bin, with an almost undetectable acoustic intensity in the frequency range between 50 Hz and 100 Hz. This demonstrates the acoustic attenuation for the flow of fluid containing sand into a well bore tube.
[0050] [0050] Turning to Figures 2 - 3, for fluid flow 206 without any sand 202 in tube 120 and / or sand screen 118, the proximity of optical fiber 162 can result in a high probability that any signals generated acoustic signals would be detected by the acoustic sensor. As discussed in this document, fluid flow 206 alone without any sand 202 is expected to produce an acoustic signal dominated by low frequency signals due to the acoustic signals being produced by turbulent fluid flow.
[0051] [0051] For fluid flow 206 with sand 202 in tube 120 and / or sand screen 118, the proximity of optical fiber 162 can result in a high probability that any acoustic signals generated can be detected by optical fiber 162. As further discussed in this document, the flow of fluid 206 with sand 202 would likely result in an acoustic signal that has broadband characteristics with excitation frequencies that extend to high frequency bands, for example, up to and beyond about 5 kHz.
[0052] [0052] For gas flow in the well bore, proximity to optical fiber 162 can result in a high probability that any acoustic signals generated will be detected by optical fiber 162. The flow of a gas in the well bore would likely result in a turbulent flow over a wide frequency range. For example, acoustic inflow signals can be between about 0 Hz and about 1000 Hz, or alternatively between about 0 Hz and about 500 Hz. An increased power intensity can occur between about 300 Hz and about 500 Hz of increased turbulence in the gas flow. An example of the acoustic signal resulting from the inflow of gas into the well hole is shown in Figure 5, which illustrates the acoustic intensity filtered by frequency in depth versus time graphs for five frequency bins. As illustrated, the five frequency bins represent 5 Hz to 50 Hz, 50 Hz to 100 Hz, 100 Hz to 500 Hz, 500 Hz to 2,000 Hz and 2,000 Hz to 5,000 Hz. The acoustic intensity can be seen in the first three bins with frequency ranges up to about 500 Hz, with an almost undetectable acoustic intensity in the frequency range above 500 Hz. This demonstrates that at least a portion of the frequency domain resources may not be present above 500 Hz, which can help to define the signature of the gas inflow.
[0053] [0053] For hydraulic fracture, the self-induced fracture of the underground formation due to various formation conditions can create an acoustic signal. The intensity of such a signal can be detected by optical fiber 162 depending on the distance between the fracture and optical fiber 162. The resulting fracture can be expected to produce a wide band response that has the acoustic energy present in a frequency band between about 0 Hz to about 400 Hz. Some amount of spectral energy can be expected up to about
[0054] [0054] For the flow of a fluid behind an enclosure in the well bore, the proximity of the fluid flow to the optical fiber 162 may result in the acoustic signal being detected. The flow behind the housing can generally be characterized by a flow of fluid through one or more constraints based on a generally narrow or small leakage path that is present. The flow through such a restriction can be characterized by an increase in spectral power in a frequency range from about 0 Hz to about 300 Hz with a main energy contribution in the range from about 0 Hz to about 100 Hz, or between about 0 Hz and about 70 Hz.
[0055] [0055] For the acoustic signals generated by mechanical instrumentation and geophysical acoustic noise, sounds can be detected by optical fiber 162 in some instances depending on the distance between the sound generation and the portion of optical fiber 162 being used to detect the sounds. Several mechanical noises are expected to have low frequency sounds. For example, several motors can operate in the 50 Hz to 60 Hz range, and the resulting acoustic signal is expected to have spectral energy in a narrow band. Various geophysical sounds can have even lower frequencies. As a result, it is expected that sounds from mechanical instrumentation and geophysical sources can be removed by filtration based on a low pass frequency filter.
[0056] [0056] For point reflection type noises, these are generally broadband in nature, however, they can occur at spatially confined depths and generally do not cover the expected spatial resolution of the interrogator. These can be removed as part of the pre-processing steps by calculating spatial averages or median filtering of data across the entire fiber depth.
[0057] [0057] Based on the sound characteristics expected from the potential acoustic signal sources, the acoustic signature of each event can be defined in relation to the background noise contributions. For sand ingress, the acoustic signature can be observed as the presence of a distinct broadband response together with the presence of high frequency components in the resulting response. The singularity in the sand signature allows the application of selective signal isolation routines to extract the relevant information that belongs to the sand ingress acoustics, as described in the following description. In addition, the characteristics of the acoustic signal portion that result from the ingress of sand can allow the location and potentially the nature and amount of sand in the fluid to be determined. The acoustic signatures of the other events can also be determined and used with processing to allow the identification of each event, even when the events occur at the same time in the same depth range.
[0058] [0058] Again with reference to Figure 1, processor 168 within the acquisition device 160 can be configured to perform processing of various data to detect the presence of one or more events along the length of well bore 114. The acquisition 160 may comprise memory 170 configured to store an application or program to perform data analysis. Although it is shown to be contained within the acquisition device 160, memory 170 may comprise one or more memories, any of which may be external to acquisition device 160. In one embodiment, processor 168 may execute the program, which may configure processor 168 to filter the acoustic data set spatially, determine one or more frequency domain resources of the beep, compare the resulting frequency domain resource values with the acoustic signatures, and determine whether an event is occurring at the location selected based on analysis and comparison. The analysis can be repeated through several locations along the length of well bore 114 to determine the occurrence of one or more events and / or event locations along the length of well bore 114.
[0059] [0059] When the acoustic sensor comprises a DAS system, the optical fiber 162 can return raw optical data in real time or almost in real time to the acquisition unit 160. The intensity of the raw optical data is proportional to the acoustic intensity of the sound that is measured. In one embodiment, the raw data can be stored in memory 170 for various subsequent uses. Sensor 164 can be configured to convert the raw optical data into an acoustic data set. Depending on the type of DAS system employed, optical data may or may not be coherent in phase and may be pre-processed to improve signal quality (for example, for normalizing opto-electronic noise / removing noise from non-biased single-point reflection through the use of median filtration techniques or even through the use of spatial motion average computations with averaging windows defined for the spatial resolution of the acquisition unit, etc.).
[0060] [0060] In some cases, instead of producing a signal that comprises raw optical data, it is also possible that the DAS system determines the derivative of the raw optical data to produce a signal derivative.
[0061] [0061] As shown schematically in Figure 6, an embodiment of a system for detecting incoming sand flow may comprise a data extraction unit 402, a processing unit 404, and / or an output or display unit 406. A data extraction unit 402 can obtain the optical data and perform the initial pre-processing steps to obtain the initial acoustic information of the signal returned from the well hole.
[0062] [0062] A variety of specific processing steps can be performed to determine the presence of an event. In one embodiment, non-biased noise "acoustic variant" data can be subjected to an optional spatial filtering step after the pre-processing steps, if present. This is an optional step and helps to focus mainly on a range of interest in the well hole. For example, the spatial filtration step can be used to focus on a production interval, where there is a maximum likelihood of a sand ingress when a sand ingress event is being examined. In one embodiment, spatial filtration can narrow the analysis focus to a reservoir section and, furthermore, allow a reduction in data typically of the order of ten times, thus simplifying data analysis operations. The resulting data set produced by converting the raw optical data can be called acoustic sample data.
[0063] [0063] This type of filtration can provide several advantages besides reducing the size of the data set. Regardless of whether the acoustic data set is spatially filtered, the resulting data, for example, the acoustic sample data, used for the next step of the analysis can be indicative of an acoustic sample through a defined depth (for example, the entire length of the acoustic sample). optical fiber, some portion thereof or a point source in well bore 114). In some embodiments, the acoustic data set may comprise a plurality of acoustic samples resulting from the spatial filter to provide data across numerous depth ranges. In some embodiments, the acoustic sample may contain acoustic data across a range of depth sufficient to capture multiple points of interest. In some embodiments, the acoustic sample data contains information over the entire frequency range at the depth represented by the sample. This means that the various filtration steps, including spatial filtration, do not remove the frequency information from the acoustic sample data.
[0064] [0064] Processor 168 can be additionally configured to perform Discrete Fourier transformations (DFT) or a short time Fourier transform (STFT) of the acoustic variant time domain data measured in each depth section along the fiber or a section of it to spectrally verify the compliance of the acoustic sample data for one or more acoustic signatures. The spectral compliance check can be used to determine whether the expected signature of an event is present in the acoustic sample data. Extraction of spectral resource across time and space can be used to determine spectral compliance and determine whether an acoustic signature (eg, a sand ingress fingerprint, gas inflow, hydraulic fracture signature, etc.) is present in the acoustic sample. In this process, several frequency domain resources can be calculated for the acoustic sample data.
[0065] [0065] The use of frequency domain resources to identify one or more events has numerous resources. First, the use of frequency domain resources results in significant data reduction in relation to the DAS raw data stream. Thus, numerous frequency domain resources can be calculated to allow event identification while the remaining data can be discarded or otherwise stored, while the remaining analysis can be performed using frequency domain resources. Even when the raw DAS data is stored, the remaining processing power is significantly reduced by using frequency domain resources instead of the raw acoustic data itself. In addition, the use of frequency domain resources provides a concise quantitative measure of the spectral character or the acoustic signature of specific sounds relevant to the monitoring of downhole fluid and other applications that can be directly used for application-specific signal processing In real time.
[0066] [0066] Although numerous frequency domain resources can be determined for the acoustic sample data, not every frequency domain resource can be used in the characterization of each acoustic signature. Frequency domain resources represent specific properties or characteristics of the acoustic signals. There are a number of factors that can affect the frequency domain resource selection for each event. For example, a selected descriptor should remain relatively unaffected by the influences of interference from the environment such as interference noise from electronic / optical devices, disturbances of competing acoustic sounds in the transmission channel and the like. In general, electronic / instrumentation noise is present in the acoustic signals captured in DAS or any other electronic meter, and is generally an unwanted component that interferes with the signal. Thermal noise is introduced during signal capture and processing by analog devices that form a part of the instrumentation (for example, electronic amplifiers and other analog circuit systems). This is mainly due to the thermal movement of cargo conveyors. In digital systems, additional noise can be introduced through sampling and quantization. Frequency domain resources must avoid any interference from these sources.
[0067] [0067] As an additional consideration in selecting the frequency domain resource (or resources) for an event, the dimensionality of the frequency domain resource must be compact. A compact representation is desired to decrease the computational complexity of subsequent calculations. The frequency domain resource must also have discriminating power. For example, for different types of audio signals, the selected set of descriptors must provide altogether different values. A measure of a resource's discriminating power is the variance of the resulting resource vectors for a set of relevant input signals. Considering different classes of similar signs, a discriminatory descriptor must have low variance within each class and high variance across different classes. The frequency domain resource must also be able to completely cover the range of property values it describes. As an example, the set of frequency domain resources chosen must be able to completely and exclusively identify the signatures of each of the acoustic signals belonging to a downhole surveillance application or selected event as described in this document. Such frequency domain resources may include, but are not limited to, the spectral centroid, spectral dispersion, spectral attenuation, spectral obliquity, mean square root band (RMS) energy (or subband energy ratios) / normalized band energy), a total RMS height or energy, a spectral flow and a spectral autocorrelation function.
[0068] [0068] The spectral centroid denotes the "brightness" of the sound captured by the optical fiber 162 and indicates the center of gravity of the frequency spectrum in the acoustic sample. The spectral centroid can be calculated as the weighted average of the frequencies present in the signal, in which the magnitudes of the present frequencies can be used as their weightings in some modalities. The value of the spectral centroid, Ci, of the 1st frame of the acoustic signal captured at a spatial location in the fiber, can be written as: ∑ () () = ∑ () (Eq. 1) Where Xi (k), is the magnitude of the short-time Fourier transform of the i-th frame, where 'k' denotes the frequency coefficient or bin index, N denotes the total number of bins and f (k) denotes the center frequency of the bin. The computed spectral centroid can be scaled to a value between 0 and
[0069] [0069] The discussion below refers to the calculation of the spectral centroid based on the calculation of the spectral centroid of a sample data set that comprises optical data produced by the DAS system. In this case, when evaluating the possibility of a sample data set comprising a high frequency component, the calculated spectral centroid should be equal to or greater than a spectral centroid boundary. However, if, as discussed above, the sample data set comprises a derivative of the optical data, the calculated spectral centroid must be equal to or less than the spectral centroid limit.
