• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
  • 引用文献
  • 法律状态
  • 优先权
    • 专利摘要:
    The present invention relates to methods and systems for detecting pipe characteristics, such as detecting a defect, including corrosion inspection, in downhole tubular members and estimating thickness. overall downhole tubular elements. A fault detection method may include placing a defect detection tool in a wellbore, the defect detection tool comprising an emitter and a plurality of receivers; processing the measurements from the wellbore defect detection tool to obtain a well log, the well log including a measure of metal loss; storing well logging in a database; importing the well log from the database into an inversion software; loading a well plan into the inversion software; determining the locations of the collars on at least one concentric pipe in the wellbore using a collet locating algorithm in the inverting software using well logging and wellbench; calibrating an anticipated model in the inversion algorithm using a calibration algorithm in the inversion software using well logging, well plan and collar locations; generating an output ratio using the inversion algorithm in the inversion software on the inversion zone, the output ratio comprising the metal thicknesses of at least one concentric pipe of a plurality of concentric pipes, and the locations of the collars; and determining a false loss of metal in the exit ratio using the exit ratio, the well plane and the locations of the collars.
    公开号:FR3058452A1
    申请号:FR1759337
    申请日:2017-10-05
    公开日:2018-05-11
    发明作者:Ilker R. Capoglu；Burkay Donderici
    申请人:Halliburton Energy Services Inc；
    IPC主号:
    专利说明:

    © Publication no .: 3,058,452 (to be used only for reproduction orders)
    ©) National registration number: 17 59337 ® FRENCH REPUBLIC
    NATIONAL INSTITUTE OF INDUSTRIAL PROPERTY
    COURBEVOIE
    ©) Int Cl 8 : E21 B 47/117 (2017.01), E 21 B 12/02
    A1 PATENT APPLICATION
    ©) Date of filing: 05.10.17. © Applicant (s): HALLIBURTON ENERGY SERVICES, ©) Priority: 06.11.16 IB WOUS2016060751. INC. - US. ©) Inventor (s): CAPOGLU ILKER R. and DONDERICI BURKAY. (43 Date of public availability of the request: 11.05.18 Bulletin 18/19. ©) List of documents cited in the report preliminary research: The latter was not established on the date of publication of the request. (© References to other national documents ©) Holder (s): HALLIBURTON ENERGY SERVICES, related: INC .. ©) Extension request (s): ©) Agent (s): GEVERS & ORES Société anonyme.
    £ 4) AUTOMATED REVERSAL WORKFLOW FOR FAULT DETECTION TOOLS.
    FR 3 058 452 - A1
    The present invention relates to methods and systems for detecting characteristics of a pipe, such as detecting a defect, including inspecting corrosion, in downhole tubular members and estimating thickness. overall of downhole tubular elements. A fault detection method may include placing a fault detection tool in a wellbore, the fault detection tool comprising a transmitter and a plurality of receivers; processing the measurements from the fault detection tool in the wellbore to obtain a well log, the well log comprising a metal loss measurement; storing the well log in a database; importing well logging from the database into inversion software; loading a well plan into the inversion software; determining the locations of the collars on at least one concentric pipe in the wellbore using a collar localization algorithm in the inversion software using the well logging and the well plan; calibrating an early model in the inversion algorithm using a calibration algorithm in the inversion software using well logging, well plan and collar locations; generating an output ratio using the inversion algorithm in the inversion software on the inversion zone, the output ratio comprising the thicknesses of the metal of at least one concentric pipe of a plurality of concentric pipes, and locations of collars; and determining a false loss of metal in the output ratio using the output ratio, well plan and locations
    i
    AUTOMATED REVERSE WORKFLOW FOR FAULT DETECTION TOOLS
    BACKGROUND OF THE INVENTION For the exploration and production of oil and gas, a network of wells, facilities and other conduits can be established by connecting together sections of metal pipe. For example, a well installation can be achieved, in part, by introducing multiple sections of metal pipe (i.e., a casing train) into a borehole, and by cementing the train casing in place. In some well installations, multiple casing trains are used (for example, a concentric arrangement of several trains) to allow for different operations related to completion, production, or enhanced oil recovery (EOR) options associated with a well.
