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    • 专利摘要:
    An acoustic logging system identifies types of hydrocarbon formations by a method of determining real-time constrained slime wave slowness using guided waves from the borehole. The system also combines data processing of different acoustic waveform processing techniques using an information sharing procedure, for example, the use of monopolar source data and dipole source data, to further improve treatment results and to obtain more stable and reliable real-time shear rate responses.
    公开号:FR3058180A1
    申请号:FR1759225
    申请日:2017-10-03
    公开日:2018-05-04
    发明作者:Ruijia Wang；Chung Chang；Baichun Sun
    申请人:Halliburton Energy Services Inc；
    IPC主号:
    专利说明:

    ® FRENCH REPUBLIC
    NATIONAL INSTITUTE OF INDUSTRIAL PROPERTY
    COURBEVOIE © Publication number:
    (to be used only for reproduction orders) (© National registration number
    058 180
    59225 © Int Cl 8 : E 21 B 47/00 (2017.01)
    PATENT INVENTION APPLICATION
    A1
    ©) Date of filing: 03.10.17. © Applicant (s): HALLIBURTON ENERGY SERVICES, © Priority: 03.11.16 IB WOUS2016060367. INC. - US. @ Inventor (s): WANG RUIJIA, CHANG CHUNG and SUN BAICHUN. (43) Date of public availability of the request: 04.05.18 Bulletin 18/18. ©) 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.
    04) REAL-TIME DETERMINATION OF SLUDGE SLOWNESS, TYPE OF TRAINING, AND MONOPOLAR SLOWSPOTS IN DOWNHOLE APPLICATIONS.
    FR 3 058 180 - A1 _ An acoustic logging system identifies the types of hydrocarbon formations by a method of determining the slowness of the mud waves constrained by a real-time model using guided waves from the hole drilling. The system also combines the processing of data from different techniques for processing acoustic waveforms using an information sharing procedure, for example, the use of data from monopolar sources and data from dipolar sources, to further improve treatment results and to obtain more stable and reliable real-time shear slow responses.
    DETERMINATION IN REAL TIME OF SLUDGE LENGTH, TYPE OF TRAINING, AND MONOPOLAR LENS POINTS IN
    DOWNHOLE APPLICATIONS
    AREA OF DISCLOSURE
    The present disclosure relates generally to downhole logging and, more specifically, to methods of determining in real time the slowness of the mud and the type of formation, as well as the optimization of the pointing of the slowness of monopolar refracted shear waves.
    BACKGROUND OF THE INVENTION
    Collecting information about downhole conditions, which is usually called "logging", can be done using several methods, including well drilling ("LWD") and wired line logging . Downhole acoustic logging tools are often used to acquire various characteristics of land formations traversed by the borehole. In such systems, acoustic waveforms are generated using a transmitter, and acoustic responses are received using one or more networks of receivers. The acquired data is then used to determine the slowness (velocities) of the formation and of the borehole fluid, which can be used to calculate characteristics such as porosity, Poisson's ratio, Young's modulus and the modulus of compressibility of the borehole formation or fluid. These features can be useful in well planning and cement or training assessment; for example, to orient the perforation cannons or to assess the stability of the wellbore.
    The borehole waves generated by a pulse source consist of multiple complicated guided waves traveling along the borehole surrounded by a rock. To extract the slowness measurements from these mixed wave movements, such as the slow compression waves ("DTC") and the slow shear waves ("DTS"), or the slow shear waves from the helical waves low frequency in case of LWD, a 2D coherence map is generally used for such purposes. However, the identification and correct pointing of these target wave modes according to the 2D map are difficult, since it is often necessary to manage the low signal / noise ratio (“SNR”), the interference of other wave modes, such as Leakage P, the waves induced by the tools, the Stoneley waves, the waves coming from the road due to the movements of the tools, or the aliases of these modes on the 2D coherence map. All of these reasons can contribute to a complicated wave field of the borehole, thereby reducing the ability to obtain correct, simple and automatic slowdowns in real time.
    In addition, one of the main challenges in processing acoustic data is that the signal processor does not know whether the training is hard or tender (i.e., type of training), while the characteristics of borehole waves are quite different for these two types of training. Typically, the system requires user input if shear waves exist. In real-time processing, the task becomes even more difficult since there is no human-computer interaction. Often, conventional processing of waveform data acquired using only one type of source (for example, monopolar source) is difficult to distinguish if the waves after the refracted compression waves are shear waves, mud waves, leakage P waves, Stoneley waves or high frequency pseudo-Rayleigh waves. Such multiple possibilities lead to the situation where the system cannot automatically point and identify shear waves.
    BRIEF DESCRIPTION OF THE FIGURES
    Figure 1 illustrates an acoustic tool that can be used to perform certain methods illustrative of this disclosure;
    FIG. 2A is a flow diagram of an acoustic logging method making it possible to determine the slowness of the mud waves and to identify the elastic type of the formation by carrying out a treatment of the dispersion of the guided waves coming from the borehole, according to some illustrative methods of this disclosure;
    Figure 2B is a flow diagram of a method of processing the dispersion at block 204 of Figure 2A, according to certain methods illustrative of the present disclosure;
    FIG. 3 is a flow diagram of a method which applies the slowness of the mud waves and the types of formation to constrain the pointing of the slowness of the refracted shear waves, according to certain methods illustrative of the present disclosure;
    FIG. 4 is a diagrammatic representation of a monopolar processing module (processing of refracted waves) and of a dipolar processing module (processing of guided waves);
    FIG. 5 is a flow diagram of a method intended to combine the processing of monopolar and dipolar data by exchange of the results, according to certain methods illustrative of the present disclosure;
    Figure 6 is a flow diagram for a method for combining the processing of monopolar and dipolar data by exchanging results for different waveform acquisitions, according to certain methods illustrative of the present disclosure;
    FIG. 7 is a graph illustrating an example of simultaneous estimation of the slowness of the shear waves, of the slowness of the Scholte waves, and of the slowness of the mud from the bending waves from the borehole generated by a source dipolar, according to certain methods illustrative of the present disclosure;
    FIG. 8 is a representation in the form of a histogram of the slowness of the mud for a well zone generated according to certain methods illustrative of the present disclosure;
    FIG. 9 presents an example of use of the slowness of the mud waves, of the DTC, of the travel time of the compression waves, and of the elastic type of the formation îo to constrain the pointing of the slowness of the refracted shear waves. monopolar, according to certain methods illustrative of the present disclosure;
    FIG. 10A illustrates a sonic / acoustic logging tool used in an LWD application, according to certain illustrative embodiments as described in this document; and
    FIG. 10B illustrates a sonic / acoustic logging tool used in a cable line application according to certain illustrative embodiments as described in this document.
    DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS
    The illustrative embodiments as well as the associated methods of this disclosure are described below as they can be employed in the methods and systems for real time determination of sludge slowness and type of formation, and to optimize the pointing of the slowness of the acoustic waveforms. For the sake of clarity, all the characteristics of an actual implementation or methodology are not described in the present description. It will of course be understood that, in the development of any such real embodiment, many decisions specific to an implementation must be taken to achieve the objectives specific to developers, such as respecting the constraints associated with a system and those associated with companies, which should vary from one implementation to another. In addition, it will be understood that such a development effort may be complex and time-consuming, but that it would nevertheless become a routine task for an ordinary specialist in the field who benefits from the present disclosure. Other aspects and advantages of the various embodiments and associated methodologies of this disclosure will become apparent upon consideration of the following description and associated drawings.
    As described herein, the illustrative systems and methods of this disclosure provide a calculation of the slowness of the mud and a determination of the elastic type of the formation in real time, in addition to optimizing the slowness of the waves shear. As mentioned earlier, one of the main challenges in processing acoustic data is that the system does not know the type of training (i.e., hard or soft). Therefore, the system has difficulty distinguishing whether the waveform data corresponds to shear waves, mud waves, Stoneley waves, etc.
    To fill this gap, the embodiments of the present disclosure reduce the number of waveform possibilities by first identifying the type of formation, from which the system then determines the types of modes that may exist in the complete wave train. To meet the challenges of conventional approaches, the present disclosure provides a sequence that automatically identifies the types of formation by a method of determining the slowness of constrained mud waves by a real-time model using guided waves from 'a borehole. This disclosure also combines the processing of data from different techniques for processing acoustic waveforms using an information sharing procedure, for example, the use of data from monopolar sources and data from dipolar sources, to further improve treatment results and to obtain more stable and reliable real-time shear slow responses.
    In a generalized method of the present disclosure, a logging tool is deployed at the bottom of a well along a borehole and acoustic waveforms are acquired. Using the acquired waveforms, the type of training is determined to be hard or soft training. Hard formations refer to a slow shear wave of the formation less than the slow compression wave of the mud from the borehole. Soft formations refer to a slow shear wave of the formation greater than the slow compression wave of the mud from the borehole. Once the type of training is known, the system then identifies slow points, which are then used to determine various characteristics of the training. These and other advantages will be apparent to an ordinary specialist in the field who benefits from this disclosure.
    Acoustic logging has become a basic logging service for geophysical exploration of boreholes since it provides useful information to the geoscientist and petrophysicist. Acoustic logging tools have progressed from single transmitter and dual receiver tools to become modern networked sonic tools with different types of sources and receivers. The accuracy, range and quality of measurements have been significantly increased, and the scope of the application has also been extended.
    Several acoustic guided waves exist in a drilling hole filled with fluid. For example, a monopolar source can excite a refracted P wave in a borehole, an refracted S wave in a borehole, pseudo-Rayleigh waves of different orders and Stoneley waves if it is present in a borehole fast forming. A monopolar source can generate a refracted P wave, a trailing P wave and Stoneley waves in a slow forming borehole. A dipole source can excite a refracted P wave in a borehole, a refracted S wave in a borehole, and bending waves of different orders if it is present in a rapidly forming borehole. These waves propagate along the axis of the borehole and are all guided waves from the borehole. Among all of the guided waves, the refracted P and S waves from the borehole propagate along the axis of the borehole at the speed of the volume waves of the formation, and therefore these two types of waves are used to extract the slow compression and shear waves of the formation
    Therefore, conventional acoustic logging techniques face significant challenges due to complicated downhole environments. For example, there are always multiple modes in monopolar complete wave trains, including refracted compression waves, refracted shear waves, Stoneley waves, trailing P waves, pseudo-Rayleigh waves and so on. after. If the tool is offset in the wellbore, there may be additional guided waves from the borehole of higher azimuth orders. The existence of these modes depends on the elastic types of the formations. In other words, in the different types of training, different modes can exist. Specifically, in the case of soft formations where the slowness of the shear waves of the formation is greater than the slowness of the mud waves, the refracted and pseudo-Rayleigh shear waves are not present while the trailing P waves are generally well excited when waveforms are recorded with a monopolar source and receivers. In such formations, one cannot directly extract the slowness of the shear waves of the wave trains. For this reason, scoring the slow shear waves according to monopolar waveforms is difficult if one does not know the types of formation and, therefore, if one does not know if waves shear are present.
    Conventional processing always treats refracted wave measurements in the borehole and guided wave measurements as two different types of independent measurements. Consequently, the two separate measurement methods do not exchange information which would impose constraints on each other. For example, conventional approaches can process monopolar data independently to obtain the slowness of refracted compression and shear waves, while separately processing the dipole waveform data to obtain the slowness of shear waves from asymptotes. low frequency bending waves. The slowness of the shear waves from two different types of sources can be adopted together to confirm the responses.
    Therefore, conventional independent processing provides two different responses that can be used to validate each other. However, such independent processing can lead to the situation where the data is not fully used. For example, DTP can be easily obtained in monopolar treatment, but not in dipolar treatment; however, DTC is very useful in determining the slowness-frequency treatment interval for the analysis of the dispersion of dipole bending waves. Another important parameter to help determine the limit of the slowness of shear waves excited by a monopolar source is the slowness of the mud. However, without a separate measurement, the slowness of the mud can only be estimated from the treatment of guided waves.
