A non-stationary maximum likelihood method for estimating the scatter spectrum of full-waveform acoustic logging

Abstract

The present disclosure describes a method and system for estimating the scatter spectrum of full waveform sonic (FWS) logging. A computer-implemented method includes receiving FWS data, performing a frequency-space (FX) transform on the FWS data, using a non-stationary prediction error filter (PEF) inversion on the transformed FWS data to estimate a local matrix L and a local matrix P, calculating an inverse covariance matrix based on the estimated local matrix L and the local matrix P, and obtaining a non-stationary maximum likelihood method (MLM) spectrum based on the inverse covariance matrix.

Description

[0001] Claiming priority

[0002] This application claims priority to U.S. Patent Application No. 16 / 298,540, filed on March 11, 2019, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present disclosure relates to estimating the scatter spectrum of full waveform sonic (FWS) logs, and more particularly, by using a non-stationary maximum likelihood method (MLM). Background Art

[0004] In FWS logging, dispersion analysis has been used to infer lithology, fractures, and fluid types. Effective and accurate dispersion spectrum estimation is important for FWS dispersion analysis. Summary of the invention

[0005] The present disclosure describes methods and systems for estimating the scatter spectrum of full waveform sonic (FWS) logging, including computer-implemented methods, computer program products, and computer systems. A computer-implemented method includes receiving FWS data, performing a frequency-space (FX) transform on the FWS data, using a non-stationary prediction error filter (PEF) inversion on the transformed FWS data to estimate a local matrix L and a local matrix P, calculating an inverse covariance matrix based on the estimated local matrix L and the local matrix P, and obtaining a non-stationary maximum likelihood method (MLM) spectrum based on the inverse covariance matrix.

[0006] Other implementations of this aspect include corresponding computer systems, devices, and computer programs recorded on one or more computer storage devices, all of which are configured to perform the actions of the method. The system of one or more computers can be configured to perform specific operations or actions by software, firmware, hardware, or a combination of software, firmware, or hardware installed on the system that causes the system to perform actions during operation. One or more computer programs can be configured to perform specific operations or actions by including instructions that, when executed by a data processing device, cause the device to perform an action.

[0007] The foregoing and other implementations may each optionally include one or more of the following features, alone or in combination:

[0008] In the first aspect, which can be combined with the general implementation, the method further includes obtaining a non-stationary maximum entropy method (MEM) spectrum based on the estimated local matrix L and the local matrix P.

[0009] A second aspect, which may be combined with any of the preceding aspects, wherein the FWS data is an acoustic waveform recorded by an array of receivers, and the acoustic waveform is excited by a transmitter and propagates along a wellbore above a hydrocarbon reservoir.

[0010] In a third aspect, which may be combined with any of the preceding aspects, the method further comprises estimating rock physical properties of the wellbore region based on the non-stationary MLM spectrum.

[0011] A fourth aspect, which may be combined with any of the preceding aspects, comprises calculating the inverse covariance matrix using the Burg equation.

[0012] A fifth aspect, which may be combined with any of the preceding aspects, comprises performing a non-stationary PEF inversion in the FX domain.

[0013] A sixth aspect may be combined with any of the preceding aspects, wherein the FX transform is a one-dimensional (1D) Fourier transform, and a non-stationary MLM spectrum is obtained independently of a linear moveout (LMO) correction.

[0014] Although generally described as computer-implemented software embodied on a tangible medium that processes and transforms corresponding data, some or all of these aspects may be computer-implemented methods or further included in corresponding systems or other devices for performing the described functions. These and other aspects of the disclosure and details of implementations are set forth in the drawings and the following description. Other features and advantages of the disclosure will become apparent from the description and drawings, and from the claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The patent or application file contains at least one drawing executed in color. Copies of the patent or patent application publication with color drawing(s) will be provided by the Patent and Trademark Office upon request and payment of the necessary fee.

[0016] Figure 1 An example method for estimating the scatter spectrum of a full waveform sonic (FWS) log using a non-stationary maximum likelihood method (MLM) is shown in accordance with some embodiments.

[0017] Figure 2 Example images of synthetic FWS data and corresponding dispersion curves are shown in accordance with some embodiments.

[0018] Figure 3 An example image showing an estimated scatter spectrum using a conventional MLM in accordance with some embodiments.

[0019] Figure 4 Example images showing estimated scatter spectra using non-stationary MLM and non-stationary maximum entropy method (MEM) in accordance with some embodiments.

[0020] Figure 5 Example images showing estimated scatter spectra using conventional MLM and non-stationary MLM according to some embodiments.

[0021] Figure 6 is a block diagram illustrating an example computer system for providing computing functionality associated with the algorithms, methods, functions, processes, procedures, and procedures as described in this disclosure, according to some embodiments.