    Corrosion of metal pipes is a permanent problem. In order to reduce corrosion, corrosion-resistant alloys, coatings, treatments and corrosion transfer are used, among others. In addition, efforts to improve corrosion monitoring are ongoing. For downhole casing trains, various types of corrosion monitoring tools are available. One type of corrosion detection tool uses electromagnetic fields (EM) to estimate the thickness of a pipe or other corrosion indicators. For example, an EM logging tool can collect EM logging data, where EM logging data can be interpreted to correlate a level of flux loss or EM induction with corrosion. When multiple casing trains are used together, the proper management of corrosion detection operations by EM logging tools and the interpretation of data can be complex.
    BRIEF DESCRIPTION OF THE FIGURES These drawings illustrate certain aspects of certain examples of this disclosure, and should not be used to limit or define the disclosure.
    Figure 1 is a schematic illustration of an operating environment for a fault detection tool.
    Figure 2 illustrates a process diagram of an automated reversing workflow for electromagnetic fault / corrosion inspection.
    Figure 3 illustrates underlying algorithms that can be used for inversion software.
    FIG. 4 illustrates a process diagram of an inversion algorithm.
    DETAILED DESCRIPTION This disclosure may relate, in general, to methods of detecting the characteristics of a pipe, such as detecting a defect, in particular a corrosion inspection, in tubular downhole elements. and estimating the overall thickness of downhole tubular elements (for example, pipes such as tubing and / or a production tube). More specifically, this disclosure may relate to techniques that can help automate the inspection of tubing corrosion by electromagnetic means. Corrosion inspection can be accomplished by collecting electromagnetic data using a tool in a cased well, and then processing the electromagnetic data in a post-processing inversion algorithm. The exit from the inversion algorithm may be the loss of metal in a number of concentric metallic tubular elements. The present disclosure may relate to a workflow for the entire post-processing procedure, in which the steps can be automated and performed with limited human interaction. This can lead to maximum efficiency and speed, which can be crucial in today's market where inspection results may be required in a matter of hours. Electromagnetic inspection of tubing corrosion can be performed using two techniques: a technique using eddy current and the technique using loss of magnetic flux. The workflow described in this disclosure may be primarily applicable to the technique using eddy current, although it may be applicable to the technique using loss of magnetic flux by certain modifications.
    The present disclosure may include one or more of the following: automatic ghost elimination: introduction of the casing collar detection outputs in a ghost elimination algorithm; iterative adjustments: use of the results from a first iteration to adjust the well plan information, the locations of the tubing collars, the phantom locations and the inversion weights; cancellation flexibility: an ability to use alternative manual inputs for unreliable information (for example, well plan, locations of tubing collars); advanced quality control: an ability to view the calibration coefficients, to match the total thickness from the individual pipes to the RFEC, and to adjust the inversion parameters accordingly; customization based on the pipe or zone: an ability to vary the specific parameters of the algorithm for each pipe or zone; processing workflow: a specific order of processing steps in relation to each other: i) the calibration being performed on the basis of weight assignments, ii) the inversion being performed after the calibration, iii) the allocation of weight, the detection of necklaces and the detection of ghosts being applied after an inversion; calculation time control: an ability to switch between fast and slow inversion in different sections and distribute the inversion to multiple computers using different schemes.