    In view of these shortcomings in conventional techniques, the present disclosure also provides methods for combining monopolar and dipolar data, using both refracted wave processing and dispersion processing, which is beneficial for advanced processing of sonic log data. To combine refracted wave processing and dispersion processing, this disclosure provides a well-designed sequence, which ensures that the slow responses of monopolar and dipolar sources remain separate solutions, and can be used to validate one the other. In addition, the present disclosure provides sequences which increase the reliability of the processing by combining the two sources.
    To achieve these benefits, certain illustrative methods described in this document exchange processing results between the treatment of refracted waves and the treatment of guided waves in order to improve the response product while also retaining the two methods as independent solutions. acoustic logging. To meet the challenge of identifying types of formation, the present disclosure provides methods for calculating the slowness of mud waves in real time and, further, the type of formation can be identified by comparing the value of the slowness of mud and slow shear wave estimates from processing guided waves from drilling.
    Then, the type of formation and the slowness of the mud waves are communicated to the monopolar processing module to optimize the pointing of the slowness of the refracted shear waves.
    In view of the above, Figure 1 illustrates an acoustic tool that can be used to perform certain illustrative methods of this disclosure. Generally, the acoustic logging tool 100 includes multiple transmitters 102 to drive different borehole modes and a receiver array section 104 which captures the acoustic waves from the borehole of different azimuth orders. The transmitters 102 include a monopolar source 102a, a dipole source Y 102b, and a dipole source X102c. Such an acoustic system makes the tool 100 capable of obtaining the slowness of the compression and shear waves in any type of borehole and of formations. The acoustic tool 100 may be, for example, an Array Sonic Tool ("AST") and a Xaminer® Sonic tool from Halliburton Energy Services, Inc. It should also be noted that in some illustrative embodiments the tool 100 may include multiple receiver stations 1 ... N where each station includes multiple azimuthal AH receivers as illustrated in Figure 1. Finally, the methods described in this document can be applied in any variety of azimuthal receiver networks or other configurations receivers. In addition, the tool 100 can also include transverse dipole sources, specifically an X source or a Y source, so that the methods described in this document can be applied either to X dipole data or to Y dipole data.
    FIG. 2A is a flow diagram of an acoustic logging method 200 making it possible to determine the slowness of the mud waves and to identify the elastic type of the formation by carrying out a treatment of the dispersion of the guided waves coming from the borehole, according to certain illustrative methods of this disclosure. After the acoustic logging tool has been deployed along the borehole and the acoustic pulses have been used, at block 202, the guided waves (for example, bending / helical waveform data) are acquired and entered into the system for processing. At block 204, the theoretical modal dispersions are extracted from the waveform data whereby the asymptote of the high frequency slowness of the data is estimated based on an inversion of the dispersion over the entire frequency range. To achieve this, a variety of dispersion techniques, such as, for example, dispersive wave processing from the borehole using automatic dispersion matching, can be applied to waveform data to extract a complete dispersion curve constrained by the modeling according to the waveform of the network.
    ιο
    To carry out the processing at block 204, various different illustrative methods can be applied. Figure 2B is a flow diagram of a method of processing dispersion at block 204, according to certain methods illustrative of the present disclosure. At block 204A, the measured dispersion curves are calculated based on the waveforms acquired using any desired dispersion extraction method. At block 204B, the actual or simplified theoretical dispersion curves are calculated by direct modeling. In certain illustrative methods, these theoretical dispersion curves can be pre-calculated and saved in the memory, then recalled if necessary during the processing. Then, at block 204C, inversion processing is performed to minimize the mismatch between the theoretical and measured dispersion curves, and therefore the final dispersions are estimated from the theoretical dispersion curves that have the best fit. with the dispersions measured. At block 204D, the asymptotes for the slowness of the dispersion estimates are determined, in which the low frequency asymptotes often denote the slowness of the shear waves for certain guided waves and the high frequency asymptotes represent the slowness of the Scholte waves.
    Referring again to Figure 2A, as discussed in more detail below, in some alternative embodiments, the DTC of monopolar processing can be applied to processing the dispersion of the guided waveforms at block 205 for thus constraining the determination of the type of training. At block 206, the slowness of the Scholte waves is then extracted from the asymptotes of the low frequency slowness of the bending / helical wave dispersion curves estimated using:
    D (fJ = bD (DTS, U, a · f „) + (l ~ b) DTS _ Eq. (L), where DTS represents the slowness of shear waves; AU represents the asymptotes of low frequency slowness; a and b denote the optimal parameters for the simplified model; s shear represents the estimated slowness of the shear waves resulting from the treatment of the dispersion; and) represents the dispersions of the fundamental bending waves which are elaborated with several basic parameters of the model. This illustrative dispersion model introduces two elongation parameters a and b to compensate for the influence of unknown parameters, such as the anisotropy or invasion parameters. Parameter a is adopted to compensate for changes in the frequency axis due to the influence of the other parameters, while parameter b is used to compensate for changes in the slowness axis. A combination of a and b describes the influences of all other parameters on the dispersion responses. Parameters a and b compensate for all errors that are generated by unknown mud / formation factors, and the variables are inverted incrementally in depth during processing.
    Still referring to method 200, the slowness of the mud waves (“DTM”) is calculated at block 208 using the slowness of the Scholte waves according to the following analytical equation:
    2> 2
    DTM =
    DT Sch
    P mud aDTS *
    Eq. (2) or, oAdt ^ -dtc)
    Eq (3), and β = (^ - DTS 2 )
    Eq (4), where DTM represents the slowness of the mud; DTC and DTS denote the slow compression and shear waves of the formation, respectively; DTs c h represents the slowness of the Scholte waves; and ^ mud and Pf rmL , n represent the density of mud and formation, respectively. In real-time processing, the density of the mud is a fixed value that is determined based on the type and formula of the mud, as well as the temperature and downhole pressure for a specific well. The density of the formation is a logarithmic curve which is obtained from the previous density log. If the density diagram does not exist, in some methods, some empirical equations can be used to relate the density of the formation to certain known diagrams / parameters, for example the
    DTC and DTS.