[0070] [0070] The absolute magnitudes of the computed spectral centroides can be scaled to read a value between zero and one. The turbulent noise generated by other sources such as fluid flow and inflow can typically be at the lowest frequencies (for example, under about 100 Hz) and centroid computing can produce lower values, for example, around or below 0.1 after rescheduling. The introduction of sand can trigger broader frequencies of sounds (for example, a broadband response) that can extend in spectral content to higher frequencies (for example, up to and beyond 5,000 Hz). This can produce centroides of higher values (for example, between about 0.2 and about 0.7 or between about 0.3 and about 0.5), and the magnitude of change would remain considerably independent of concentration general sand assuming there is a good signal-to-noise ratio in the measurement assuming a traditional electronic noise floor (for example, white noise with intermittent noise imposed at lower frequencies). This can, however, depend on the size of the sand particles hitting the pipe.
[0071] [0071] The spectral dispersion can also be determined for the acoustic sample. Spectral dispersion is a measure of the shape of the spectrum and helps to measure how the spectrum is distributed around the spectral centroid. To compute the spectral dispersion, Si, it is necessary to take the deviation of the spectrum from the computed centroid according to the following equation (all other terms defined above): ∑ (()) () = ∑ () (Eq. 2)
[0072] [0072] The spectral attenuation is a measure of the bandwidth of the audio signal. The spectral attenuation of the i-th frame is defined as the “y” frequency bin below which the cumulative magnitudes of the short-time Fourier transform reach a certain percentage value (usually between 85% to 95%) of the general sum of magnitudes spectrum. ∑ | () | = ∑ | () | ........... (Eq. 3) Where c = 85 or 95. The result of the spectral attenuation calculation is a binary index and makes it possible to distinguish acoustic events based on dominant energy contributions in the frequency domain (for example, between gas inflow and fluid flow, etc.).
[0073] [0073] Spectral obliquity measures the symmetry of the distribution of spectral magnitude values around its arithmetic mean.
[0074] [0074] RMS band energy provides a measure of the signal energy within the defined frequency bins which can then be used for the signal amplitude population. The selection of bandwidths can be based on the characteristics of the captured acoustic signal. In some embodiments, a subband energy ratio that represents the ratio of the upper frequency in the selected band to the lower frequency in the selected band can vary from about 1.5: 1 to about 3: 1. In some embodiments, the subband energy ratio can range from about 2.5: 1 to about 1.8: 1, or alternatively be about 2: 1. In some embodiment, frequency bands selected for a signal with a 5,000 Hz Nyquist acquisition bandwidth may include: a first bin with a frequency range between 0 Hz and 20 Hz, a second bin with a frequency range between 20 Hz and 40 Hz, a third bin with a frequency range between 40 Hz and 80 Hz, a fourth bin with a frequency range between 80 Hz and 160 Hz, a fifth bin with a frequency range between 160 Hz and 320 Hz, a sixth bin with a frequency range between 320 Hz and 640 Hz, a seventh bin with a frequency range between 640 Hz and
[0075] [0075] The total RMS energy of the acoustic waveform calculated in the time domain can indicate the height of the acoustic signal. In some modalities, the total RMS energy can also be extracted from the temporal domain after filling the signal for noise.
[0076] [0076] Spectral flattening is a measure of the noise / tone of an acoustic spectrum. The same can be computed by the ratio of the geometric mean to the arithmetic mean of the energy spectrum value and can be used with an alternative approach to detect signals submitted to broadband (for example, as those caused by the ingress of sand). For tonal signals, spectral flattening can be close to 0 and for broadband signals, it can be closer to 1.
[0077] [0077] The spectral slope provides a basic approximation of the spectrum format by a linearly regressed line. The spectral slope represents the decrease in spectral amplitudes from low to high frequencies (for example, a spectral slope). The slope, the y-intersection and the max. and media can be used as resources.
[0078] [0078] Spectral kurtosis provides a measure of the flatness of a distribution around the mean value.
[0079] [0079] The spectral flow is a measure of instantaneous changes in the magnitude of a spectrum. It provides a measure of the quadratic difference from frame to frame of the spectral magnitude vector added across all frequencies or a selected portion of the spectrum. Signals with slowly varying (or almost constant) spectral properties (for example, noise) have a low spectral flow, while signals with abrupt spectral changes have a high spectral flow. The spectral flow can allow a direct measure of the rate of change of spectral location and consequently serves as an event detection scheme that could be used to capture the onset of acoustic events that can then be further analyzed using the defined resource above to uniquely identify and classify the acoustic signal.
[0080] [0080] The spectral autocorrelation function provides a method in which the signal is shifted, and for each signal shift (latency) the correlation or similarity of the shifted signal to the original is computed. This makes it possible to compute the fundamental period by selecting the latency, for which the signal is most similar to itself, for example, in which the autocorrelation is maximized. This can be useful in the analysis of explanatory signature / even for anomaly detection for monitoring well integrity through specific depths in which all barrier elements to be monitored are positioned.
[0081] [0081] Any of these frequency domain resources, or any combination of these frequency domain resources, can be used to provide an acoustic signature for a rock bottom event. In one embodiment, a selected set of characteristics can be used to provide the acoustic signature for each event, and / or all frequency domain resources that are calculated can be used as a group to characterize the acoustic signature for an event. The specific values for the frequency domain resources that are calculated may vary depending on the specific attributes of the acoustic signal acquisition system, so that the absolute value of each frequency domain resource can be changed between systems. In some embodiments, frequency domain resources can be calculated for each event based on the system that is used to capture the acoustic signal and / or differences between systems can be taken into account when determining frequency domain resource values. for each signature between the systems used to determine the values and the systems used to capture the acoustic signal being evaluated.
[0082] [0082] Figure 7 illustrates a diversity of different events in a graph of acoustic power versus frequency to demonstrate the differences in signatures. As shown, event signatures for background instrument noise, gas leaks, inflow of gas into the well bore, ingress or inflow of sand, transport of sand into a pipe, self-induced hydraulic fracture, and flow behind a wrapper are illustrated. A plurality of frequency domain resources can be used to characterize each type of event. In one embodiment, at least two, alternatively, at least three, alternatively, at least four, alternatively, at least five, alternatively, at least six, alternatively, at least seven or alternatively, at least eight different frequency domain resources. Although Figure 7 exhibits only acoustic power, the relative frequencies present are illustrated for exemplary purposes to demonstrate the uniqueness of the acoustic signal result from different events, which can be characterized using a plurality of frequency domain resources.
[0083] [0083] In one embodiment, an event that comprises a gas leak from the formation in the well hole can be characterized by an acoustic signature including a spectral centroid in a lower frequency range (for example, in a range of about 0 Hz to about 500 Hz), with a relatively high normalized spectral centroid value. The spectral spread can be relatively small since the expected signal cannot be a broadband signal. In addition, RMS band energy would be expected in frequency representative bins up to about 500 Hz, while frequency representative bins above about 500 Hz would not have RMS band energies (or subband energy ratios ) or a significantly reduced RMS band energy with respect to the bins representative of the frequencies between 0 Hz and about 500 Hz. In addition, the RMS band energy representative of the frequency range from about 300 Hz to about 500 Hz it can demonstrate the higher RMS band energy (or subband energy ratio) in relation to the representative bins of the other frequency bands. Additional frequency domain features can also be determined for a gas leak event and can be used as part of a gas leak subscription.
[0084] [0084] An event that comprises influx of gas from the formation in the well hole can be characterized by an acoustic signature that includes a spectral centroid within a lower frequency range (for example, in a range of about 0 Hz to about 500 Hz). The spectral spread can be relatively small since the expected signal cannot be a broadband signal. In addition, RMS band energy would be expected in frequency representative bins up to about 500 Hz, while frequency representative bins over about 500 Hz would not have RMS band energies or significantly reduced RMS band energy with respect to bins representative of frequencies between 0 Hz and about 500 Hz. In addition, the RMS band energy representative of the frequency range from about 0 Hz to about 50 Hz can demonstrate the highest RMS band energy with the bins representative of the other frequency bands. Additional frequency domain resources can also be determined for a gas inflow event and can be used as part of a gas inflow subscription.
[0085] [0085] An event that comprises sand ingress can be characterized by an acoustic signature that includes a spectral centroid above about 500 Hz. The spectral dispersion can be relatively greater since the expected signal can be a broadband signal. In addition, the RMS band energy in the bins representative of frequencies above 500 Hz would be expected with values above zero, therefore, providing an indication of the presence of broadband frequencies. Additional frequency domain resources can also be determined for a sand ticket event and can be used as part of a sand ticket subscription.
[0086] [0086] An event that comprises a high rate of fluid flow from the formation in the well bore and / or within the completion set can be characterized by an acoustic signature that includes a spectral centroid in a lower frequency range (for example, example, within a range of 0 Hz to about 50 Hz). The spectral spread can be relatively small since the expected signal cannot be a broadband signal. In addition, RMS band energy would be expected in frequency representative bins up to about 50 Hz, while frequency representative bins above about 50 Hz would not have RMS band energies or significantly reduced RMS band energy with respect to bins representative of frequencies between 0 Hz and about 50 Hz. Additional frequency domain resources can also be determined for a high rate fluid flow event and can be used as part of a fluid flow subscription high rate.
[0087] [0087] An event that involves transporting sand in a well and or the movement of a sand slug can be characterized by an acoustic signature that includes a spectral centroid within a low frequency range (for example, in a 0 Hz range at about 20 Hz). The spectral spread can be relatively small since the expected signal cannot be a broadband signal. In addition, RMS band energy would be expected in frequency representative bins up to about 20 Hz, while frequency representative bins above about
[0088] [0088] An event that comprises the flow of a fluid after a restriction that comprises a cap of sand or sand dune in the borehole tube or the production of tubing can be characterized by an acoustic signature that includes a spectral centroid in a low frequency range (for example, within a range of about 0 Hz to about 50 Hz). The spectral spread can be relatively small since the expected signal cannot be a broadband signal. In addition, RMS band energy would be expected in frequency representative bins up to about 50 Hz, while frequency representative bins above about 50 Hz would not have RMS band energies or significantly reduced RMS band energy with respect to bins representative of frequencies between 0 Hz and about 50 Hz. Additional frequency domain features can also be determined for fluid flow after a restriction event and can be used as part of a fluid flow after a restriction type signature.
[0089] [0089] An event comprising fluid flow behind an enclosure (for example, between the enclosure and the formation) can be characterized by an acoustic signature that includes a spectral centroid within the low frequency range (for example, a range of about 0 Hz to about 300 Hz). The spectral spread can be relatively small since the expected signal cannot be a broadband signal. In addition, RMS band energy would be expected in frequency representative bins up to about 300 Hz, while frequency representative bins over about 300 Hz would have little or no RMS band energy or RMS band energy significantly reduced with respect to the bins representative of the frequencies between 0 Hz and about 300 Hz. In addition, the RMS energy in the bins representative of the frequencies between 0 Hz and about 70 Hz would have an increased energy or power level with respect to the energy of RMS in the remaining frequency bins. Additional frequency domain features can also be determined for fluid flow behind an enclosure and can be used as part of a flow subscription behind an enclosure.
[0090] [0090] An event that comprises a self-induced hydraulic fracture that could be caused by fluid movement in the region close to the well hole can be characterized by an acoustic signature that includes a spectral centroid in an intermediate frequency range (for example, a from about 0 Hz to about 1000 Hz). The spectral dispersion can be relatively large since the expected signal can include a broadband signal with frequencies that extend up to about 5000 Hz. In addition, the RMS band energy would be expected in the representative bins of frequencies up to about 1000 Hz. In addition, the spectral flow can be indicative of the fracture event. A large spectral flow can be expected at the beginning of the fracture due to the instantaneous increase in spectral power during the creation of the hydraulic fracture. The spectral flow can similarly indicate the end of the event, if the event occurs for more than a single frame during the acoustic monitoring. Additional frequency domain features can also be determined by a self-induced hydraulic fracture event and can be used as part of a self-induced hydraulic fracture subscription.