    Figure 1 illustrates an operating environment for a fault detection tool 100 100 as disclosed in this document. The detection tool 100 may include a transmitter 102 and receivers 104. The fault detection tool 100 may be operatively coupled to a transport line 106 (for example, a wired line, a smooth cable, a tube spiral, pipe or equivalent) which can provide mechanical suspension, as well as electrical connectivity, for the fault detection tool 100. The conveyor line 106 and the fault detection tool 100 can extend to the inside a casing train 108 to a desired depth within the wellbore 110. The transport line 106, which may include one or more electrical conductors, may exit from the wellhead 112, may pass around of the pulley 114, can come into contact with Pedometer 116, and can be wound on a winch 118, which can be used to raise or lower the tool assembly in the wellbore 110. The recorded signals by the fault detection tool 100 can be stored in a memory and then processed by a display and storage unit 120 after the recovery of the fault detection tool 100 from the wellbore 110. As a variant , the signals recorded by the fault detection tool 100 can be routed to the display and storage unit 120 using a transport line 106. The display and storage unit 120 can process the signals, and the information they contain can be displayed for an operator to observe and store for future processing and serve as a reference. The display and storage unit 120 may also contain an apparatus for supplying control signals and energy to the downhole tool assembly, the downhole tool assembly comprising a downhole detection tool. default 100.
    A conventional casing train 108 may extend from the wellhead 110 at ground level or above to a selected depth within a wellbore 109. The casing train 108 may comprise a plurality of junctions or segments of casing, each segment being connected to the adjacent segments by a threaded collar.
    Figure 1 also illustrates a conventional drill string 122, which can be positioned inside the casing train 108 extending downward over part of the distance from the wellbore 110. The drill string 122 can be production tubing, production tubing, tubing train, or other pipe disposed in tubing train 108. The fault detection tool 100 can be sized to be lowered into the wellbore 110 through the drill string 122, thereby avoiding the difficulties and expenses associated with removing the drill string 122 from the wellbore 110.
    In logging systems, such as, for example, logging systems using the fault detection tool 100, a digital telemetry system can be used, in which an electrical circuit is used to both provide power to the fault detection tool 100 and to transfer data between the display and storage unit 120 and the fault detection tool 100. DC voltage can be supplied to the fault finding tool fault detection 100 by a power supply located above ground level, and the data can be coupled to the DC power supply conductor by a baseband current pulse system. As a variant, the fault detection tool 100 can be powered by batteries located in the downhole tool assembly, and / or the data supplied by the detection tool 100 can be stored in the tool assembly bottom of the well, rather than transmitted to the surface during logging (fault detection).
    The transmission of electromagnetic fields by the transmitter 102 and the recording of signals by the receivers 104 can be controlled by an information mapipulation system. The transmitter 102 and receivers 104 may include coils.
    The systems and methods of the present disclosure can be implemented, at least in part, with an information manipulation system 124. As illustrated, the information manipulation system 124 can be a component of the display and storage unit 120. As illustrated, the information handling system 124 may be a component of the fault detection tool 100. An information handling system 124 may include any instrument or any aggregation of instrumentalities allowing to calculate, estimate, classify, process, transmit, receive, find, produce, switch, store, display, manifest, detect , to record, reproduce, manipulate or use any form of information, intelligence or data for commercial, scientific, control, or other purposes. For example, an information manipulation system 124 may be a personal computer, a network storage device, or any other suitable device, and may vary in size, shape, performance, functionality, and of price. The information handling system 124 may include a processing unit 123 (for example, a microprocessor, a central processing unit, etc.) which can process data by executing software or instructions obtained from non-transient media computer-readable 125 local or remote (for example, optical discs, magnetic discs). The computer-readable media 125 may store software or instructions for the methods described in this document. The computer-readable non-transient media 125 may include any instrumentality or aggregation of instrumentalities that can retain data and / or instructions for a period of time. The computer-readable non-transient media 125 may include, for example, but not limited to, storage media such as a direct access storage device (for example, a hard disk drive or a floppy drive) , a sequential access storage device (for example, a tape drive), a compact disc, CD-ROM, DVD, RAM, ROM, erasable and electrically programmable read-only memory (EEPROM) and / or flash memory ; as well as communication media such as wires, optical fibers, microwaves, radio waves, and other electromagnetic and / or optical carriers; and / or any combination of the above. The information handling system 124 may also include one or more input devices 127 (for example, a keyboard, a mouse, a touchpad, etc.) and one or more output devices 129 (for example, a monitor , printer, etc.). The input device (s) 127 and the output device (s) 129 provide a user interface that allows an operator to interact with the fault detection tool 100 and / or software executed by the processing unit 123 For example, the information manipulation system 124 may allow an operator to select analysis options, view collected log data, view analysis results, and / or perform other tasks. .