    Once the slowness of the Scholte waves is determined by the asymptote of the high frequency slowness of the bending / helical wave dispersions at block 208, the slowness of the mud is then determined using the Equation 2 above. In some alternative methods, it should be noted that the slowness of the Scholte waves and the slowness of the mud waves can be determined using the asymptote of the high frequency slowness of the Stoneley wave dispersions.
    After the slowness of the mud has been estimated at block 208, the elastic type of the formation is determined. This determination can be made in various ways, including, for example, by comparing the slowness of the mud waves and the estimation of the slow shear waves of the formation from the guided waves from the borehole. For example, if the slow shear waves in the formation is greater than the slowness of the mud waves 10, then the formation is soft; otherwise the training is hard. As previously mentioned, in this document, a hard formation, also called rapid formation, refers to the slow shear waves of the formation which is less than the slowness of the mud waves while a soft formation, also called formation slow, refers to the slowness of the shear waves of the formation which is greater than the slowness of the mud waves. In some illustrative methods, this additional information can be communicated to the monopolar processing module to determine whether refracted shear waves or other waves exist in the waveform or not. Then, as discussed in more detail below, the type of formation is applied to identify the slowness dots used to identify a variety of characteristics of the formation, such as porosity, Poisson ratio, Young's modulus and the compressibility module.
    In certain illustrative methods of this disclosure, the DTM and the determined types of formation of Method 200 can be applied in the processing of refracted wave data, which is generally recognized by industry as monopolar refractive shear waves ( although another treatment may be used). FIG. 3 is a flow diagram of a method 300 which applies the DTM and the types of formation to constrain the pointing of the slowness of the refracted shear waves (“DTRS”). At block 302, based on the type of training (block 210), the system or user determines whether it is necessary to extract the DTRS. For example, if it is a soft formation, the DTRS calculation block can be omitted (block 304) since in such a case, the monopolar source cannot excite the refracted shear waves which could be detected. In the event of hard formation, the process goes to block 306 where the extraction of the DTRS begins.
    At block 306, the travel time of the compression waves ("TTC") and the slowness of the compression waves ("DTC") based on the processing of the monopolar compression waves and the slowness of the mud waves (" DTM ") from the guided wave processing (block 208) are combined to determine the search range for slow shear waves. There follows the physical rule according to which if the DTRS exists, the DTS is always greater than the DTC and less than the DTM:
    , s min = DTC * 1.35
    Eq. (5), and
    Lna * =
    Eq. (6).
    where s mi „is the minimum limit of the DTRS slow search interval and s max is the maximum limit of the DTRS slow search interval. Furthermore, since the DTM does not change rapidly in the well, it can be assumed that the DTM is constant in the section of the entire acoustic system (from transmitter to receiver). Therefore, in some illustrative methods, DTM is used to determine the upper limit of the shear wave travel time ("t max "). The TTC is used to determine the lower limit of the shear wave travel time ("t min ") as:
    = (îTC-z „J · 1.4 + <„ DTM = (TTc-t comp Y dtc comp
    Eq. (7), and
    Eq. (8), or t.
    comp represents a time compensation linked to the radius of the borehole and the duration of the source. Therefore, the search time range is determined at block 308. In an alternative illustrative method, t max can be calculated using an integrated approach:
    RReceiver
    Anax = J DTM (z) dz + flfW (z Sour j (r (z Sour J - r Source ) + W (z Recelver ) (r (z Rece] ver ) - r Receiver )
    Z Source
    Eq. (9), where r represents the radial position of the source or the receiver; z represents the axial position of the source or receiver along the direction of the borehole. After the slowness range and the travel time range have been determined by the equations above in blocks 306 and 308, the system then calculates the similarity map and plots the slowness of the DTRS following the peaks on this card, at block 310.
    Another advantage of the present disclosure is the ability to provide an exchange of processing results between the refracted wave processing and guided wave processing modules. In general, the treatment of refracted waves refers to the various types of monopolar treatment that can be applied, while the treatment of guided waves refers, for example, to the treatment of bending or helical waves. In the present disclosure, the exchange of processing results between the refracted wave processing and guided wave processing modules further constrains the results of the inversion and improves the quality, as well as the accuracy, of the two treatments.
    FIG. 4 is a diagrammatic representation of a monopolar processing module (treatment of refracted waves) and of a dipolar processing module (processing of guided waves). The modules will be part of the computer processing unit used to perform the illustrative process, as will be understood by an ordinary specialist in the field who benefits from this disclosure. For example, as illustrated in FIG. 4, since the DTC can be easily extracted by the monopolar processing, the DTC can be communicated according to the monopolar results to the processing module of the guided waves so as to constrain the determination of the slowness and of the frequency range for estimating bending / helical wave dispersions (for example, block 204 in Figure 2A). This constrains processing by restricting the slowness / frequency search range for bending / helical wave dispersions using empirical equations to generate direct modeling to link knowledge of DTC to the wave dispersion response. bending.
    In addition, the DTM, the DTS and the type of training which are determined by the module for processing the dispersion of the guided waves (FIG. 2A, for example) can be sent to the module for processing the refracted waves, thereby constraining the determination. the range of search for slowness and travel time for the DTRS (for example, blocks 306 & 308). The window of slowness and travel time for the DTRS will constrain the pointing within a reasonable range since in the search range projected for the DTRS, the arrivals of the shear waves dominate the waveform. Consequently, the estimation of the slowness of the DTRS will be improved. Note also that Figure 4 shows only one example of the exchange of results from monopolar and dipolar treatment. In alternative methods, the exchange results can be used to identify other types of waves, for example a Stoneley wave and a Pseudo-Rayleigh wave.