[0091] [0091] An event that comprises a fluid leak after a restriction or downhole cap can be characterized by an acoustic signature that includes a spectral centroid in a low frequency range (for example, in a range of 0 Hz to about 500 Hz). The spectral spread can be relatively small since the expected signal cannot be a broadband signal. In addition, RMS band energy would be expected at bins representative of frequencies up to about 500 Hz. The additional frequency domain resources can also be determined by a fluid leak after a restriction event and can be used as part of a fluid leak signature.
[0092] [0092] An event that comprises the propagation of rock fracture can be characterized by an acoustic signature that includes a spectral centroid in a high frequency range (for example, in a range of 1000 Hz to about 5000 Hz). In addition, RMS band energy would be expected at bins representative of frequencies between about 1000 Hz and about 5000 Hz. In addition, the spectral flow may be indicative of the fracture propagation event. A large spectral flow can be expected at the beginning of the fracture propagation due to the close instantaneous growth in spectral power during the fracture propagation. The spectral flow can similarly indicate the end of the event, if the event occurs for more than a single frame during the acoustic monitoring. Additional frequency domain resources can also be determined for a rock fracture event and can be used as part of a rock fracture subscription.
[0093] [0093] Although the exemplary numerical ranges are provided in this document, actual numerical results may vary depending on the data acquisition system and / or the values may be normalized or otherwise processed to provide different results. As a result, subscriptions for each event may have different limits or ranges of values for each of a plurality of frequency domain resources.
[0094] [0094] To obtain the frequency domain resources, the acoustic sample data can be converted into the frequency domain. In one embodiment, the raw optical data can contain or represent acoustic data in the time domain. A domain representation of the data frequency can be obtained using a Fourier Transform. Various algorithms can be used as known in the art. In some modalities, a Short Time Fourier Transform technique or a Discrete Fourier transform can be used. The sample of resulting data,
[0095] [0095] Processor 168 can then be used to analyze the acoustic sample data in the frequency domain to obtain one or more of the frequency domain resources and provide an output with the determined frequency domain resources for further processing. In some modalities, the issuance of frequency domain resources may include resources that are not used to determine the presence of each event.
[0096] [0096] The processor output with frequency domain capabilities for the acoustic sample data can then be used to determine the presence of one or more events at one or more locations in the well hole corresponding to depth intervals at over which acoustic data is acquired or filtered. In some embodiments, determining the presence of one or more events may include comparing the frequency domain resources with the frequency domain resource thresholds or ranges in each event subscription. When the frequency domain resources in the acoustic sample data correspond to one or more of the event signatures, the event can be identified as having occurred during the sample data measurement period, which can be in real time. Several outputs can be generated to display or indicate the presence of one or more events.
[0097] [0097] The compatibility of frequency domain resources with event subscriptions can be obtained in a variety of ways. In some modalities, a direct compatibility of the frequency domain resources with the limits or ranges of event subscriptions can be performed over a plurality of frequency domain resources. In some modalities, machine learning or even deterministic techniques can be incorporated to allow new signals to be standardized automatically based on the descriptors. As an example, k averaging clustering techniques and k nearest neighborhood classification can be used to group events and classify them to their nearest neighborhood to provide the ability for diagnostic / exploratory surveillance for various events, and in some cases examples, to identify new rock bottom events that have not established event subscriptions. The use of learning algorithms can also be useful when multiple events occur simultaneously, so that the acoustic signals accumulate to form the resulting acoustic sample data. In one embodiment, frequency domain resources can be used to determine the presence of sand ingress at one or more locations in the well bore. The determination of the spectral centroid and the spectral dispersion, and the comparison of the limits can allow a determination of the presence of particles in the fluid at the selected depth in the well bore. Since the high frequency components tend to be present at the location where the sand enters the well hole tube with the fluid, the locations that meet the spectral dispersion and spectral centroid criteria indicate those locations where the sand ingress occurs . This can provide information about the entry point rather than simply a location where sand is present in the well bore pipe (for example, present in a flowing fluid), which can occur at any point above the ingress location. sand as the fluid flows to the surface of the well bore 114.
[0098] [0098] As above, the spectral dispersion can be computed using the spectral centroid, and thus, typically the spectral centroid is calculated first, followed by the spectral dispersion. The comparison of the spectral dispersion and the spectral centroid to the corresponding limit can occur in any order. In some modalities, both values can be calculated, separately or together with additional frequency domain resources, and compared to the corresponding values or limit ranges to determine whether sand ingress is present at the depth represented by the acoustic sample data . In other embodiments, only one of the two properties can be determined first. If the value of the spectral dispersion or the spectral centroid, whichever is determined first, is not above the corresponding limit, the energy value for the depth or depth range represented by the acoustic sample data can be set to zero, and another sample can be processed. If the value is greater than the corresponding limit, then the other property can be determined and compared to the corresponding limit. If the second comparison does not result in the property exceeding the limit, the energy value for the depth range represented by the acoustic sample data can be set to zero. This can result in a data point that comprises a value of zero, so that a resulting record can comprise a value of zero at the corresponding depth. Only when both properties meet or exceed the limit corresponding to the other value, such as the energy or intensity value, is recorded in a data record for the well. The calculated values for energy or intensity can be stored in memory 170 for those acoustic sample data sets in depth and time that meet or exceed the corresponding limits, and a value of zero can be stored in memory 170 for those data sets of acoustic sample that do not meet or exceed one or both of the corresponding limits.
[0099] [0099] The other events can also be identified in a similar way to the presence of sand ingress. In one embodiment, a gas leak event can be characterized by a gas leak signature that comprises a limit range for each of a plurality of spectral descriptors (for example, spectral dispersion, spectral attenuation, spectral asymmetry, the mean quadratic value band energy (RMS) (or as normalized subband energy / band energy ratios), a noise or total RMS energy, a spectral flow and a spectral autocorrelation function). The gas leak signature can be indicative of a gas leak from a well bore formation through a leak path. The processor, using the analysis application, can be configured to compare the spectral descriptor values to the limits and / or ranges and determine whether a gas leak from the formation to the ring in the well hole has occurred. The determination of the spectral descriptor values can be carried out in any order, and the determination can be carried out sequentially (for example, by checking whether a first frequency domain resource is within a limit and / or a range, followed by a second frequency domain resource, etc.), or in parallel with the use of frequency domain resources in the event subscription.
[0100] [0100] In one embodiment, the gas inflow into the well bore can be characterized by a gas inflow signature that comprises a limit range for each of a plurality of spectral descriptors (for example, spectral dispersion, spectral attenuation , spectral asymmetry, mean quadratic value (RMS) band energy (or normalized subband energy / band energy ratios), noise or total RMS energy, spectral flow, and / or a spectral autocorrelation function). The gas inflow signature can be indicative of a gas inlet flow from a well-hole formation.
[0101] [0101] In one embodiment, liquid inlet flow into the well bore can be characterized by a liquid inlet flow signature comprising a spectral centroid limit range and an RMS band energy range, and the frequency domain can include a spectral centroid and RMS band energies in a plurality of bins. The liquid inlet flow signature can be indicative of a liquid inlet flow from a well hole formation. The processor, using the analysis application, can be configured to compare the plurality of spectral descriptor values to the limits and / or ranges and to determine whether the flow of liquid input from the formation occurred. The determination of the spectral descriptor values can be carried out in any order, and the determination can be carried out sequentially (for example, by checking whether a first frequency domain resource is within a limit and / or a range, followed by a second frequency domain resource, etc.), or in parallel with the use of frequency domain resources in the event subscription.
[0102] [0102] In one embodiment, sand transport within the borehole can be characterized by a sand transport signature comprising a spectral centroid limit band and a spectral attenuation limit, and frequency domain features may include a spectral centroid and spectral attenuation. The sand transport signature can be indicative of sand flowing within a fluid carrier within the well bore. The processor, using the analysis application, can be configured to compare the plurality of spectral descriptor values to the limits and / or ranges and to determine whether sand transport within the well hole has occurred. The determination of the spectral descriptor values can be carried out in any order, and the determination can be carried out sequentially (for example, by checking whether a first frequency domain resource is within a limit and / or a range, followed by a second frequency domain resource, etc.), or in parallel with the use of frequency domain resources in the event subscription.
[0103] [0103] In one embodiment, the fluid flow after a sand restriction can be characterized by a sand restriction signature that comprises a spectral power limit range, and the frequency domain resources can comprise the spectral power. The sand restriction signature can be indicative of a liquid flow after a sand restriction in a pipe inside the well hole. The processor, using the analysis application, can be configured to compare the plurality of spectral descriptor values to the limits and / or ranges and to determine whether fluid flow after a sand restriction has occurred. The determination of the spectral descriptor values can be carried out in any order, and the determination can be carried out sequentially (for example, by checking whether a first frequency domain resource is within a limit and / or a range, followed by a second frequency domain resource, etc.), or in parallel with the use of frequency domain resources in the event subscription.
[0104] [0104] In one embodiment, fluid flow behind an enclosure (for example, fluid flow through a leak path, etc.) can be characterized by an enclosure fluid flow signature that comprises a power limit range spectral and one or more bands of RMS band energy, and the frequency domain resources can comprise a spectral centroid and RMS band energies in a plurality of bins. The shell fluid flow signature can be indicative of a fluid flow between a shell and a formation. The processor, using the analysis application, can be configured to compare the plurality of spectral descriptor values to the limits and / or ranges and to determine whether fluid flow behind a shell has occurred. The determination of the spectral descriptor values can be carried out in any order, and the determination can be carried out sequentially (for example, by checking whether a first frequency domain resource is within a limit and / or a range, followed by a second frequency domain resource, etc.), or in parallel with the use of frequency domain resources in the event subscription.
[0105] [0105] In one embodiment, the occurrence of a self-induced hydraulic fracture can be characterized by a self-induced hydraulic fracture signature comprising a spectral centroid limit range and an RMS band energy range, and frequency domain resources can understand a spectral centroid and RMS band energies in a plurality of bins. The self-induced hydraulic fracture signature can be indicative of a formation of a self-induced fracture within a formation. The processor, using the analysis application, can be configured to compare the plurality of spectral descriptor values to the limits and / or ranges and determine whether a self-induced hydraulic fracture has occurred. The determination of the spectral descriptor values can be carried out in any order, and the determination can be carried out sequentially (for example, by checking whether a first frequency domain resource is within a limit and / or a range, followed by a second frequency domain resource, etc.), or in parallel with the use of frequency domain resources in the event subscription.
[0106] [0106] In one embodiment, the presence of a fluid leak can be characterized by a fluid leak signature that comprises a spectral centroid limit range and an RMS band energy range, and frequency domain resources can understand a spectral centroid and RMS band energies in a plurality of bins. The fluid leak signature can be indicative of a flow of liquid after a downhole cap into the downhole. The processor, using the analysis application, can be configured to compare the plurality of spectral descriptor values to the limits and / or ranges and determine whether fluid flow after a restriction, such as a downhole cap has occurred. The determination of the spectral descriptor values can be carried out in any order, and the determination can be carried out sequentially (for example, by checking whether a first frequency domain resource is within a limit and / or a range, followed by a second frequency domain resource, etc.), or in parallel with the use of frequency domain resources in the event subscription.
[0107] [0107] In one embodiment, the occurrence of a fracture within the formation can be characterized by a fracture signature comprising a spectral centroid limit range and an RMS band energy range, and frequency domain resources may comprise a spectral centroid and RMS band energies in a plurality of bins. The fracture signature is indicative of a fracture formation within a formation. The processor, using the analysis application, can be configured to compare the plurality of spectral descriptor values to the limits and / or ranges and determine whether a fracture in the formation has occurred. The determination of the spectral descriptor values can be carried out in any order, and the determination can be carried out sequentially (for example, by checking whether a first frequency domain resource is within a limit and / or a range, followed by a second frequency domain resource, etc.), or in parallel with the use of frequency domain resources in the event subscription.