[0022] Like reference numbers and designations in the various drawings indicate like elements. DETAILED DESCRIPTION

[0023] The following detailed description describes the use of a non-stationary maximum likelihood method (MLM) to estimate the scatter spectrum of a full waveform sonic (FWS) log, and is presented to enable one skilled in the art to make and use the disclosed subject matter in the context of one or more specific embodiments. Various modifications, changes, and permutations of the disclosed embodiments may be implemented and will be apparent to those skilled in the art without departing from the scope of the present disclosure, and the general principles defined may be applicable to other embodiments and applications. Therefore, the present disclosure is not intended to be limited to the embodiments described or shown, but should be accorded the widest scope consistent with the principles and features disclosed.

[0024] In FWS logging, dispersion analysis has been used to infer lithology, fractures, and fluid types. MLM can be used to estimate FWS dispersion spectra. However, MLM relies on, for example, partial linear moveout (LMO) correction or pseudo-integrated averaging and pre-whitening to estimate the MLM kernel (i.e., the inverse local covariance matrix). Therefore, using MLM to estimate FWS dispersion spectra may lead to overestimation and inaccurate representation of FWS dispersion spectra.

[0025] At a high level, the described method provides a non-stationary MLM to estimate the dispersion spectrum of FWS logs. The described non-stationary MLM utilizes non-stationary prediction error filter (PEF) inversion within the framework of the Berg algorithm to estimate the local matrix L and the local matrix P, and uses the local matrix L and the local matrix P to obtain the local inverse covariance. By using the non-stationary MLM, no partial LMO correction is required when estimating the FWS dispersion spectrum. In other words, the non-stationary MLM is independent of the partial LMO correction and pseudo ensemble averaging. Therefore, the computational cost of estimating the FWS dispersion spectrum can be reduced by using the non-stationary MLM. In addition, the described non-stationary MLM can be used as a standard workflow for dispersion estimation and dispersion analysis, provides efficient and accurate dispersion spectrum estimation, and has higher resolution than other stationary MLMs in characterizing velocity dispersion.

[0026] Since FWS logging data is located in the middle frequency band between seismic data and ultrasonic data, FWS logging data is important information for studying dispersion phenomena. Among the dispersion characterization methods, MLM is a data adaptive processing method and can be applied to reflection seismic, vertical seismic contours and FWS processing.

[0027] Typically, in a FWS configuration, a receiver array can record the waveform of the acoustic wave excited by the transmitter and propagating along the wellbore. The recorded signal can be shown by a tilted waveform along the offset direction. For example, in a FWS configuration, there are M receivers in the receiver array with offsets ranging from z1 to z M , and in the i th The sound wave waveform recorded at the receiver is x i (1≤i≤M). The classical MLM form for spectrum estimation can be expressed as:

[0028]

[0029] in w and t c are the angular frequency and recoding time, respectively, and represents the scan slowness s centered around a reference slowness, which is used to partially correct a tilted waveform (usually in the frequency domain) in order to let more data information flow into the window used for MLM analysis. yes The core of equation (1) involves R -1 It is the inverse matrix of the covariance matrix R. R can be expressed as:

[0030] R(w,t c , s) = x(w, t c ,s)x + (w,t c ,s)(2)

[0031] where x + (w,t c , s) is x(w, t c , s), and x(w, t c , s) represents the LMO-corrected short-time Fourier transform (STFT) of the recorded signal of each channel, and the time window size is t w ,Right now:

[0032]

[0033] As shown in equations (1) to (3), R (or R -1 ) is important for spectrum estimation. In some embodiments, averaging the covariance matrix R over adjacent frequency bands of the frequency of interest (also called pseudo ensemble averaging) can be used to improve the estimation accuracy of R. In addition, normalization and pre-whitening can be used to ensure that the covariance matrix R is consistent from R to R. -1 Numerical stability during the inverse transformation of .

[0034] Equation (1) expresses a three-dimensional (3D) MLM form in terms of frequency, time, and slowness. To use the 3D MLM form for spectrum estimation, the following workflow can be performed. For example, a reference slowness and its corresponding sweep slowness range are determined for each frequency. For a particular frequency, an LMO-corrected STFT is performed (i.e., equation (3)), and then R is estimated (i.e., equation (2)). After R is converted to R -1 Afterwards, equation (1) can be used to calculate the specific frequency (i.e., ) spectrum. In many cases, the actual scan slowness is large and cannot be handled efficiently with one reference slowness. In these cases, multiple reference slownesses can be used and the same process can be repeated for each sub-scan range. A 3D spectrum (i.e., ). Since an important task of dispersion analysis is to evaluate the slowness (or speed) and frequency (i.e., ), so in each Energy clusters were manually picked on the graph to quantify this relationship.