    FIG. 5 is a flow diagram of a method 500 making it possible to combine the processing of monopolar and dipolar data by exchange of the results, according to certain methods illustrative of the present disclosure. In method 500, after the waveform data has been imported at block 502, during a first waveform acquisition ("ACQ n"), the monopolar processing for pointing the DTC is performed at block 504. At block 506, the response from DTC is transmitted to the dipole module to determine the slowness / frequency or the slowness / time range of the DTXX and DTYY. In this disclosure, DTXX represents the estimated shear wave slowness based on waveform data excited by the dipole source X and captured by the dipole receptor network X, and DTYY represents the estimated shear wave slowness according to the waveform data excited by the dipole source Y and captured by the network of dipole receivers Y. Then, at block 508, the DTM, the DTXX or the DTYY and the type of formation for the first Acquisition of waveforms are retransmitted to the monopolar module to determine the range of slowness / admissible travel time of the monopolar refracted shear waves. At block 510, the monopolar / dipolar slowness diagrams are out. Note that in the method 500, certain illustrative methods may require performing monopolar treatment twice in order to point the DTC and the DTRS separately.
    FIG. 6 is a flow diagram for a method 600 making it possible to combine the processing of monopolar and dipolar data by exchange of the results for different waveform acquisitions, according to certain methods illustrative of the present disclosure. In the present disclosure, as in method 500, data is exchanged between the refracted wave processing module and the guided wave processing module. To avoid processing the monopolar data twice, based on the fact that the DTM and the type of formation do not change rapidly depending on the depth since the length of the network of receivers is much more important than the distance between two adjacent shots , the system transmits the DTM, the type of training, and the DTS to the monopolar processing module of the following waveform acquisition. Waveform acquisitions can be distinguished by the depth of the borehole or the acquisition time. Method 600 uses the results of monopolar and dipolar processing to coerce each other, resulting in slow compression waves / shear waves / mud in real time / post- more reliable treatment.
    In method 600, after the waveform data has been imported at block 602, during a first waveform acquisition ("ACQ n"), the monopolar processing for pointing the DTC is performed at block 604. At block 606, the response from DTC is transmitted to the dipole module in order to determine the slowness / frequency or the slowness / time range of the DTXX and the DTYY. In this disclosure, DTXX represents the estimated shear wave slowness based on waveform data excited by the dipole source X and captured by the dipole receptor network X, and DTYY represents the estimated shear wave slowness from waveform data excited by the dipole source Y and captured by the dipole receptor network Y. Next, the DTM, DTXX or DTYY and the type of training for the first waveform acquisition are retransmitted to the monopolar module to determine the range of slowness / admissible travel time of the monopolar refracted shear waves of a second waveform acquisition (“ACQ: n + 1”), where this process is repeated iteratively . Likewise, at block 608, all the results of the dipolar / monopolar processing are output.
    FIG. 7 is a graph illustrating an example of simultaneous estimation of the slowness of the shear waves, the slowness of the Scholte waves, and the slowness of the mud from the bending waves coming from the borehole generated by a dipolar source, according to certain methods illustrative of the present disclosure. DTC, which is determined by monopolar processing, serves as an input parameter to the dipole processing module to help prevent the selection of P or P modes of leakage during scatter plotting. As illustrated in FIG. 7, the similarity map is calculated by a differential phase frequency similarity method, and the estimate of the dispersion of the bending waves is obtained by the automatic dispersion matching method. The slowness of the shear waves is determined by the asymptotes of the low frequency slowness of the bending wave dispersion curves, while the slowness of the Scholte waves is obtained by the asymptotes of the low frequency slowness. Then, the value of the slowness of the mud is determined using Equation 2 of the slowness of the Scholte waves.
    In some situations, the slowness of the mud may not change quickly in the borehole. Therefore, a multi-depth or zoned analysis of the slowness of the mud can be performed during logging. Therefore, in some illustrative methods of the present disclosure, a method using a histogram is applied to analyze the slowness distribution of the mud for the target area as illustrated in Fig. 8, which shows a representation in the form of a sludge slowness histogram for a well area. In the present disclosure, the response of the final slowness of the sludge can be obtained by taking the average of all the values of the slowness of the sludge in this zone. Another illustrative method may select the maximum value of the histogram as being the response of the final slowness of the mud. To obtain better accuracy, an interpolation procedure, such as a “spline interpolation” for example, can be applied to the rhistogram. As illustrated in FIG. 8, the value of the slowness of the mud of 199.6 ps / feet is derived from the curve of the histogram after the interpolation.
    Figure 9 shows an example of the use of DTM, DTC,
    TTC and the elastic type of the formation to constrain the pointing of the monopolar DTRS, according to certain methods illustrative of the present disclosure. In this example, the pointing range of min ' max and min ' max is determined by Equations 5-8 above. By applying additional constraints, the Stoneley peaks and the P wave peaks are excluded from potential candidates, so the peak of the shear waves becomes the only peak remaining in the window.
    The illustrative methods of the present disclosure can be used in a variety of logging applications including, for example, LWD or MWD applications.
    FIG. 10A illustrates a sonic / acoustic logging tool used in an LWD application, which acquires acoustic waveforms and which performs the determinations of slowness using the illustrative methods described in this document. The methods described in this document can be performed by a system control center located on the logging tool or they can be performed by a processing unit at a remote location, such as the surface.
    FIG. 10A illustrates a drilling platform 1002 equipped with a derrick 1004 which supports a lifting device 1006 used to raise and lower a drilling train 1008. The lifting device 1006 suspends an upper drive mechanism 1010 suitable for rotating the drill string 1008 and lowering it through the wellhead 1012. A drill bit 1014 is connected to the lower end of the drill string 1008. When the drill bit 1014 rotates, it creates a wellbore 1016 which passes through various layers of a formation 1018. A pump 1020 circulates a drilling fluid through a supply line 1022 to the upper drive mechanism 1010, makes it descend through the interior of the drill string 1008, through holes in the drill bit io 1014, makes it rise to the surface via the annular space around the drill string 1008, and in a retention pit 1024. The drilling fluid transports the cuttings from the drilling hole towards the pit 1024 and participates in maintaining the integrity of the drilling well 1016. Various materials can be used for the drilling fluid, comprising, but without limitation, conductive sludge based on salt water.