[0108] [0108] In addition to detecting the presence of one or more events at a depth or location in well bore 114, the analysis software that runs on processor 168 can be used to view event locations or transfer the calculated energy values through computer network for viewing at a remote location. In order to view one or more of the events, the energy or intensity of the acoustic signal can be determined in the depth range of interest (for example, reservoir section in which the sand ingress locations must be determined)
[0109] [0109] The intensity of the acoustic signal in the filtered data set can then be calculated, in which the intensity can represent the energy or power in the acoustic data. A variety of power or intensity values can be calculated. In one embodiment, the mean quadratic value (RMS) spectral energy ratios or subband energy over the filtered data set frequency broadband can be calculated in each of the event depth sections identified using a assembly integration time to compute an integrated data trace of the acoustic energies over the whole or a portion of the fiber length as a function of time. This computation of an event record can be performed repeatedly, such as every second, and later integrated / weighted for different time periods - for example, at times of higher well extractions, to display a time event record over several stages of the production process (for example, from baseline closure, from during well elevation, from stable production,
[0110] [0110] The resulting event record (s) computed every second can be stored in memory 170 or transferred (s) over a computer network, to popularize an event database. The data stored / transferred in memory 170 can include any of the frequency domain resources, the filtered energy data set, and / or the RMS spectral energy over time, to one or more of the data set depths and can be stored every second. This data can be used to generate an integrated event record at each event depth sample point along the length of the optical fiber 162 in conjunction with a synchronized time stamp indicating the measurement times. In producing a visualization event record, the RMS spectral energy for depth sections that do not display or are not compatible with one or more event signatures can be set to zero. This allows those points or depth zones that display or are compatible with event subscriptions to be easily identified.
[0111] [0111] As an example, the analysis software that runs on the 168 processor can be used to view sand ingress locations or transfer the calculated energy values through a computer network for viewing at a remote location. In order to visualize the sand ingress, the energy or intensity of the acoustic signal, or at least the high frequency portion of the acoustic signal, can be determined in the depth range of interest (for example, the reservoir section in which the locations sand ingress should be determined)
[0112] [0112] When the spectral descriptors have values above the corresponding limits in the event signature, the acoustic sample data can be filtered to obtain the sand ingress acoustic data. In some embodiments, only the acoustic sample data that meets or exceeds the corresponding limits can be further analyzed, and the remaining acoustic sample data can be set to zero. The acoustic sample data sets that meet or exceed the corresponding limits can be filtered with a high frequency filter. In one embodiment, the acoustic sample data sets that meet or exceed the corresponding limits can be filtered with a high frequency filter to remove frequencies below about 0.5 kHz, below about 1 kHz, below about 1.5 kHz, or below about 2 kHz. The upper frequency range can be less than about 10 kHz, less than about 7 kHz, less than about 6 kHz, or less than about 5 kHz, where the filter bandwidth can have a frequency range between any of the lower values and any of the upper values. In one embodiment, the acoustic sample can be filtered to produce a filtered data set that comprises frequencies between about 0.5 kHz and about 10 kHz, or between about 2 kHz and about 5 kHz of the acoustic sample. The filtered data set allows the broadband acoustic energy at the higher frequencies to be isolated, and therefore allows the sand ingress acoustics to be distinguished from the general low frequency fluid flow noise captured by the acoustic sensor that results from the fluid flow and mechanical sources of acoustic signals.
[0113] [0113] The intensity of the acoustic signal in the filtered data set can then be calculated, in which the intensity can represent the energy or power in the acoustic data. In one embodiment, the mean quadratic value (RMS) spectral energy over the broadband frequency of the filtered data set can be calculated in each of the sanding depth sections identified through a set integration time to compute a integrated data trace of sand ingress energies over all or a portion of the fiber length as a function of time. This computation of a “sand ingress log” can be performed repeatedly, as every second, and later integrated / weighted for different periods of time - for example, at times of upper well extractions, to display an ingress log of time-lapse sand at various stages of the production process (for example, from baseline closure, from during well elevation, from stable production, from high extraction / production rates, etc. .). Time intervals can be long enough to provide adequate data, although longer periods of time can result in larger data sets. In one embodiment, time integration can occur over a period of time from about 0.1 second to about 10 seconds, or between about 0.5 seconds and about a few minutes or even hours.
[0114] [0114] The sand records computed every second can be stored in memory 170 or transferred through a computer network, to populate an event database. The data stored / transferred in memory 170 may include the measured spectral centroid, the measured spectral dispersion, the filtered energy data set, and / or the RMS spectral energy over time, for one or more of the data set depths and can be stored every second. These data can be used to generate a high frequency plowing energy record integrated at each event depth sample point along the length of the optical fiber 162 together with a synchronized time stamp indicating the measurement times.
[0115] [0115] In the production of a visualization sand register, the RMS spectral energy for depth sections that do not exhibit spectral conformity can be set to zero. This allows those points or zones of depth that have spectral centroides and spectral dispersions greater than the limits to be easily observed. Figure 8 represents an example of a modification of a sanding register that shows RMS spectral energy against depth. The figure illustrates locations that have sand ingress locations as peaks in the total RMS spectral energy. In one embodiment, spectral energy data filtered by band can be viewed together or in a schematic representation of well completion or an open hole petrophysical record that indicates sanding zones in the integration time intervals to allow easy identification with equipment and production zones in a borehole. The sand ingress register can also be viewed as a 3D plot with the spectral energy of RMS along the vertical geometric axis (geometric axis x); the sample point depth along the y axis and the time along the z axis. This modality provides a DAS sand record that can allow visualization of zonal sand contributions in almost real time. In some instances, sanding events may not be continuous, and time-based recording may allow viewing of sand ingress in a time-dependent manner.
[0116] [0116] The spectral energy of RMS and its visualization in the sand register can, therefore, be used to identify the relative contribution of the plowing ingress at different points along the well hole. For example, it may be possible to determine which zone contributes the largest proportion of sand ingress, which zone contributes the second largest portion of sand ingress, and so on.
[0117] [0117] In some modalities, a qualitative determination of the amount of sand that enters the well hole can occur in one or more locations. In order to determine the qualitative amount of sand that enters the well hole,
[0118] [0118] The data issued by the system can indicate, in general, one or more locations or depths of sanding, and, optionally, a relative amount of ingress of sand between the locations or depths identified and / or a qualitative indicator of sand that enters the borehole at a location. If sand ingress is observed in the fluid produced (as determined by methods such as surface sand detectors, visual observation, etc.), but the location and / or amount of the sand ingress cannot be identified with sufficient clarity with use of the methods described in this document, several actions can be taken in order to obtain a better visualization of the acoustic data. In one embodiment, the production rate can be temporarily increased. The resulting data analysis can be performed on the data during the extended production period. In general, an increased fluid flow rate in the well bore can be expected to increase the acoustic signal intensity at the sand inlet locations. This can allow a signal to noise ratio to be improved to more clearly identify sand ingress at one or more locations, for example, by providing increased signal strength to allow spectral compliance to be determined. Sand energies can also be more clearly calculated based on the increased signal outputs. Once zones of interest are identified, production levels can be adjusted based on locations and amounts of sand ingress. Any changes in quantities of sand production over time can be monitored using the techniques described in this document and the operating conditions can be adjusted accordingly (for example, dynamically adjusted, automatically adjusted, manually adjusted, etc.).
[0119] [0119] In some embodiments, the change in the production rate can be used to determine a correlation between the production rate and the sand inlet locations and inlet flow rates at one or more points along the well bore. In general, a decrease in the production rate can be expected to reduce sand ingress rates. By determining the correlations between the production rate and the sand ingress rates, the well production rate and / or one or more zones can be adjusted to reduce the sand ingress rate in the identified locations. For example, an adjustable production choke or choke can be changed to adjust specific sand ingress rates in one or more production zones. If none of the production zones are adjustable, several recovery procedures can be used to alter the production of specific zones. For example, several intake sleeves can be blocked, zonal isolation devices can be used to block production from certain areas, and / or some other operations can be performed to reduce the amount of sand ingress (for example, consolidation procedures, etc.).
[0120] [0120] The same analysis procedure can be used with any of the event subscriptions described in this document.
[0121] [0121] Additional data processing techniques can also be used to detect events in the well bore. In some embodiments, processor 168 can run a program, which can configure processor 168 to filter the acoustic data set spatially and spectral to provide acoustic data from extracted frequency band (FBE) along multiple frequency bands. This can be similar to the frequency bands described with respect to the RMS energies. The set of acoustic data can be pre-processed and then the frequency filtered for bands of multiple frequencies at certain intervals such as every second of data acquisition. Multiple frequency bands can include multiple bands. As an example, multiple frequency bands can include a first band from about 5 Hz to about 50 Hz; a second band from about 50 Hz to about 100 Hz; a third of about 100 Hz to about 500 Hz; a fourth band from about 500 Hz to about 2000 Hz; a fifth band from about 2000 Hz to about 5000 Hz, and so on along the length of the fiber or a selected portion of it, although other bands for the frequency bands can also be used.).
[0122] [0122] The resulting FBE data can then be compared to identify zones with event signature corresponding to the FBE data. For example, the acoustic amplitudes in each of the multiple frequency bands can be compared to determine depths with a response relative to a baseline acoustic signal. The baseline acoustic signal can be taken as the acoustic measurements captured when the well is closed (for example, without producing a fluid). In some embodiments, the baseline beep may comprise a mid-time beep through one or more portions of the well bore. The time period for considering the average may be as much as possible to avoid the potential of an event across the entire average. Any comparison of an acoustic signal that comprises an event with the average time must then indicate a signal increased by at least one frequency range corresponding to the event frequency bands of interest.
[0123] [0123] With the use of sand ingress detection as an example, additional data processing techniques can also be used to detect sand ingress locations.
[0124] [0124] The event signature can include any of those described in this document, such as a gas leak from an underground formation in a ring in the well bore, a gas inlet flow from the underground formation in the bore well, ingress of sand into the well hole, a flow of liquid into the well hole, transport of sand into a tube in the well hole, fluid flow after a cap of sand into a tube in the well hole, fluid flow behind an enclosure, a self-induced hydraulic fracture within the underground formation, a fluid leak after a rock bottom seal, or a rock fracture propagation event.
[0125] [0125] In one embodiment, the method can be used to determine the presence of an inlet flow of sand into a well hole using the inlet sand signature. The sample data set can be analyzed to determine that the sample data set comprises acoustic frequencies greater than about 0.5 kHz, and the spectral characteristic may include a spectral centroid of the sample data set and a spectral dispersion of the sample data set. The sand ticket subscription can include a spectral centroid limit and a spectral dispersion limit. The determination that at least one spectral characteristic is compatible with the event signature can be carried out by determining that the spectral centroid is greater than a spectral centroid boundary, determining that the spectral dispersion is greater than the dispersion boundary. spectral, and determining the presence of sand inlet flow in the well hole based on the determination that the at least one spectral characteristic is compatible with the event signature.
[0126] [0126] In one embodiment, the method can be used to determine the presence of a gas leak using the gas leak signature that is indicative of a gas leak from a formation through a leak path. in the well hole. Frequency domain resources may include a plurality of the frequency domain resources described in this document (for example, spectral dispersion, spectral attenuation, spectral obliquity, mean square root band energy (RMS) (or normalized subband / band energy ratios), a total RMS height or energy, a spectral flow and / or a spectral autocorrelation function). The determination of the presence of the gas leak can be carried out by comparing the plurality of frequency domain resources to the limits and / or ranges and determining a gas leak from the formation for the ring in the well bore occurred.
[0127] [0127] In one embodiment, the method can be used to determine the presence of a gas inflow into the well bore with the use of a gas inflow signature that comprises limits and / or ranges for a plurality of frequency domain resources . Frequency domain resources may include a plurality of the frequency domain resources described in this document (for example, spectral dispersion, spectral attenuation, spectral obliquity, mean square root band energy (RMS) (or normalized subband / band energy ratios), a total RMS height or energy, a spectral flow and / or a spectral autocorrelation function). The determination of the presence of the gas leak can be performed by comparing a plurality of frequency domain resource values in an acoustic sample to the limits and / or ranges and by determining whether a gas leak from the formation to the ring in the well bore occurred.