[0035] Due to the additional consideration of the time dimension and the repeated partial LMO correction for each reference slowness, the computational cost of 3DMLM (i.e., Equation (1)) can be expensive. Therefore, 3DMLM for spectrum estimation is not affordable in practical applications. In some embodiments, the Berg algorithm can be used as an effective method to reduce the computational cost and efficiently generate a two-dimensional (2D) MLM spectrum (i.e., ).

[0036] Based on the Berg algorithm, estimating the MLM spectrum can be performed in the frequency-space (FX) domain (discussed in more detail using equation (4) below). For example, prediction error filters (PEFs) of all available orders can be constructed by recursively calculating reflection coefficients under the principle of maximizing the forward / backward prediction error energy. Therefore, the lower triangular matrix L containing the PEF coefficients and the diagonal matrix P containing the prediction errors can then be derived and combined using the Berg equation (i.e., equation (9) below) to estimate R -1 and MLM spectrum.

[0037] The Berg algorithm is used for MLM spectrum estimation without considering the time dimension and without performing repeated partial LMO corrections. Therefore, the Berg algorithm for MLM spectrum estimation has significantly lower computational cost than the 3DMLM form for spectrum estimation, making it affordable to use in practical applications. However, the Berg algorithm approaches MLM from a stationary perspective by iteratively solving the matrix L and matrix P for each frequency (discussed in more detail in equations (7) to (9) below), which ignores the inherent non-stationarity of the frequency dimension. In addition, when the Berg algorithm described previously is used for MLM spectrum estimation, the spectrum of each frequency is estimated separately. Therefore, the Berg algorithm described previously cannot capture the dispersion characteristics. In addition, when the Berg algorithm described previously is used for MLM spectrum estimation, pseudo ensemble averaging and pre-whitening are performed to enhance the numerical robustness of the estimation process. Performing pseudo ensemble averaging and pre-whitening can average (or smear) the final estimated dispersion spectrum and result in inaccurate dispersion spectrum estimation.

[0038] In order to efficiently and accurately characterize the dispersion of FWS data, a new workflow for dispersion estimation and analysis, called non-stationary MLM, is described in this disclosure. In non-stationary MLM, an inverse form of non-stationary autoregression is used to estimate local prediction coefficients and prediction errors. Non-stationary MLM has similar efficiency to the previously described Burg algorithm for MLM spectrum estimation. In addition, non-stationary MLM is more competent than the previously described Burg algorithm for MLM spectrum estimation in capturing and preserving the non-stationary dispersion features contained in FWS data.

[0039] To use non-stationary MLM for spectral estimation, the following workflow may be performed. For example, FWS data may be preprocessed before being used for dispersion estimation and analysis. In some embodiments, FWS data does not need to be preprocessed before being used for dispersion estimation and analysis. In other words, preprocessing is an optional step in non-stationary MLM for spectral estimation. Preprocessing may include determining the effective frequency and slowness ranges. Preprocessing may also include signal-to-noise ratio enhancement using a frequency filtering procedure or other suitable denoising procedure. In some cases, preprocessing may include separating a target wave component from the total wave field to reduce the mixing effects of other wave components. In some embodiments, optional preprocessing is implemented to better prepare FWS data for dispersion estimation and analysis.

[0040] Next, a frequency-space (FX) transform (also called a frequency shift transform) is applied to the preprocessed FWS data (or the FWS data if the optional preprocessing is not implemented) instead of the STFT. For example, the FX transform can be performed by taking a one-dimensional (1D) Fourier transform of the recorded signal of each channel (or track). The 1D Fourier transform can be expressed as:

[0041] xi (w,z i ) = ∫x i (t,z i ) -jwt dt(4)

[0042] As shown in equation (3) to equation (4), the FX transform (ie, equation (4)) does not have the partial LMO correction included in equation (3). Therefore, the computational cost of equation (4) is lower than that of equation (3).

[0043] After the FX transform, a non-stationary autoregression can be performed in the FX domain. In some embodiments, a non-stationary prediction error filter (PEF) is used to perform the non-stationary autoregression. In order to obtain a solution that can preserve the dispersion characteristics in the FWS signal, the non-stationary PEF can be formulated by considering the dependence of the PEF coefficients on frequency (along the frequency dimension rather than the offset dimension), that is:

[0044]

[0045] where ε(w) is the PEF error at frequency w, a w，k is the frequency-dependent PEF coefficient, m is the shift index (e.g., indicating the mth th receivers), and i is the PEF order to be analyzed. In some embodiments, shaping regularization can be used to reduce the non-uniqueness of non-stationary PEF inversion. For example, the regularized objective function of equation (5) can be expressed as:

[0046]

[0047] Solving the nonstationary PEF inversion for all available orders (i.e., 0 to M-1), the obtained PEF coefficients and their corresponding prediction errors at frequency w can be used to form the lower triangular matrix L w and the diagonal matrix P w ,Right now:

[0048]

[0049]

[0050] In equation (7), the rightmost column to the leftmost column correspond to the 0th to (M-1th) th In equation (8), p w (i) is the frequency i thThe local L and local P are estimated by inversion of the non-stationary PEF. In doing so, the frequency relationship of the PEF can be properly handled by using shaping regularization. Therefore, the non-stationary features contained in the FWS signal can be properly preserved and represented.