    An acoustic logging tool 1026 is integrated into the downhole assembly near the drill bit 1014. In this illustrative embodiment, the logging tool 1026 is an LWD sonic tool; however, in other illustrative embodiments, the 1026 logging tool can be used in a wired or piped line logging application. If the logging tool is used in an application that has not rotated the assembly at the bottom of the well, the logging tool can be equipped with azimuthally positioned sensors which acquire the measurement of the slowness around the hole drilling. In some other illustrative embodiments, the acoustic logging tool 1026 can be adapted to perform logging operations in both open and closed hole environments.
    In this example, the acoustic logging tool 1026 will include transmitters which may be multipolar and networks of receivers (not shown) which generate acoustic waves in geological formations and which record their transmission. In some embodiments, the transmitters can direct their energies into. substantially opposite directions, while in others only one transmitter can be used and rotated accordingly. The frequency, magnitude, angle and firing time of the transmitters' energy can also be controlled, as desired. In other embodiments, the collected slowness measurements can be stored and processed by the tool itself, while in other embodiments the measurements can be communicated to a remote processing circuit in order to perform the treatment of slowness.
    The acoustic logging tool 1026 is used to acquire data for measuring slowness at many azimuths. As such, some embodiments may also include a directional sensor to determine the orientation of the tool. The illustrative methods described in this document can be used in a variety of propagation modes, including, for example, refracted compression waveforms from the borehole, shear, low frequency bending, low frequency helical, quadrupole or Stoneley.
    Still referring to FIG. 10A, while the drill bit 1014 extends into the wellbore 1016 through the formations 1018, the logging tool 1026 collects the signals for measuring the slowness linked to the various properties / training characteristics, as well as tool orientation and various other drilling conditions. In some embodiments, the logging tool 1026 may take the form of a drill collar, i.e., a thick-walled tubular member which provides weight and rigidity to facilitate the drilling process. A telemetry fitting 1028 can be included to transfer slow images and measurement data / signals to a surface receiver 1030 and to receive commands from the surface. In some embodiments, the telemetry fitting 1028 does not communicate with the surface, but instead stores the slowness measurement data for later retrieval at the surface when the logging set is retrieved.
    In certain embodiments, the acoustic logging tool 1026 includes a system control center (“SCC”), together with the necessary processing / storage / communication circuit, which is coupled in communication to one or “several transmitters / receivers (not shown) used to acquire the slowness measurement signals. In some embodiments, once the acoustic waveforms are acquired, the system control center calibrates the signals, performs the methods of calculating the slowness described in this document, and then communicates the data upwards. hole and / or other components of the assembly via a telemetry fitting 1028. In an alternative embodiment, the system control center can be located at a location remote from the logging tool 1026, as in on the surface or in a different borehole, and performs statistical processing accordingly. These and other variations within this disclosure will be apparent to an ordinary specialist in the field who benefits from this disclosure.
    FIG. 10B illustrates an alternative embodiment of the present disclosure in which a wired line acoustic logging tool acquires and generates slowness signals. At various times during the drilling process, the drill string 1008 can be removed from the borehole, as shown in Figure 10B. After the withdrawal of the drill string 1008, logging operations can be carried out by means of a wired line acoustic logging probe 1034, namely, an acoustic probe suspended by a cable 1041 having conductors for supplying energy to the probe and to transmit telemetry data from the probe to the surface. A 1034 wired line acoustic logging probe may have buffers and / or centering springs to hold the tool near the axis of the borehole while the tool is pulled upward from the hole. An acoustic log probe 1034 can include a variety of transmitters / receivers for measuring acoustic anisotropy. A logging facility 1043 collects the measurements from the logging probe 1034, and includes a computer system 1045 for processing and storing the slowness measurements collected by the sensors, as described in this document.
    In some illustrative embodiments, the system control centers used by the acoustic logging tools described in this document include at least one processor incorporated inside the system control center and a non-transient storage medium readable by computer, all interconnected via a system bus. The instructions of the software executable by the processor making it possible to implement the illustrative processing methods described in this document can be stored in the local storage device or in any other computer-readable medium. It will also be understood that the instructions of the statistical processing software can also be loaded into the storage device from a CD-ROM or from another suitable storage medium by means of wired or wireless methods.
    In addition, an ordinary specialist in the field will understand that the various aspects of disclosure can be put into practice with various configurations of computer systems, notably portable devices, multiprocessor systems, programmable or microprocessor-based consumer electronic systems, minicomputers, mainframe computers, and the like. Any number of computer systems and computer networks is acceptable for use with this disclosure. Disclosure can be practiced in distributed computing environments where the tasks are performed by remote processing devices which are connected via a communication network. In a distributed computing environment, program modules can be located both on local and / or remote computer storage media, including memory storage devices. This disclosure may therefore be implemented in connection with various hardware, software, or a combination thereof, in a computer system or other processing system.
    Therefore, the illustrative methods described in this document provide new methods for automatically determining the slowness of mud waves from field data, as well as for implementing constraints for both monopolar and dipolar processing. , thereby improving the results of the treatment. The disclosure further provides the slowness of the mud in real time, which facilitates the pointing of the monopolar DTRS and which can be applied to the advanced processing of acoustic data. In addition, the methods described in this document can be applied in real time or after treatment or planning.
    The embodiments and methods of this disclosure described in this document further relate to any one or more of the following paragraphs:
    1. A method of acoustic downhole logging, comprising acquiring acoustic waveforms of a borehole extending along a formation; determining a type of training formation using the acquired waveforms; identifying slow points using the type of training; and determining a characteristic of the formation using the slowness dots.