[0128] [0128] In one embodiment, the method can be used to determine the presence of liquid inlet flow into the well bore with the use of a liquid inlet flow signature that comprises limits and / or ranges for a plurality of resources frequency domain. Frequency domain resources may include a plurality of the frequency domain resources described in this document (for example, spectral dispersion, spectral attenuation, spectral obliquity, mean square root band energy (RMS) (or normalized subband / band energy ratios), a total RMS height or energy, a spectral flow and / or a spectral autocorrelation function). The liquid inlet flow signature can be indicative of a liquid inlet flow from a well hole formation. The determination of the presence of the liquid inlet flow can be performed by comparing a plurality of frequency domain resource values in an acoustic sample to the limits and / or ranges and in determining whether the liquid inlet flow has occurred.
[0129] [0129] In one embodiment, the method can be used to determine the presence of sand that is transported inside the well bore in a fluid carrier using the sand transport signature that comprises limits and / or ranges for a plurality of frequency domain resources. Frequency domain resources may include a plurality of the frequency domain resources described in this document (for example, spectral dispersion, spectral attenuation, spectral obliquity, mean square root band energy (RMS) (or normalized subband / band energy ratios), a total RMS height or energy, a spectral flow and / or a spectral autocorrelation function). The sand transport signature can be indicative of sand being transported in a tube. The determination of the presence of sand transport can be carried out by comparing a plurality of frequency domain resource values in an acoustic sample to the limits and / or bands and when determining whether sand transport has occurred.
[0130] [0130] In one embodiment, the method can be used to determine the presence of fluid flowing after a sand restriction, using a sand restriction signature that comprises boundaries and / or ranges for a plurality of domain resources frequency. Frequency domain resources may include a plurality of the frequency domain resources described in this document (for example, spectral dispersion, spectral attenuation, spectral obliquity, mean square root band energy (RMS) (or normalized subband / band energy ratios), a total RMS height or energy, a spectral flow and / or a spectral autocorrelation function). The determination of the presence of the sand restriction can be carried out by comparing a plurality of resource values of frequency domain in an acoustic sample to the limits and / or bands and when determining whether the sand restriction is present.
[0131] [0131] In one embodiment, the method can be used to determine the presence of fluid flowing between an enclosure and the formation with the use of an enclosure fluid flow signature that comprises limits and / or ranges for a plurality of resources frequency domain. Frequency domain resources may include a plurality of the frequency domain resources described in this document (for example, spectral dispersion, spectral attenuation, spectral obliquity, mean square root band energy (RMS) (or normalized subband / band energy ratios), a total RMS height or energy, a spectral flow and / or a spectral autocorrelation function). The liquid inlet flow signature can be indicative of a liquid inlet flow from a well hole formation. The determination of the presence of fluid flow behind an enclosure can be done by comparing a plurality of frequency domain resource values in an acoustic sample to the limits and / or ranges and by determining whether the fluid flow behind the enclosure occurred.
[0132] [0132] In one embodiment, the method can be used to determine the occurrence of a self-induced hydraulic fracture within the formation using a self-induced hydraulic fracture signature that comprises limits and / or ranges for a plurality of frequency domain resources . Frequency domain resources may include a plurality of the frequency domain resources described in this document (for example, spectral dispersion, spectral attenuation, spectral obliquity, mean square root band energy (RMS) (or normalized subband / band energy ratios), a total RMS height or energy, a spectral flow and / or a spectral autocorrelation function). The self-induced hydraulic fracture signature can be indicative of a formation of a self-induced fracture within a formation. The determination of the presence of the self-induced hydraulic fracture can be performed by comparing a plurality of frequency domain resource values in an acoustic sample to the limits and / or ranges and by determining whether the self-induced hydraulic fracture has occurred.
[0133] [0133] In one embodiment, the method can be used to determine the presence of fluid leaking after a restriction using a fluid leak signature that comprises limits and / or ranges for a plurality of frequency domain resources. Frequency domain resources may include a plurality of the frequency domain resources described in this document (for example, spectral dispersion, spectral attenuation, spectral obliquity, mean square root band energy (RMS) (or normalized subband / band energy ratios), a total RMS height or energy, a spectral flow and / or a spectral autocorrelation function). The determination of the presence of the leaking fluid after the restriction can be performed by comparing a plurality of frequency domain resource values in an acoustic sample to the limits and / or ranges and by determining whether the fluid leak after the restriction has occurred .
[0134] [0134] In one embodiment, the method can be used to determine the occurrence of a fracture within the formation with the use of a fracture signature that comprises limits and / or ranges for a plurality of frequency domain resources. Frequency domain resources may include a plurality of the frequency domain resources described in this document (for example, spectral dispersion, spectral attenuation, spectral obliquity, mean square root band energy (RMS) (or normalized subband / band energy ratios), a total RMS height or energy, a spectral flow and / or a spectral autocorrelation function). The determination of the presence of the fracture can be performed by comparing a plurality of resource values of frequency domain in an acoustic sample to the limits and / or bands and when determining whether the fracture occurred.
[0135] [0135] In addition to other methods described in this document, a method for determining the presence of ingress of sand into a well hole can begin with obtaining an acoustic signal from within a well hole. The well bore may comprise a fluid that serves as a fluid carrier for the sand. In some embodiments, the fluid can be produced from the well during the time that the acoustic signal is obtained so that the fluid that carries the sand is flowed into the well hole or well hole tube that serves as the pipe. of production, and / or the fluid can be fluid from the formation in the well bore.
[0136] [0136] The acoustic signal can include data for the entire well hole or just a portion of the well hole. An acoustic sample data set can be obtained from the acoustic signal. In one embodiment, the sample data set can represent a portion of the acoustic signal by a defined depth range or point. In some embodiments, the acoustic signal can be obtained in the time domain. For example, the acoustic signal may be in the form of an acoustic amplitude with respect to a collection time. The sample data set can also be in the time domain and be converted to the frequency domain using a suitable transform, such as a Fourier transform. In some embodiments, the sample data set can be obtained in the frequency domain, so that the acoustic signal can be converted before obtaining the sample data set. Although the sample data set can be obtained using any of the methods described in this document, the sample data set can also be obtained by receiving it from another device. For example, a separate extraction or processing step can be used to prepare one or more sample data sets and transmit them for separate processing using any of the processing methods or systems disclosed in this document.
[0137] [0137] The spectral compliance of the sample data set can then be achieved using several compliance checks. In one embodiment, a spectral centroid of the sample data set can be determined and compared to a spectral centroid boundary. Similarly, a spectral dispersion of the sample data set can be determined and compared to a spectral dispersion limit. If the spectral centroid or the spectral dispersion does not exceed the corresponding limit, the ingress of sand may not have occurred at the depth represented by the sample data set. In some modalities, the spectral dispersion and the spectral centroid can be determined and compared to the applicable limit in series, and the failure of one to comply with the corresponding limit can interrupt the process, so that another spectral property cannot be determined. When both the spectral dispersion and the spectral centroid meet or exceed the applicable limit, the presence of sand in the fluid (for example, in the fluid entering the well bore) can be determined to be occurring.
[0138] [0138] The general method and the corresponding steps are schematically illustrated as a flow chart shown in
[0139] [0139] Raw data can then optionally be pre-processed at step 605. As shown in Figure 9, pre-processing can be performed using a variety of optional steps. For example, a spatial sample point filter can be applied at step 606. This filter uses a filter to obtain a portion of the acoustic signal corresponding to a desired depth in the well hole. Since the time in which the light pulse sent to the optical fiber returns as backscattered light can correspond to the distance traveled, and therefore the depth in the well hole, the acoustic data can be processed to obtain a sample indicating the desired depth or depth range. This can allow a specific location within the well bore to be isolated for further analysis. The pre-processing step can also include the removal of spurious rear reflex noise at specific depths using median spatial filtration or spatial weighting techniques.
[0140] [0140] In step 607, filtered data can be transformed from the time domain into the frequency domain using a transform, such as a Fourier transform (for example, a Short time Fourier Transform or through Distinct Fourier). By transforming the data after applying the spatial filter, the amount of data processed in the transform can be reduced.
[0141] [0141] In step 608, a noise normalization routine can be performed on the data to improve the signal quality. This step may vary depending on the type of acquisition device used, as well as the configuration of the light source, sensor and other processing routines. Although it is shown in a specific order in Figure 9, the order of the steps in the pre-processing routines can be varied, and any order of steps 606, 607, 608 can be used. The resulting sample data set may have a reduced data size compared to the raw data set. In one embodiment, a ratio of the sample data file size after preprocessing to the raw data file size before preprocessing can be between about 0.05 and about 0.5, or around 0 , 1, or less if the data are spatially / temporally weighted.
[0142] [0142] After the beep is pre-processed, the sample data set can be used in a spectral compliance verification process or routine in step
[0143] [0143] Before returning to step 622, it can be seen that if the comparison in step 614 between the determined spectral centroid and the spectral centroid limit or the comparison in step 618 between the determined spectral spread and the spectral dispersion limit result in a property that is below the corresponding threshold, the process can set an energy value for the sample data set to zero in step 626 before allowing the process to proceed to the data integration routine in step 628. The checks of spectral compliance can occur in any order, and serial comparisons can allow those sample data sets that failed in the first comparison of the spectral centroid or spectral dispersion to proceed to a post-processing routine without having to pass through the remaining elements spectral or routine compliance process.
[0144] [0144] Turning to the 610 spectral or routine compliance process, the sample data set can optionally be further processed to allow the determination of a relative amount of sand entering the well hole at the depth or depth range represented by the sample data set. In step 622, the sample data set can be filtered to isolate the high frequency broadband components from the acoustic data. The sample data set can be filtered within a predefined frequency range to produce a second data set. In one embodiment, the sample data set can be filtered over a broadband, as described in this document. For example, the sample data set can be filtered over a wide frequency band between about 0.5 kHz to about 10 kHz or between about 2 kHz and about 5 kHz. The frequency filter applied in step 622 can isolate the acoustic signature from the sand inlet while removing the lower frequency portions attributable to the fluid flow and other potential acoustic sources. The resulting second data set can then be processed at step 624 to compute the spectral energy of the second data set. In one embodiment, the spectral energy can be calculated as the spectral energy of the mean square value of the second data set. Spectral energy can represent the power or energy of the acoustic signal over the period of time at the depth represented by the second data set. The value of the determined spectral energy can then be stored in a memory as being associated with the depth at the time of the acoustic signal collection.
[0145] [0145] In some modalities, processing in the 610 spectral or routine compliance process may include determining the magnitude and quality factor of peak sand ingress in the second data set. Quality factors can then be used to determine or approximate an amount or rate of sand ingress at the location of the peaks. This information can be passed to and stored as part of the event data record.
[0146] [0146] The resulting determination can then be passed to data integration processing in step 628. In general, the processing steps determine the presence of sand ingress at a depth represented by the sample data set. In order to obtain an analysis along the length of the well hole, the processing steps between the data pre-processing steps and the spectral compliance check can be repeated for a plurality of sample data sets representing various depths along the borehole.
[0147] [0147] In the data integration process, data from each analysis can be received and used to update an event database in step 630. The data can also be sent to another database and / or the database event can be located remotely from the processing location. The data can then be further analyzed for data integration and visualization in near real time or at any later time. Data can include spectral centroid, spectral dispersion, spectral energy (assuming both spectral centroid and spectral dispersion meet or exceed the corresponding limits), or a value of zero for spectral energy when the spectral centroid, the spectral dispersion, or both are below the corresponding limit, the depth associated with the sample data set, a time associated with the acquisition of an acoustic signal, or any combination thereof. The data from a plurality of analysis can then be stored in a database or event record in step 632.