    The fault detection tool 100 can be used to excite the transmitters 102. The transmitters 102 can transmit electromagnetic signals in an underground formation. The electromagnetic signals can be received and measured by the receivers 104 and processed by the information manipulation system 124 to determine the parameters of a pipe, such as, for example, the thickness of a pipe and faulty pipes. Nonlimiting examples of suitable transmitters 102 may include a coil, a wire antenna, a toroidal antenna, or an azimuth button electrode. For example, receivers 104 may include take-up coils (for example, tilted take-up coils), magnetometer receivers, wire antenna, toroidal antenna, or azimuth button electrodes.
    A workflow according to the present disclosure is presented in the figure
    2. The workflow can start with frame 200. Frame 202 shows that a cased logging tool (for example, the fault detection tool 100 of Figure 1) can be lowered into a cased well (for example, the casing train 108 of FIG. 1). The cased well logging tool can take steps to obtain well logging and total metal loss ("TML"). Well logging can include induction measurements performed at at least one receiver (for example, receiver 104 in Figure 1) and at least one frequency. The excitation can be provided by a transmitter (for example, the transmitter 102 in Figure 1) placed at a vertical distance from the receiver (for example, the receiver 104 in Figure 1). The measurement of TML can be carried out using a principle using the far-field eddy current. TML can also be obtained using external tools that measure only TML.
    Box 204 shows that the well log can be stored in a database accessible via a network, or any other suitable form of a data storage medium. Well logging can be read by an analyst (on the network or by obtaining data storage media) at a post-processing center (for example, a training evaluation office). Box 206 shows that the analyst can import the well log into the inversion software ("IS"). A schematic description of FIS is presented in the figure
    3. Box 300 provides the IS. Frame 302 provides an inversion algorithm ("AI"). Frame 304 provides a calibration algorithm ("CA"). Box 306 provides a collar location algorithm ("CLA"). Box 308 provides a weight allocation algorithm ("WAA"). Frame 310 provides a ghost detection algorithm ("GDA"). The underlying algorithms called (for example, used) by FIS can be explained in the following steps.
    Referring again to Figure 2, the IS can load the well plan which belongs to the well which has been subjected to logging. Box 208 shows that the IS can load the well plan, make depth adjustments to the well plan based on the well log. The well plan can show the lengths, start and end depths of all pipes and columns lost in the completed well. The SI can then compare the well plan and at least one depth-based curve (for example, a depth-based measurement such as TML) to automatically determine any change in depth that may have occurred during the log. This can be achieved by comparing at least one major transition point of the well plane and the depth-based curve. The transition points of the depth-based curve can be the curves where a significant change occurs in the average amplitude of the signal. After finding the optimal change in depth, the SI can correct any logging curves (for example, depth-based measurements such as receiver voltages, currents, TML, and other data based on the depth) for this change in depth.
    Box 210 shows that FIS can define at least one reversal zone, which can be based on TML. The reversal zones can be contiguous, non-overlapping logging sections where the TML can be above a certain severity threshold. This threshold may depend on the needs of the client. The default threshold can be set between 5% and 20%, for example. In a particular implementation, the default threshold can be set to 15%.
    Box 212 shows that FIS can call (for example, use) a CLA to determine the locations of the collars on at least one concentric pipe. The CLA can take the collar locations on the innermost pipe from a traditional tubing collar locator ("CCL"). The CLA can also determine the locations of collars on any pipe using more advanced techniques, such as the analysis of periodic net signatures of collars on a well log. The final output from the CLA can be a network of binary collar masks (i.e., true or false) which can indicate the presence of a collar on any pipe at any depth. The SI can use this mask to optimize the inversion at the collar locations (for example, by allowing more positive thickness changes in the metal). The SI can determine updated locations of collars on at least one concentric pipe in the wellbore using the collar localization algorithm in the inversion software using the well diagram, well plan and ratio Release. In addition, FIS can generate an updated output report using the updated locations of the collars, can determine a false metal loss updated in the output report using the output report, the well plan and the updated locations of the collars and can generate an updated output report using false metal loss.