    2. The method according to paragraph 1, in which the slowness of the mud waves is used to determine the type of formation.
    3. The method according to paragraphs 1 or 2, in which the determination of the type of formation comprises the determination of the asymptotes of the slowness of the acquired waveforms; extracting the slowness of Scholte waves from the asymptotes of slowness; calculating the slowness of the mud waves using the slowness of the Scholte waves; and comparing the slowness of the mud waves and the slowness of the shear waves to determine the type of formation.
    4. The method according to any one of paragraphs 1 to 3, wherein determining the asymptotes of slowness comprises calculating a dispersion response of the acquired waveforms; determining the dispersion estimates over the entire frequency range by minimizing a mismatch between the theoretical dispersion curves and the measured dispersion curves; and determining the asymptotes of slowness from estimates of dispersion over the entire frequency range.
    5. The method according to any one of paragraphs 1 to 4, in which the identification of the slowness spikes comprises the determination of a search range for the slowness of the shear waves using the slowness of the sludge, compression wave travel time, and compression wave slowness; determining a travel time of the slow shear waves using the slow mud waves and the travel time of the compression waves; generating a similarity map card using the search range and travel time; and identifying the slow points of the similarity map.
    6. The method according to any one of paragraphs 1 to 51, in which the determination of the type of training is carried out in real time.
    7. The method according to any one of paragraphs 1 to 6, in which processing of the monopolar waves of a first acquisition of waveforms is applied to constrain the determination of the type of formation.
    8. The method according to any one of paragraphs 1 to 7, wherein the processing of the dipole waves from the first acquisition of waveforms is applied to constrain the identification of spikes of slowness.
    9. The method according to any one of paragraphs 1 to 8, wherein the processing of the dipole waves of the first acquisition of waveforms is applied to constrain processing of the monopolar waves of a second acquisition of waveforms ;
    and the second waveform acquisition is acquired at a time or at a different depth of the borehole than the first waveform acquisition.
    10. The method according to any one of paragraphs 1 to 9, wherein the slowness of the mud waves is determined by averaging the slowness of the mud waves over a target area of the borehole.
    11. The method according to any one of paragraphs 1 to 10, wherein the acoustic waveforms are acquired using an acoustic logging tool positioned along a cable line or a set of drilling.
    12. An acoustic logging system, comprising an acoustic logging tool coupled in communication to a processor; and a memory coupled to the processor comprising instructions stored therein, which, when executed by the processor, cause the processor to perform operations including acquiring acoustic waveforms from a borehole s' extending along a formation; determining a type of training formation using the acquired waveforms; identifying slow points using the type of training; and determining a characteristic of the formation using the slowness dots.
    13. The system according to paragraph 12, in which the slowness of the mud waves is used to determine the type of formation.
    14. The system according to paragraphs 12 or 13, in which the determination of the type of formation includes the determination of the asymptotes of the slowness of the acquired waveforms; extracting the slowness of Scholte waves from the asymptotes of slowness; calculating the slowness of the mud waves using the slowness of the Scholte waves; and comparing the slowness of the mud waves and the slowness of the shear waves to determine the type of formation.
    15. The system of any one of paragraphs 12 to 14, wherein determining the asymptotes of slowness includes calculating a dispersion response of the acquired waveforms; determining the dispersion estimates over the entire frequency range by minimizing a mismatch between the theoretical dispersion curves and the mesurée measured dispersion curves; and determining the asymptotes of slowness from estimates of dispersion over the entire frequency range.
    16. The system according to any one of paragraphs 12 to 15, wherein the identification of the slowness spikes comprises determining a range of search for the slowness of the shear waves using the slowness of the waves mud, compression wave travel time, and slow compression waves; determining a travel time of the slow shear waves using the slow mud waves and the travel time of the compression waves; generating a similarity map using the search range and the travel time; and identifying the slow points of the similarity map.
    17. The system according to any one of paragraphs 12 to 16, wherein the determination of the type of training is carried out in real time.
    18. The system according to any one of paragraphs 12 to 17, in which the processing of the monopolar waves of a first acquisition of waveforms is applied to constrain the determination of the type of formation.
    19. The system according to any one of paragraphs 12 to 18, in which the processing of the dipole waves of the first acquisition of waveforms is applied to constrain the identification of the slow dots.
    20. The system according to any one of paragraphs 12 to 19, wherein the processing of the dipole waves of the first acquisition of waveforms is applied to constrain processing of the monopolar waves of a second acquisition of waveforms ; and the second waveform acquisition is acquired at a time or at a different depth of the borehole than the first waveform acquisition.
    21. The system of any one of paragraphs 12 to 20, wherein the slowness of the mud waves is determined by averaging the slowness of the mud waves over a target area of the borehole.
    22. The system according to any one of paragraphs 12 to 21, wherein the acoustic waveforms are acquired using an acoustic logging tool positioned along a cable line or a set of drilling.
    In addition, the preceding paragraphs and other methods described in this disclosure may be incorporated into a system comprising a processing circuit for implementing any of the methods, or into a non-transient computer readable medium comprising instructions which , when executed by at least one processor, causes the processor to perform any of the methods described in this disclosure.
    Although various embodiments and methods have been presented and described, the present disclosure is not limited to such embodiments and methodologies and will be understood to include all of the modifications and variations that would appear apparent to the user. skilled in the art. Thus, it should be understood that this disclosure is not intended to be limited to the particular forms described. On the contrary, the intention of the invention is to cover all the modifications, equivalents and alternatives falling within the spirit and the scope of the present disclosure as defined by the appended claims.
    权利要求:
    Claims (23)
    [1" id="c-fr-0001]
    THE CLAIMS ARE AS FOLLOWS:
    1. Method of acoustic downhole logging comprising:
    5 acquiring acoustic waveforms of a borehole extending along a formation;
    determining a type of training formation using the acquired waveforms;
    identifying slow points using the type of training; and determining a characteristic of the formation using the slowness dots.