[0148] [0148] The processing steps in the compliance and spectral storage steps can be used to reduce the amount of data stored with respect to a sample data set. In one embodiment, the data stored in the event database in the data integration process may be reduced in file size, so that a ratio of the sample size set of file size to the file size of stored data can be reduced. be between about 500: 1 and about 4000: 1. The overall file size reduction, when taking into account the file reduction in 605 preprocessing steps, may result in a ratio of the raw acoustic file data size to the data file size of the data stored in the integration process of data from about 5000: 1 to about 40,000: 1 or between about 10,000: 1 to about 30,000: 1. Thus, the process disclosed in this document advantageously reduces the amount of raw acoustic data obtained from the well bore to produce a useful and manageable representation of the sand ingress locations, as well as optionally the relative amount of sand ingress at the locations of ingress of sand.
[0149] [0149] The data stored in the data integration process can be passed to the 640 data visualization process. In this process, a variety of records can be created to allow viewing and / or representation of the sand and / or ingress locations. or quantities across different times / stages of production. In one embodiment, the data, which can be optionally integrated in the data integration process 628, but has not been integrated, can be passed to the data visualization process 640. In step 642, the spectral energy calculated for a data set of sample can be analyzed to determine if the spectral energy value is greater than zero.
[0150] [0150] The 640 visualization process can also include the generation and display of a sand ticket or "sand ticket". The sand register represents, in general, the acoustic power or total spectral energy caused by the ingress of sand in one geometry axis and a depth represented by the sample data set in another geometry axis. This record can be obtained using the integrated record data from the 628 data integration process and / or the individual data sets can be analyzed iteratively in step 650 to create the integrated sand record. In this modality, locations in which no sand ingress is detected can have a spectral energy set at zero. In step 622, the integrated sand log can be displayed on a display to provide a representation of the locations or depths that have sand ingress. A plurality of sand logs can be created for different acoustic data collection times in order to provide and display multiple sand logs in real time or near real time for varying production configurations.
[0151] [0151] As described above, several actions can be taken based on the identification of sand ingress locations or locations where sand ingress does not occur. In some embodiments, sand ingress identification methods may be performed, and no sand ingress locations may be located or an identified amount of sand ingress may be below that observed in the fluid that is produced from the well bore. . For example, if the sand is identified within the produced fluid, but no sand ingress location has been identified, it can be determined that the acoustic signal does not detect sand acoustics at a level sufficient to permit detection and location identification. In this example, the rate of fluid production from the well bore can be temporarily increased. The resulting data analysis can be performed on the data during the increased production period while the fluid is produced. In general, an increased fluid flow rate in the well bore can be expected to increase the acoustic signal intensity at sand ingress locations. This can allow a signal to noise ratio to be improved in order to more clearly identify the ingress of sand in one or more locations, for example, by improving a higher signal strength to allow spectral compliance to be determined. Sand energies can also be more clearly calculated based on the increased signal outputs. Once zones of interest are identified, production levels can be adjusted based on locations and amounts of sand ingress.
[0152] [0152] In one aspect of the invention, secondary information can be analyzed in conjunction with information on sand ingress locations to inform decisions about well hole remedies. Secondary information may be characteristics of the well, such as characteristics of the formation that bears the hydrocarbon through which the well penetrates, characteristics of completion / recovery, and / or characteristics or information about production parameters.
[0153] [0153] In one embodiment, a method for correcting a well comprises: determining the presence of ingress of sand at a depth in a well hole; predict a fluid flow path from the ingress of sand depth to a second depth in the well bore by identifying in the secondary information data indicative of a sand barrier (which can be a fluid barrier) and identify, with the use of secondary information, a fluid flow path between the ingress of sand depth and the said barrier; and isolating the predicted fluid flow path from the well hole to prevent fluid ingress from the fluid flow path into the well hole, thereby reducing the ingress of sand into the well hole.
[0154] [0154] The sand barrier can include fluid barriers as well as permeable sand control barriers that allow well fluids to flow through the barrier. Examples of fluid barriers are impermeable layers of formation around the well, such as shale layers and flow control elements installed in the well, such as packers, annular insulation devices installed in the ring between a forming wall and an enclosure to seal the ring, casing coverings, lids, production sleeves, etc.
[0155] [0155] Examples of permeable sand control barriers are gravel packages and consolidated gravel packages, etc.
[0156] [0156] Flow path insulation may comprise installing a fluid barrier in the well to redirect fluid flowing along the fluid flow path along a second fluid flow path through the sand barrier and into the borehole well above the sand barrier. Alternatively, isolating the flow path comprises installing a fluid barrier in the well to prevent well fluids from flowing from the first depth to above the sand barrier.
[0157] [0157] The fluid flow path can be between the casing and the well hole wall (for example, the forming face, etc.). Although this ring can be filled with cement, cracks, cavities or other faults can occur, which allow fluid flow between the well hole wall and the outer surface of the fluid barrier.
[0158] [0158] The determination of the presence of sand ingress at a depth in the well hole may comprise: determining a plurality of frequency domain resources from a sample data set, where the sample data set is a sample of an acoustic or dynamic voltage signal that originates within a well bore, and the sample data set is representative of the acoustic dynamic voltage signal across a frequency spectrum; and determining the presence of sand ingress at one or more locations within the well bore based on the determination that the plurality of frequency domain resources is compatible with a sand ingress subscription.
[0159] [0159] Additionally, determining the presence of ingress of sand in this modality can comprise any of the steps to detect the inflow of sand, according to any other modality described in this document.
[0160] [0160] Information related to the characteristics of the formation can be petrophysical data and can include the type and properties of the rock surrounding the well and the depth (in the well) and thickness of the different rock layers. The rock properties can be used alone or in combination with registration data to provide information about production zones with respect to non-production zones.
[0161] [0161] The term “completion” is known in the art and refers to the way in which the well bore is prepared for production. Different types of completion are well known in the art and may involve the absence of any casing / coating in the hydrocarbon production zone (open hole), the presence of casing that is drilled, the use of sand control assemblies, such as screens and gravel bundles (for example, as shown in FIG. 1), or other arrangements known in the industry. Completion characteristics may include information on the type of completion used in the well bore, properties (such as condition / quality) of completion, completion location, and / or completion configurations that include completion facilities.
[0162] [0162] Production parameters can include pressures, temperatures and downhole flow rates for various fluids. In some examples, data can be made available on sand production across all production areas. This information can be used in conjunction with sand ingress detection to provide a qualitative measurement of the amount of sand that is produced, as well as a sand ingress check.
[0163] [0163] This technique not only addresses plowing that has been identified at a particular depth in a well, but also provides an analytical approach to predict future plowing events, so that preventive measurements can be taken to prevent such predicted plowing events as well. . This means that a single, more comprehensive remediation process can be carried out. This is also significantly more effective than performing remediation to address the existing and identified plowing event, followed by one or more remediation processes such as future plowing events.
[0164] [0164] The need for a predicative approach like this was identified by the inventors due to their observations that addressing an identified sanding event by remedying the location of sand ingress into the well bore does not necessarily prevent the ingress of sand into the well. long-term well bore. Where an adhesive is installed at a depth in a well hole where the location of the ingress of sand in the well hole has been identified, it was found that sand of the same location can still pass through the well hole at a different depth at a later date, typically when the well is being operated on a higher extraction (for example, when there is a greater removal in the formation that carries the hydrocarbon). This can occur, for example, in situations in which the fluid from the sanding location encounters a fluid flow path from such a sanding location along an unsatisfactory completion behind the adhesive. The sand is collected behind the adhesive and can then be carried together with the fluid.
[0165] [0165] In one embodiment, the location of the ingress of sand can be compared to the secondary information that indicates the depth, in relation to the ingress of sand, of non-permeable layers (for example, shale), and that indicate the integrity of the completion (for example, completeness of completeness, such as sand control measurements, for example, a gravel pack) above and / or below the sand inlet location.
[0166] [0166] Reference is made to FIGS. 11A-11C. In some embodiments, a method can be provided to determine a sand ingress location and predict the flow path to the production point from the formation. This can allow both the location of the sand ingress and the relative flow path to be identified within the well bore. This information can then be used to perform a recovery to effectively remedy, reduce and / or prevent sand from entering the well hole at the sand inlet location as well as other potential sand inlet locations.
[0167] [0167] A predicted fluid flow path can be from a sand ingress location through a permeable region 802 of formation 102 that surrounds well hole 114 to a non-permeable region 804 of formation 102, as a layer of shale 804a, and / or by completing above and / or below the sand inlet location 806 where completion is indicated in the secondary information to be ineffective. For example, a gravel package can be ineffective if it is incomplete.
[0168] [0168] Secondary information can be displayed on a user display in conjunction with a sand log (obtained as disclosed in this document) that indicates one or more sand ingress locations, to predict a fluid flow path from from the sand inlet locations to a second depth. In this way, an appropriate remediation procedure can be determined.
[0169] [0169] Remediation procedures to isolate the expected fluid flow path from the well bore may include installing a fluid barrier, such as a well bore (adhesive) 808 liner or similar layer that prevents fluid from flowing into the borehole. of well 114, which extends along the sand inlet location 806 and up and / or down to the next layer of non-permeable formation 810 (like the next layer of shale). Another option may be to install an annular cap at completion to isolate completion at the sand inlet location from the above and / or below the sand inlet location.
[0170] [0170] In some respects, the use of DAS sand ingress detection in conjunction with additional data (as well as the ability to correlate additional data with DAS sand ingress data) may allow an ingress location differential sand is determined. When sand ingress 806a occurs as described in the present document, sand first enters the well hole from formation 114 at a first sand ingress location 806. When a completion set is present, as shown in FIG. 11B, the sand may not be able to directly enter the interior of completion set 808, but may instead migrate into the ring between completion set 808 and the well hole wall before entering the interior of completion set 808. In addition to identifying the sand inlet location 812 within the completion set 808 (where it can pass with the fluids produced to the surface), the DAS system can also be used to identify the point 806 at which sand enters well hole 114 from formation 102. This information can then be used to identify the sand flow path 814, as well as possible remediation measurements to reduce the ingress of sand into the well hole / assembly completion number 806.
[0171] [0171] In one aspect, a method to detect ingress of sand into a well hole can begin by obtaining the DAS acoustic data set to determine a first range of depth or depth location (eg location 812) at which the ingress of sand into an 808 completion set (for example, a production trajectory inside the well bore) occurs. This process is described in this document and can include any of the methods described with respect to the use of the DAS system. In one embodiment, the sand ingress procedure may include obtaining a sample data set that is a sample of an acoustic signal that originates within well hole 114 that comprises a fluid. The sample data set is representative of the acoustic signal over a frequency spectrum, which can allow a plurality of frequency domain resources from the sample data set to be determined across a plurality of depth ranges. In some ways, a bin-based approach to sand detection using the sample data set can be used. Using any of the approaches described in this document, the presence of sand ingress can be determined in a first depth range of the plurality of depth ranges within the wellbore based on the determination that the plurality of frequency domain across the first depth range is compatible with a sand ingress subscription.
[0172] [0172] In addition to the sand ingress signature, a sand migration signature can also be used to detect the migration of sand in a ring inside well 114. Thus, the presence of sand migration along a sand flow path 814 can be determined over a second depth range of the plurality of depths within well bore 114 based on the determination that the plurality of frequency domain resources across the second depth range is compatible with a sand migration signature. The resulting data then provides a point or range of relative depth 816 at which the sand is migrating in a ring, as well as the location of sand ingress 806. The migration of sand from depth range 816 can generally end at the sand inlet location 812 or in the depth range, and the resulting data may be indicative of a sand entry point 806 that allows the sand to migrate in a ring at the well hole before moving on to the production path at the sand inlet location 812.
[0173] [0173] This type of sand ingress process can be indicative of a failure of a fluid barrier, such as a pack of gravel, cement or the like in the ring, but it can also be used to indicate where recovery is needed inside the hole well 114. For example, an attempt to isolate the sand inlet location 812 by itself may still allow the sand to bypass the fluid barrier, such as coating 808 or travel along a different path to still result in ingress of water. sand inside the well hole. In order to reduce or mitigate sand ingress, the entire interval between the sand entry point 806 in the well hole and the sand ingress location 812 may need to be sealed or addressed in order to prevent future sand ingress into a different point in the well hole.