    Box 214 shows that FIS can call a WAA which automatically assigns weights to each channel (that is to say, a combination of receiver / frequency) in the cost function associated with the inversion algorithm , as shown in Figure 4. Frame 400 provides well log signals. Box 402 provides computational incompatibility for well log signals and model signals. Box 404 shows whether there is convergence for the computational incompatibility of well log signals and model signals. Frame 406 provides the thicknesses of the individual pipes. Frame 408 provides model signals. Box 410 provides refresh model parameters. Box 412 provides the discovery of a model response. Box 414 provides calibration coefficients. Different reversal zones can get different weight assignments, since the number of concentric casings can be different in each zone. The values of the weights can be determined by prior research on the underlying inversion algorithm. Two aspects of inversion algorithms can include: (1) the sensitivity of each channel to model parameters (i.e., the thicknesses of the metal on each pipe), (2) possible detrimental factors, such as noise, poor model accuracy and poor measurement accuracy. The WAA can assign the same weight to all channels.
    Referring again to Figure 2, the box 216 shows that the SI can call a CA and can calculate calibration coefficients for an anticipated model. The CA is executed separately within each reversal zone. There may be a single calibration performed for the entire area, multiple calibrations within sub-areas of shorter lengths defined by an IS user. The CA can statistically analyze a well log in the reversal zone (or a sub-zone), and can find a nominal zone where the pipes have not been corroded and are otherwise faultless. These areas can be statistically common in a well log, since faults can be an exception rather than a rule in any given well. The relationships between the voltages measured in a nominal area and the simulated voltages from an anticipated model can be calibration coefficients, which can be applied to an anticipated model in later inversion cycles.
    Box 218 shows that the IS can call an AI who can estimate the thicknesses of individual pipes and who can write the estimated thicknesses in an output report. The SI can call an AI on each reversal zone. AI can start with an initial estimate of the model parameters (i.e., the thicknesses of the metal in each pipe), and can update these parameters using an optimization algorithm (for example, Gauss-Newton , LevenbergMarquardt) until a cost function is minimized. The cost function can be an absolute square difference between a well log and a calibrated anticipated model result. The SI displays the estimated thicknesses of the metal for each pipe to a user in the form of an output ratio.
    Box 220 shows that the IS can call a GDA which can determine false metal losses in an output report. The SI can call a GDA that automatically determines ghosts, which are false losses of metal that appear as periodic narrow peaks in the output report. These apparent losses may in fact be a consequence of the collars; or more specifically, the inability of the inversion algorithm to fully take into account their presence due to a finite vertical resolution of the fault detection tool 100. Many fault detection tools have a vertical resolution several feet (ie several meters), while the largest collars can have a vertical resolution of approximately one foot (ie approximately 0.3 meters). The GDA can automatically detect ghosts in an output report in the same way that the CLA detects the collars' signatures in a well log (that is, by exploiting a frequency of ghost signatures). A final output from the GDA can be a network of binary ghost masks that indicates the presence of a ghost (for example, true or false) on any pipe at any depth.
    Box 222 shows that the IS can allow a user to re-run an AI using the ghost information from the previous step (for example, box 220). SI can present a user (for example, through a monitor) with an option to re-perform a reversal (for example, starting at box 218) using the ghost mask array as a constraint 'inversion. The inversion constraint may be to assign zero to the metal losses at the locations where the ghost mask is equal to 1, in order to eliminate narrow peaks in the output ratio. For efficiency reasons, the inversion algorithm can be run again at the locations where the ghost mask is 1, and the original results can be kept the same. The SI can present updated results to a user. Box 224 shows that boxes 210 to 220 can be repeated, if necessary. Box 226 shows the end of the workflow.