    [2" id="c-fr-0002]
    2. The method of claim 1, wherein the slowness of the mud waves is used to determine the type of formation.
    [3" id="c-fr-0003]
    3. Method according to claim 1, in which the determination of the type of training comprises:
    Determining the asymptotes of the slowness of the acquired waveforms;
    extracting the slowness of Scholte waves from the asymptotes of slowness; calculating the slowness of the mud waves using the slowness of the Scholte waves; and comparing the slowness of the mud waves and the slowness of the shear waves to determine the type of formation.
    20
    [4" id="c-fr-0004]
    4. Method according to claim 3, in which the determination of the asymptotes of slowness comprises:
    calculating a dispersion response of the acquired waveforms;
    the determination of the dispersion estimates over the entire frequency range by minimizing a mismatch between the theoretical dispersion curves and the
    25 measured dispersion; and determining the asymptotes of slowness from estimates of dispersion over the entire frequency range.
    [5" id="c-fr-0005]
    5. Method according to claim 3, in which the identification of the slowness spikes comprises:
    Determining a search range for the slow shear waves using the slow mud waves, the travel time of the compression waves, and the slow compression waves;
    determining a travel time of the slow shear waves using the slow mud waves and the travel time of the compression waves;
    Generating a similarity map using the search range and the travel time; and identifying the slow points of the similarity map.
    [6" id="c-fr-0006]
    6. The method of claim 1, wherein the determination of the type of training is carried out in real time.
    [7" id="c-fr-0007]
    7. The method of claim 1, wherein the treatment of monopolar waves of a first acquisition of waveforms is applied to constrain the determination of the type of formation.
    [8" id="c-fr-0008]
    8. The method of claim 7, wherein the processing of the dipole waves of the first acquisition of waveforms is applied to constrain the identification of the slowness spikes.
    [9" id="c-fr-0009]
    9. Method according to claim 8, in which:
    the processing of the dipole waves of the first acquisition of waveforms is applied to constrain a processing of the monopolar waves of a second acquisition of waveforms; and the second waveform acquisition is acquired at a time or at a different depth of the borehole than the first waveform acquisition.
    [10" id="c-fr-0010]
    The method of claim 2, wherein the slowness of the mud waves is determined by averaging the slowness of the mud waves over a target area of the borehole.
    [11" id="c-fr-0011]
    11. The method of claim 1, wherein the acoustic waveforms are acquired using an acoustic logging tool positioned along a wired line or a drilling assembly.
    [12" id="c-fr-0012]
    12. Acoustic logging system, comprising:
    an acoustic logging tool coupled in communication to a processor; and a memory coupled to the processor comprising instructions stored therein, which, when executed by the processor, cause the processor to perform operations comprising:
    acquiring acoustic waveforms from a borehole extending along a formation; determining a type of training formation using the acquired waveforms;
    identifying slow points using the type of training; and determining a characteristic of the formation using the slowness dots.
    [13" id="c-fr-0013]
    13. The system of claim 12, wherein the slowness of the mud waves is used to determine the type of formation.
    [14" id="c-fr-0014]
    14. The system as claimed in claim 12, in which the determination of the type of training comprises:
    determining asymptotes of the slowness of the acquired waveforms; extracting the slowness of Scholte waves from the asymptotes of slowness;
    5 the calculation of the slowness of the mud waves using the slowness of the Scholte waves; and comparing the slowness of the mud waves and the slowness of the shear waves to determine the type of formation.
    [15" id="c-fr-0015]
    15. The system as claimed in claim 14, in which the determination of the asymptotes of slowness comprises:
    ίο the calculation of a dispersion response of the acquired waveforms;
    determining the dispersion estimates over the entire frequency range by minimizing a mismatch between the theoretical dispersion curves and the measured dispersion curves; and determining the asymptotes of slowness from the estimates of the dispersion over the entire frequency range.
    [16" id="c-fr-0016]
    16. The system as claimed in claim 14, in which the identification of the slow points comprises:
    determining a search range for the slowness of the shear waves using the slowness of the mud waves, the travel time of the compression waves, and the slowness
    20 compression waves;
    determining a travel time of the slow shear waves using the slow mud waves and the travel time of the compression waves;
    generating a similarity map using the search range and the travel time; and
    25 the identification of the slow points of the similarity map.
    [17" id="c-fr-0017]
    17. The system of claim 12, wherein the determination of the type of training is carried out in real time.
    [18" id="c-fr-0018]
    18. The system of claim 12, wherein the processing of monopolar waves of a first acquisition of waveforms is applied to constrain the
    30 determining the type of training.
    [19" id="c-fr-0019]
    19. The system of claim 18, wherein the processing of the dipole waves of the first acquisition of waveforms is applied to constrain the identification of the slowness spikes.
    [20" id="c-fr-0020]
    20. The system of claim 19, wherein;
    The processing of the dipole waves of the first acquisition of waveforms is applied to constrain a processing of the monopolar waves of a second acquisition of waveforms; and the second waveform acquisition is acquired at a time or at a different depth of the borehole than the first waveform acquisition.
    5
    [21" id="c-fr-0021]
    21. The system of claim 13, wherein the slowness of the mud waves is determined by averaging the slowness of the mud waves over a target area of the borehole.
    [22" id="c-fr-0022]
    22. The system of claim 12, wherein the acoustic waveforms are acquired using an acoustic logging tool positioned along a wired line or a drilling assembly.
    [23" id="c-fr-0023]
    23. A non-transient computer-readable medium comprising instructions which, when executed by at least one processor, causes the processor to carry out any of the methods according to claims 1 to 11.
    1/1
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    同族专利:
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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 |
    2020-03-20| PLSC| Search report ready|Effective date: 20200320 |
    2021-04-30| RX| Complete rejection|Effective date: 20210325 |
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    IBWOUS2016060367|2016-11-03|
    PCT/US2016/060367|WO2018084847A1|2016-11-03|2016-11-03|Real-time determination of mud slowness, formation type, and monopole slowness picks in downhole applications|
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