[0174] [0174] Various types of remediation procedures can be used to isolate a potential sand ingress location 812. When a fluid barrier seal or liner 808 is placed in well bore 114, the sand ingress can channel around the coating 808 if the coating does not extend between portions of the completion (for example, a pack of gravel, canvas, etc.) that are suitable to protect or prevent the ingress of sand. The identification of the sand entry point from formation 806 can then help to identify the interval over which the coating is required.
[0175] [0175] In some respects, the petrophysical data can be correlated for the borehole with the plurality of depth ranges, and a plurality of non-porous zones 804, 810 on both sides of the first depth range and the second depth range. depth can be identified. Non-porous areas 804, 810 may represent non-production areas that would not pose a risk of sand ingress. Alternatively, petrophysical data may indicate that completion (eg, gravel packet) is good at locations corresponding to 804 and 810 and, therefore, it is known that sand will not be transported through the completion zone. A remediation procedure can then include isolating well bore 114 between the plurality of non-porous zones 804, 810 (or between the areas with good completion). This can prevent the migration of additional sand along a ring to deflect any fluid barriers placed in the well bore. In this way, the ability to detect sand migration using the DAS system in addition to detecting the location of sand ingress can better identify the potential remediation measurements needed, as well as the interval that needs treatment.
[0176] [0176] An example of the resulting configuration is illustrated in FIG. 11C. As shown, an extended fluid barrier,
[0177] [0177] As mentioned, this invention can be applied in situations where a completion is found to be good above an area that has an unsatisfactory completion. For example, a cover can be installed in the well hole at a depth compatible with the start of good completion. In this way, the fluid from the bottom of the cap cannot pass in addition to the well hole, but it can be deflected around the cap and through good completion. In this case, the sand is prevented from moving over the well by good completion.
[0178] [0178] Any of the systems and methods disclosed in this document can be performed on a computer or other device comprising a processor, such as the acquisition device 160 of Figure 1. Figure 10 illustrates a computer system 780 suitable for implementing a or more modalities disclosed in this document as the acquisition device or any portion thereof. The computer system 780 includes a processor 782 (which can be called a central processor unit or CPU) that is communicating with memory devices including secondary storage 784, read-only memory (ROM) 786, random access memory ( RAM) 788, input / output (I / O) devices 790 and network connectivity devices 792. Processor 782 can be implemented as one or more CPU chips.
[0179] [0179] It is understood that by programming and / or loading executable instructions on the 780 computer system, at least one among CPU 782, RAM 788 and ROM 786 is changed, transforming the computer system 780, in part, into a particular machine or device that has the innovative functionality taught by the present disclosure. It is critical for electrical engineering and software engineering techniques that the functionality that can be implemented by loading executable software on a computer can be converted into a hardware implementation by well-known design rules. Decisions between implementing a concept in software versus hardware typically depend on considerations of design stability and numbers of units to be produced instead of any problems involved in translating the software domain to the hardware domain. In general, a project that is still subject to frequent changes may be preferred to be implemented in software, since performing a hardware implementation again is more expensive than performing a software project again. In general, a project that is stable that will be produced in high volume may be preferred to be implemented in hardware, for example, in an application specific integrated circuit (ASIC),
[0180] [0180] Additionally, after the 780 system is turned on or initialized, the CPU 782 can execute a computer program or application. For example, CPU 782 can run software or firmware stored in ROM 786 or stored in RAM 788. In some cases, at startup and / or when the application is started, CPU 782 can copy the application or portions of the application from the secondary storage 784 for RAM 788 or for the memory space within CPU 782 itself, and CPU 782 can then execute instructions from which the application is understood. In some cases, the 782 CPU can copy the application or portions of the application from the memory accessed through network connectivity devices 792 or through I / O devices 790 to RAM 788 or into the memory space within of CPU 782, and CPU 782 can then execute instructions from which the application is understood. During execution, an application can load instructions on CPU 782, for example, load some of the application's instructions into a cache of CPU 782. In some contexts, it can be said that an application that runs configures CPU 782 to do something, for example, to configure CPU 782 to perform the function or functions promoted by the present application. When CPU 782 is configured in this way by the application, CPU 782 becomes a special-purpose computer or a special-purpose machine.
[0181] [0181] Secondary storage 784 is typically comprised of one or more disk drives or tape drives and is used for non-volatile data storage and as an overflow data storage device if RAM 788 is not large enough to hold all functional data. Secondary storage 784 can be used to store programs that load into RAM 788 when such programs are selected to run. ROM 786 is used to store instructions and, perhaps, data that is read during program execution. ROM 786 is a non-volatile memory device that typically has a small memory capacity compared to the larger memory capacity of secondary storage 784. RAM 788 is used to store volatile data and perhaps to store instructions. Access to both ROM 786 and RAM 788 is typically faster than secondary storage 784. Secondary storage 784, RAM 788 and / or ROM 786 can be called, in some contexts, storage media readable by computer and / or computer readable non-transitory means.
[0182] [0182] 790 I / O devices may include printers, video monitors, liquid crystal displays (LCDs), touch sensitive displays, keyboards, alphanumeric keyboards, switches, displays, mouse, trackballs, speech recognizers, audio players card, paper tape readers or other well-known input devices.
[0183] [0183] 792 network connectivity devices can take the form of modems, modem banks, Ethernet cards, universal serial bus (USB) interface cards, serial interfaces, token ring cards, distributed data interface cards fiber (FDDI), wireless local area network (WLAN) cards, radio transceiver cards that promote radio communications using protocols such as code division multiple access (CDMA), global system for mobile communications ( GSM), long term evolution (LTE), worldwide interoperability for microwave access (WiMAX), near field communications (NFC), radio frequency identity (RFID) and / or other interface protocol radio transceiver cards network and other well-known network devices. These 792 network connectivity devices can enable the 782 processor to communicate with the Internet or one or more intranets. With such a network connection, it is contemplated that the 782 processor can receive information from the network, or can send information to the network (for example, to an event database) during the performance of the method steps described above. Such information, which is often represented as a sequence of instructions to be performed using the 782 processor, can be received from and sent to the network, for example, in the form of a computer data signal embedded in a carrier wave.
[0184] [0184] Such information, which may include data or instructions to be executed using the 782 processor, for example, can be received from and sent to the network, for example, in the form of a baseband signal. computer data or signal embedded in a carrier wave. The baseband signal or the signal embedded in the carrier wave, or other types of signals currently used or developed in the future, can be generated according to various methods well known to someone skilled in the art. The baseband signal and / or the signal embedded in the carrier wave can be called, in some contexts, a transient signal.
[0185] [0185] The 782 processor executes instructions, codes, computer programs, scripts that it accesses from the hard disk, floppy disk, optical disk (these various disk-based systems can all be considered secondary storage 784), flash drive, ROM 786, RAM 788, or network connectivity devices 792. Although only one processor 782 is shown, multiple processors may be present. Thus, although instructions can be discussed as executed by a processor, instructions can be executed simultaneously, in series or otherwise executed by one or multiple processors. Instructions, codes, computer programs, scripts and / or data that can be accessed from secondary storage 784, for example, hard drives, floppy disks, optical disks and / or other device, ROM 786, and / or RAM 788 may be called, in some contexts, non-transitory instructions and / or non-transitory information.
[0186] [0186] In one embodiment, the computer system 780 can comprise two or more computers in communication with each other, which collaborate to perform a task.
[0187] [0187] In one embodiment, some or all of the functionality revealed above can be provided as a computer program product.
[0188] [0188] In some contexts, secondary storage 784, ROM 786 and RAM 788 may be referred to as a non-transitory computer-readable medium or computer-readable storage medium. A dynamic RAM modality of RAM 788, in a similar way, can be called a non-transitory computer-readable medium, in the sense that, while a dynamic RAM receives electrical power and is operated according to its design, for example, during a period of time during which the 780 computer system is powered up and operational, dynamic RAM stores information that is written to it. Similarly, the 782 processor may comprise an internal RAM, an internal ROM, a cache memory and / or other internal blocks of non-transitory storage, sections or components that can be called, in some contexts, computer readable non-transitory media or computer-readable storage media.
[0189] [0189] Having described several systems and methods in this document, specific modalities may include, but are not limited to:
[0190] [0190] In a first aspect, a method to detect ingress of sand into a well bore comprises: obtaining a sample data set, where the sample data set is a sample of an acoustic signal that originates within a borehole comprising a fluid, and in which the sample data set is representative of the acoustic signal across a frequency spectrum; determining a plurality of frequency domain resources from the sample data set over a plurality of ranges; determine a presence of ingress of sand in a first depth range of the plurality of depth ranges within the well hole based on the determination that the plurality of frequency domain resources throughout the first depth range is compatible with a signature sand ingress; and determining a presence of sand migration over a second depth range of the plurality of depths within the well hole based on the determination that the plurality of frequency domain resources across the second depth range is compatible with a subscription of sand migration.
[0191] [0191] A second aspect may include the method of the first aspect, in which the first depth range and the second depth range are contiguous.
[0192] [0192] A third aspect may include the method of the first or second aspect, which further comprises: identifying the location of sand ingress from the formation as being within the second depth range.
[0193] [0193] A fourth aspect may include the method of any one of the first to the third aspects, which further comprises: identifying the location of sand ingress in the well hole as being within the first depth range.
[0194] [0194] A fifth aspect may include the method of any of the first to the fourth aspects, which further comprises: isolating the first depth range and the second depth range; and to prevent sand from entering the well hole based on the insulation.
[0195] [0195] A sixth aspect may include the method of the fifth aspect, in isolating the first depth range comprises sealing a ring between a completion set and the well hole wall between the first depth range and the second depth range.
[0196] [0196] A seventh aspect may include the method of any one of the first to the sixth aspects, which further comprises: correlating petrophysical data for the borehole with the plurality of depth ranges; identifying a plurality of non-porous zones on both sides of the first depth range and the second depth range; and isolating the borehole between the plurality of non-porous zones.
[0197] [0197] An eighth aspect may include the method of any one of the first to seventh aspects, in which the well hole comprises a coating, in which a ring is formed between the coating and the well hole wall, and in which the presence of sand migration is within the ring.
[0198] [0198] A ninth aspect may include the method of the eighth aspect, in which the presence of ingress of sand occurs between the ring and the interior of the coating.
[0199] [0199] A tenth aspect may include the ninth aspect method, which further comprises: determining a fluid flow path between the second depth within the ring and into the liner at the first depth.
[0200] [0200] An eleventh aspect can include the method of any one of the first to eleventh aspects, which additionally comprises: increasing a production rate from the well bore; obtaining a second set of sample data at the increased production rate, wherein the second set of sample data is a second sample of an acoustic signal that originates within the well bore; again determine the presence of sand ingress in the first depth range using the second sample data set; and verify a flow path that has the presence of sand migration using the second sample data set.
[0201] [0201] In a twelfth aspect, a system for determining a sand migration path comprises: a receiver unit comprising a processor and a memory, in which the receiver unit is configured to receive a first signal from a sensor arranged in a well hole, in which a processing application is stored in memory, and in which the processing application, when executed in the processor, configures the processor to: receive the first signal from the sensor, in which the signal comprises an indication of an acoustic signal received in a plurality of depth zones within the well bore; determine the presence of ingress of sand in the well hole in a first depth zone of the plurality of depth zones with the use of the first signal; determining the presence of sand migration between the first depth zone and a second depth zone of the plurality of depth zones using the first signal; and identifying a sand ingress location from the formation as being within the second depth zone.
[0202] [0202] A thirteenth aspect may include the twelfth aspect system, which further comprises: the sensor, wherein the sensor comprises a fiber optic cable disposed within the well bore along the plurality of depth zones; and an optical generator coupled to the fiber optic cable, wherein the optical generator is configured to generate a beam of light and pass the beam of light onto the fiber optic cable.
[0203] [0203] A fourteenth aspect may include the twelfth or thirteenth aspect system, which further comprises: an isolation device, in which the isolation device is disposed within the borehole along the first depth zone and the second depth zone.