    The systems and methods may include any of the various features of the systems and methods disclosed in this document, including one or more of the following statements.
    Item 1: a fault detection method comprising: placing a fault detection tool in a wellbore, the fault detection tool comprising a transmitter and a plurality of receivers; processing measurements from the wellbore fault detection tool to obtain a well log, the well log including a metal loss measurement; storing the well log in a database; import of well logging from the database into inversion software; loading a well plan into the inversion software; determining the locations of the collars on at least one concentric pipe in the wellbore using a collar localization algorithm in the inversion software using the well logging and the well plan; calibrating an advance model in the inversion algorithm using a calibration algorithm in the inversion software using well logging, well plan and clamp locations; generating an output ratio using the inversion algorithm in the inversion software on the inversion zone, the output ratio comprising the thicknesses of the metal of at least one concentric pipe of a plurality of concentric pipes, and locations of collars; and determining a false loss of metal in the output ratio using the output ratio, the well plan and the locations of the collars.
    Statement 2: a fault detection method according to statement 1, further comprising determining the updated locations of the collars on at least one concentric pipe in the wellbore using the collar localization algorithm in the reversal software using well logging, well plan and output ratio.
    Item 3: a fault detection method according to item 2, further comprising generating an updated output report using the updated locations of the collars.
    Statement 4: A method of detecting a defect according to any preceding statement, further comprising determining a false loss of metal updated in the output ratio using the output ratio, the well plan and the locations discounted necklaces.
    Statement 5: a method of detecting a defect according to any preceding statement, further comprising the generation of an updated output report using the false loss of metal.
    Statement 6: A method of detecting a defect according to any preceding statement, further comprising comparing the well plan to depth-based measurements on the well log to determine a change in depth.
    Statement 7: a fault detection method according to statement 6, further comprising the correction of the measurements based on the depth for the change of depth.
    Statement 8: a method of detecting a defect according to any preceding statement, further comprising the definition of at least one inversion zone, in which the inversion zone is a contiguous non-overlapping section of the logging of wells where metal loss is greater than a threshold.
    Statement 9: a fault detection method according to statement 8, in which the threshold ranges from 5% to 20%.
    Statement 10: a fault detection method according to any preceding statement, further comprising assigning weight to a channel in a cost function of an inversion algorithm using a weight allocation algorithm in inversion software, in which the channel includes combinations of receivers and frequencies.
    Statement 11: a fault detection method according to any preceding statement, further comprising analyzing the periodic signatures of collars on the well log to determine the locations of the collars and the output of a network of masks binary necklace.
    Statement 12: a fault detection method according to statement 11, in which the network of binary collar masks comprises an indication of the presence of a collar on a pipe at a depth.
    Statement 13: a fault detection method according to statement 12, further comprising optimizing an inversion at the location of the collars with the network of binary collar masks.
    Statement 14: a fault detection system comprising: a fault detection tool, in which the fault detection tool comprises a transmitter and a plurality of receivers; and an information handling system in communication with the fault detection tool, wherein the information handling system is configured to: process measurements from the fault detection tool in the wellbore to obtaining a well log, wherein the well log includes a measurement of metal loss; storing the well log in a database; import the well log from the database into inversion software; load a well plan into the inversion software; determining collar locations on at least one concentric pipe in the wellbore using a collar location algorithm in the inversion software using well logging and the well plan; calibrate an early model in the inversion algorithm using a calibration algorithm in the inversion software using well logging, well plan and clamp locations; generate an output ratio using the inversion algorithm in the inversion zone inversion software, in which the output ratio comprises the thicknesses of the metal of at least one concentric pipe of a plurality of pipes concentric, and locations of collars; and determining a false loss of metal in the output ratio using the output ratio, the well plan and the locations of the collars.
    Statement 15: A fault detection system according to Statement 14, in which the information handling system is further configured to determine updated locations of the collars on at least one concentric pipe in the wellbore. using the collar localization algorithm in the inversion software using the well log, well plan and exit ratio.