[0204] [0204] A fifteenth aspect can include the system of either the twelfth or fourteenth aspect, in which the processing application, when run on the processor, further configures the processor to: identify a depth of a fluid barrier in the bore well; determining a fluid flow path between the second depth and the fluid barrier; and identify the fluid flow path.
[0205] [0205] In a sixteenth aspect, a method to correct a well comprises: determining the presence of ingress of sand at a first depth in a well hole; identify, using secondary information data, the depth of a fluid barrier in the well bore; identify, using secondary information data, the fluid flow path between the ingress of sand depth and the fluid barrier; and isolating the fluid flow path; and blocking the ingress of fluid from the fluid flow path in the well bore; and reducing the ingress of sand into the well bore in response to blocking fluid ingress.
[0206] [0206] A seventeenth aspect may include the sixteenth aspect method, in which determining the presence of sand ingress at the first depth in the well bore comprises: determining a plurality of frequency domain resources from a sample data set , where the sample data set is a sample of an acoustic or dynamic voltage signal that originates within a well bore, and where the sample data set is representative of the acoustic signal over a spectrum of frequency; and determining the presence of sand ingress at the first depth within the well hole based on the determination that the plurality of frequency domain resources is compatible with a sand ingress subscription.
[0207] [0207] An eighteenth aspect may include the sixteenth or seventeenth aspect method, in which isolating the flow path comprises sealing a ring between an enclosure and a forming wall.
[0208] [0208] A nineteenth aspect may include the method of any of the sixteenth to eighteenth aspects, in which the secondary information data comprises a well record, a cement record, a gravel pack record or column data complete.
[0209] [0209] A twentieth aspect may include the method of any one of the sixteenth to the nineteenth aspects, in which identifying the fluid flow path comprises: predicting the fluid flow path using the secondary information data.
[0210] [0210] A twenty-first aspect may include the method of any of the sixteenth to the nineteenth aspects, in which identifying the fluid flow path comprises: determining a presence of sand migration along the fluid flow path.
[0211] [0211] Although several modalities according to the principles revealed in this document have been shown and described above, modifications of them can be carried out by the person skilled in the art without departing from the spirit and teachings of revelation. The modalities described in this document are only representative and are not intended to be limiting. Many variations, combinations and modifications are possible and fall within the scope of the disclosure. The alternative modalities that result from the combination, integration and / or omission of resources of the modality (or modalities) are also covered by the scope of the disclosure. For example, resources described as steps in the method may have corresponding elements in the system modalities described above, and vice versa. Consequently, the scope of protection is not limited by the description presented above, but is defined by the following claims, in which such scope includes all the subject matter equivalents of the claims. Each and every claim is incorporated as additional disclosure in the specification and the claims are embodiments of the present invention (or inventions). In addition, any advantages and resources described above may refer to specific modalities, but should not limit the application of such claims issued to processes and structures that achieve any and all of the above advantages or that have all or any of the above resources.
[0212] [0212] Additionally, the section headings used in this document are provided for consistency with the suggestions through 37 C.F.R. 1.77 or otherwise to provide organizational guidance. These headings should not limit or characterize the invention (or inventions) presented in any claims that may arise from the present disclosure. Specifically and by way of example, although the headings may refer to a "Field", claims should not be limited by the language chosen under that heading to describe the so-called field. Furthermore, a description of a technology in “Background” should not be interpreted as an admission that a certain technology consists of the prior art to any invention (or inventions) in the present disclosure. Neither the "Summary" should be considered a limiting characterization of the invention (or inventions) presented in the claims issued. Furthermore, any reference in the present disclosure to the singular “invention” should not be used to argue that there is only a single point of innovation in the present disclosure. Multiple inventions can be presented according to the limitations of the multiple claims issued from the present disclosure, and such claims, consequently, define the invention (or inventions) and their equivalents, which are therefore protected. In any case, the scope of the claims must be considered on its own merits in light of the present disclosure, however, it should not be interpreted by the headings presented in this document.
[0213] [0213] The use of broader terms as comprises, includes and having should be understood in order to provide support for narrower terms as consisting of, consisting essentially of and substantially understood by. The use of the term "optionally", "may", "may", "possibly" and the like in relation to any element of a modality means that the element is not necessary or, alternatively, the element is necessary, in which both alternatives are covered by the scope of the modality (or modalities). In addition, references to examples are provided for illustrative purposes only, and are not intended to be exclusive.
[0214] [0214] Although the preferred modalities have been shown and described, modifications of them can be made by an element versed in the technique without departing from the scope or teachings in this document. The modalities described in this document are only exemplary and are not limiting. Many variations and modifications to the systems, apparatus and processes described in this document are possible and fall within the scope of the disclosure. For example, the relative dimensions of various parts, the materials from which the various parts are produced and other parameters can be varied. Consequently, the scope of protection is not limited to the modalities described in this document, but, it is limited only by the following claims, the scope of which must include all the subject matter equivalents of the claims. Unless expressly stated otherwise, the steps in a method claim may be performed in any order. Mention of identifiers such as (a), (b), (c) or (1), (2), (3) before the steps in a method claim is not intended to and does not specify a particular order for the steps, but instead, it is used to simplify the subsequent reference to such steps.
[0215] [0215] In addition, the techniques, systems, subsystems and methods described and illustrated in the various modalities as distinct or separate can be combined or integrated with other systems, modules, techniques or methods without departing from the scope of the present disclosure. Other items shown or discussed as directly coupled or in communication with each other may be indirectly coupled or in communication through some interface, device or intermediate component, electrically, mechanically or otherwise. Other examples of changes, substitutions and alterations are determinable by an element versed in the technique and can be carried out without departing from the spirit and scope revealed in this document.

权利要求:
Claims (24)
[1]
1. Method to detect an ingress of sand into a well hole, the method being characterized by comprising: obtaining a sample data set, where the sample data set is a sample of an acoustic signal that originates within a borehole comprising a fluid, and in which the sample data set is representative of the acoustic signal across a frequency spectrum; determining a plurality of frequency domain resources from the sample data set over a plurality of ranges; determine a presence of ingress of sand in a first depth range of the plurality of depth ranges within the well hole based on the determination that the plurality of frequency domain resources throughout the first depth range is compatible with a signature sand ingress; and determining a presence of sand migration over a second depth range of the plurality of depths within the well hole based on the determination that the plurality of frequency domain resources across the second depth range is compatible with a subscription of sand migration.
[2]
2. Method, according to claim 1, characterized by the fact that the first depth range and the second depth range are contiguous.
[3]
Method according to claim 1 or 2, characterized in that it further comprises: identifying the location of sand ingress from the formation as being within the second depth range.
[4]
Method according to any one of claims 1-3, characterized in that it further comprises: identifying the location of ingress of sand in the well hole as being within the first depth range.
[5]
Method according to any one of claims 1-4, characterized in that it further comprises: isolating the first depth range and the second depth range; and to prevent sand from entering the well hole based on the insulation.
[6]
6. Method according to claim 5, characterized by the fact that the insulation of the first depth range comprises sealing a ring between a completion set and a well hole wall between the first depth range and the second depth range. depth.
[7]
Method according to any one of claims 1-6, characterized in that it further comprises: correlating petrophysical data for the well bore with a plurality of depth ranges; identifying a plurality of non-porous zones on both sides of the first depth range and the second depth range; and isolating the borehole between the plurality of non-porous zones.
[8]
Method according to any one of claims 1-7, characterized in that the well hole comprises a coating, in which a ring is formed between the coating and the well hole wall, and in which the presence of sand migration is within the ring.
[9]
9. Method, according to claim 8, characterized by the fact that the presence of the ingress of sand occurs between the ring and the interior of the coating.
[10]
Method according to claim 9, characterized in that it further comprises: determining a fluid flow path between the second depth within the ring and into the coating at the first depth.
[11]
Method according to any one of claims 1-10, characterized in that it further comprises: increasing a production rate from the well bore; obtaining a second set of sample data at the increased production rate, wherein the second set of sample data is a second sample of an acoustic signal that originates within the well bore; again determine the presence of sand ingress in the first depth range using the second sample data set; and verify a flow path that has the presence of sand migration using the second sample data set.
[12]
12. System for determining a sand migration path, the system being characterized by:
a receiving unit comprising a processor and memory, in which the receiving unit is configured to receive a first signal from a sensor disposed in a well bore, in which a processing application is stored in memory, and in which the application processing, when performed on the processor, configures the processor to: receive the first signal from the sensor, wherein the signal comprises an indication of an acoustic signal received in a plurality of depth zones within the well bore; determine the presence of ingress of sand in the well hole in a first depth zone of the plurality of depth zones with the use of the first signal; determining the presence of sand migration between the first depth zone and a second depth zone of the plurality of depth zones using the first signal; and identifying a sand ingress location from the formation as being within the second depth zone.
[13]
13. The system according to claim 12, characterized in that it further comprises: the sensor, wherein the sensor comprises a fiber optic cable disposed within the well bore along the plurality of depth zones; and an optical generator coupled to the fiber optic cable, wherein the optical generator is configured to generate a beam of light and pass the beam of light into the fiber optic cable.
[14]
System according to claim 12 or 13, characterized in that it further comprises: an isolation device, in which the isolation device is disposed within the borehole along the first depth zone and the second depth zone.
[15]
15. System according to any one of claims 12-14, characterized by the fact that the processing application, when executed on the processor, additionally configures the processor to: identify a depth of a fluid barrier in the well bore; determining a fluid flow path between the second depth and the fluid barrier; and identify the fluid flow path.
[16]
16. Method to correct a well, the method being characterized by understanding: determining the presence of ingress of sand in a first depth in a well; identify, using secondary information data, a depth of a sand barrier in the well; identify, using secondary information data, a fluid flow path between the ingress of sand depth and the depth of the sand barrier; isolate the fluid flow path; block fluid flow from the fluid flow path in the well bore above the depth of the sand barrier; and reducing ingress of sand into the well bore in response to the blockage of said fluid flow.
[17]
17. Method according to claim 16, characterized by the fact that determining the presence of sand ingress at the first depth in the well hole comprises: determining a plurality of frequency domain resources from a sample data set, where the sample data set is a sample of an acoustic or dynamic voltage signal that originates within a well bore, and where the sample data set is representative of the acoustic signal over a frequency spectrum ; and determining the presence of sand ingress at the first depth within the well hole based on the determination that the plurality of frequency domain resources is compatible with a sand ingress subscription.
[18]
18. Method according to claim 16 or 17, characterized in that the isolation of the flow path comprises installing a fluid barrier in the well to prevent well fluids from flowing from the first depth to above the sand barrier .
[19]
19. Method according to claim 16 or 17, characterized in that the isolation of the flow path comprises installing a fluid barrier in the well to redirect the fluid flow along the fluid flow path over a second fluid flow path through the sand barrier and into the well bore above the sand barrier.
[20]
20. Method according to any one of claims 16-19, characterized in that the secondary information data comprises a well log, a cement log, a gravel pack record or complete column data.
[21]
21. Method according to any one of claims 16-20, characterized by the fact that the identification of the fluid flow path comprises: predicting the fluid flow path with the use of secondary information data.
[22]
22. Method according to any of claims 16-21, characterized in that the identification of the fluid flow path comprises: determining a presence of sand migration along the fluid flow path.
[23]
23. Method according to any one of claims 16 to 22, characterized in that the sand barrier comprises a fluid barrier, for example, an impermeable layer of a formation around the borehole or a component installed in the well to prevent the flow of fluid through it.
[24]
24. Method according to any one of claims 16 to 22, characterized by the fact that the sand barrier comprises a material in a ring between an enclosure and a forming wall that is permeable by well fluids, but which blocks the sand flow.

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法律状态:
2021-11-23| B350| Update of information on the portal [chapter 15.35 patent gazette]|

优先权:

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申请号 | 申请日 | 专利标题

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US201762549274P| true| 2017-08-23|2017-08-23|

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US62/549,274|2017-08-23|

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PCT/EP2018/072811|WO2019038401A1|2017-08-23|2018-08-23|Detecting downhole sand ingress locations|

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