    Statement 16; a fault detection system according to claim 15, in which the information handling system is further configured to generate an updated output report using the updated locations of the collars.
    Statement 17; a fault detection system according to any one of claims 14 to 16, in which the information handling system is further configured to determine a false loss of metal updated in the output ratio using the output ratio, the well plan and the updated locations of the collars.
    Statement 18: A fault detection system according to Statement 17, in which the information handling system is further configured to generate an updated output report using the false loss of metal.
    Item 19: A fault detection system according to any of items 14 to 18, wherein the information handling system is further configured to compare the well plan to measurements based on depth on well logging to determine a change in depth.
    Statement 20; a fault detection system according to claim 19, in which the information handling system is further configured to correct the depth-based measurements for the change in depth.
    The above description provides various examples of the systems and methods of use disclosed in this document which may contain different stages of the method and alternative combinations of components. It should be understood that, although individual examples may be presented in this document, this disclosure covers all combinations of the examples disclosed, including, without limitation, the various combinations of components, the combinations of steps of process, and properties of the system.
    Therefore, the present examples are well suited to achieve the ends and advantages mentioned, as well as those which are inherent to them. The particular examples disclosed above are illustrative only, and may be modified and practiced in different but equivalent ways evident to a specialist in the field and who benefits from these teachings. Although individual examples are discussed, the disclosure covers all combinations of all examples. In addition, there is no limitation to the construction or design details described herein, other than those described in the claims below. In addition, the terms in the claims have their clear and ordinary meaning, unless explicitly stated otherwise and clearly defined by the patent owner.
    权利要求:
    Claims (20)
    [1" id="c-fr-0001]
    The claims relate to the following:
    1. A fault detection method comprising:
    placing (202) a fault detection tool (100) in a wellbore (110), wherein the fault detection tool (100) includes a transmitter (102) and a plurality of receivers (104 );
    processing (202) measurements from the fault detection tool (100) in the wellbore (110) to obtain a well log, wherein the well log includes a measurement of metal loss;
    storing (204) the well log in a database; import (206) of the well log from the database into inversion software (IS);
    loading (208) a well plan in the inversion software (IS); determining (212) the locations of the collars on at least one concentric pipe in the wellbore using a collar localization algorithm (CLA) in the inversion software (IS) using the well logging and the well;
    calibration (216) of a model anticipated in an inversion algorithm (IA) using a calibration algorithm (CA) in the inversion software (IS) using well logging, the well plan and the locations of the collars;
    generating (218) an output ratio using the inversion algorithm (IA) in the inversion software (IS) on an inversion zone, in which the output ratio comprises the thicknesses of the metal d 'at least one concentric pipe of a plurality of concentric pipes, and the locations of the collars; and determining (220) a false loss of metal in the output ratio using the output ratio, the well plan and the locations of the collars.
    [2" id="c-fr-0002]
    2. A method of detecting a defect according to claim 1, further comprising determining the updated locations of the collars on at least one concentric pipe in the wellbore using the collar localization algorithm (CLA) in the software. inversion (IS) using well logging, well plan and exit ratio.
    [3" id="c-fr-0003]
    3. A fault detection method according to claim 2, further comprising generating an updated output report using the updated locations of the collars.
    [4" id="c-fr-0004]
    4. The fault detection method according to claim 2, further comprising determining a false loss of metal updated in the output ratio using the output ratio, the well plan and the updated locations of the collars.
    [5" id="c-fr-0005]
    5. A fault detection method according to claim 4, further comprising generating an updated output report using the false loss of metal.
    [6" id="c-fr-0006]
    The defect detection method of claim 1, further comprising comparing the well plan to depth-based measurements on the well log to determine a change in depth.
    [7" id="c-fr-0007]
    The fault detection method according to claim 6, further comprising correcting the depth-based measurements for the change in depth.
    [8" id="c-fr-0008]
    8. The defect detection method according to claim 1, further comprising defining (210) at least one inversion zone, in which the inversion zone is a contiguous non-overlapping section of the well log where the metal loss is above a threshold.
    [9" id="c-fr-0009]
    9. A fault detection method according to claim 8, in which the threshold ranges from
    5% to 20%.
    [10" id="c-fr-0010]
    The defect detection method according to claim 1, further comprising assigning (214) weight to a channel in a cost function of the inversion algorithm (IA) using a weight assignment algorithm (WAA) in inversion software (IS), in which the channel includes combinations of receivers and frequencies.
    [11" id="c-fr-0011]
    The defect detection method according to claim 1, further comprising analyzing the periodic signatures of collars on the well logging to determine the locations of the collars and the output of a network of binary collar masks.
    [12" id="c-fr-0012]
    12. A fault detection method according to claim 11, in which the network of binary collar masks comprises an indication of the presence of a collar on a pipe at a depth.
    [13" id="c-fr-0013]
    13. A fault detection method according to claim 12, further comprising optimizing an inversion at the locations of collars with the network of binary collar masks.
    [14" id="c-fr-0014]
    14. Fault detection system comprising:
    a fault detection tool (100), wherein the fault detection tool comprises a transmitter (102) and a plurality of receivers (104); and an information handling system (124) in communication with the fault detection tool (100), in which the information handling system (124) is configured to:
    processing measurements from the fault detection tool (100) in the wellbore (110) to obtain a well log, wherein the well log includes a measurement of metal loss;
    storing the well log in a database;
    import the well log from the database into inversion software (IS);
    load a well plan in the inversion software (IS);
    determine locations of collars on at least one concentric pipe in the wellbore (110) using a collar location algorithm (CLA) in the inversion software (IS) using well logging and well plan ;
    calibrate an advance model in an inversion algorithm (IA) using a calibration algorithm (CA) in the inversion software (IS) using well logging, well plan and clamp locations;
    generate an output ratio using the inversion algorithm (IA) in the inversion software (IS) on an inversion zone, in which the output ratio comprises the thicknesses of the metal of at least one concentric pipe a plurality of concentric pipes, and the locations of the collars; and determining a false loss of metal in the output ratio using the output ratio, the well plan and the locations of the collars.
    [15" id="c-fr-0015]
    15. The fault detection system of claim 14, wherein the information manipulation system (124) is further configured to determine updated locations of the collars on at least one concentric pipe in the wellbore (110). using the clamp locator algorithm (CLA) in the inversion software (IS) using the well log, well plan and output ratio.
    [16" id="c-fr-0016]
    The fault detection system of claim 15, wherein the information handling system (124) is further configured to generate an updated output report using the updated locations of the collars.
    [17" id="c-fr-0017]
    The fault detection system of claim 15, wherein the information handling system (124) is further configured to determine a false loss of metal discounted in the output ratio using the output ratio, the plane wells and updated locations of collars.
    [18" id="c-fr-0018]
    18. The fault detection system of claim 17, wherein the information manipulation system (124) is further configured to generate an updated output report using the false loss of metal.
    5
    [19" id="c-fr-0019]
    The defect detection system of claim 14, wherein the information manipulation system (124) is further configured to compare the well plan with depth-based measurements on the well log to determine a change depth.
    [20" id="c-fr-0020]
    20. A fault detection system according to claim 19, wherein the system
    Information manipulation 10 (124) is further configured to correct depth-based measurements for depth change.
    î / 4
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    法律状态:
    2018-09-28| PLFP| Fee payment|Year of fee payment: 2 |
    2019-10-30| PLFP| Fee payment|Year of fee payment: 3 |
    2020-04-17| PLSC| Publication of the preliminary search report|Effective date: 20200417 |
    2021-05-07| RX| Complete rejection|Effective date: 20210329 |
    优先权:
    申请号 | 申请日 | 专利标题
    IBWOUS2016060751|2016-11-06|
    PCT/US2016/060751|WO2018084863A1|2016-11-06|2016-11-06|Automated inversion workflow for defect detection tools|
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