[0051] Based on the Burg equation, the inverse covariance matrix R for each frequency w -1 It can be expressed as:

[0052] R -1 =LP -1 L + (9)

[0053] Using non-stationary MLM, the inverse covariance matrix R can be obtained from equation (9) instead of equation (2) -1 Substitute into equation (1) to obtain the estimated scatter spectrum (i.e., non-stationary MLM spectrum). In addition, the L obtained from equations (7) and (8) can be respectively w and P w Obtain a non-stationary maximum entropy method (MEM) spectrum. For example, the i th The MEM spectrum of order can be expressed as:

[0054]

[0055] Where Δx is the offset spacing interval.

[0056] Figure 1 An example method 100 for estimating the scatter spectrum of a full waveform sonic (FWS) log using a non-stationary maximum likelihood method (MLM) according to some embodiments is shown. For clarity of illustration, the following description generally describes the method 100 in the context of other figures in this specification. For example, the method 100 may be performed by Figure 5 However, it should be understood that method 100 can be performed by any suitable system, environment, software and hardware, or by a suitable combination of systems, environments, software and hardware, as appropriate. In some embodiments, the various steps of method 100 can be run in parallel, combined, cyclically or in any order.

[0057] Method 100 begins at block 105, where full waveform sonic (FWS) data is received. In some embodiments, the FWS data is a sonic waveform recorded by a receiver array. The receiver array can record a sonic waveform excited by a transmitter and propagating along a wellbore on a hydrocarbon reservoir. The recorded signal can be shown by a tilted waveform along an offset direction. For example, the receiver array includes M receivers with an offset range from z1 to z M The i-th receiver array thThe sound wave waveform recorded at the receiver is x i (1≤i≤M).

[0058] At block 110, a frequency-space (FX) transform is performed on the FWS data. The FX transform may be a one-dimensional (1D) Fourier transform (e.g., Equation (4)). In some embodiments, the FWS data may be pre-processed prior to performing the FX transform on the FWS data. For example, a valid frequency and slowness range may be determined based on the FWS data. A frequency filtering procedure (or other suitable denoising procedure) may be used to enhance the signal-to-noise ratio of the FWS data. In some cases, a target wave component may be separated from the total wave field to reduce the mixing effects of other wave components. In doing so, the FWS data may be better prepared for dispersion estimation and analysis.

[0059] At block 115, a non-stationary prediction error filter (PEF) inversion is applied to the transformed FWS data instead of the Berg algorithm to estimate the local matrix L and the local matrix P. In some embodiments, a non-stationary autoregression is performed in the FX domain. For example, the PEF coefficients and the corresponding PEF prediction errors at frequency w can be obtained by solving the non-stationary PEF inversion for all available orders (i.e., 0 to M-1). The local matrix L and the local matrix P (e.g., the lower triangular matrix L w and the diagonal matrix P w ) can be formed using the PEF coefficients and the corresponding PEF prediction error at frequency w (e.g., equations (7) and (8)).

[0060] At block 120, an inverse covariance matrix is ​​calculated based on the estimated local matrix L and the local matrix P. In some embodiments, the inverse covariance matrix is ​​calculated using the Burg equation (e.g., equation (9)). For example, the Burg equation (i.e., ) computes the inverse covariance matrix of frequency w.

[0061] In box 125, a non-stationary maximum likelihood method (MLM) spectrum is obtained based on the inverse covariance matrix. For example, the inverse covariance matrix obtained from equation (9) instead of equation (2) can be used in equation (1) to estimate the non-stationary MLM spectrum (i.e., FWS scatter spectrum). In some embodiments, the non-stationary MLM spectrum is obtained independently of the linear time difference (LMO) correction. The scatter spectrum can be artifact-free and suitable for inferring rock physical properties near the wellbore area. In some embodiments, in addition to the non-stationary MLM spectrum, a non-stationary maximum entropy method (MEM) spectrum can also be obtained based on the estimated local matrix L and local matrix P (e.g., equation (10)).

[0062] Figure 1 The example method 100 shown in FIG. 1 may be modified or reconfigured to include additional, fewer, or different steps ( Figure 1 125 ), these operations may be performed in the order shown or in a different order. For example, after block 125, reservoir properties (e.g., rock physical properties of the wellbore region) may be estimated based on the non-stationary MLM spectrum. In some embodiments, non-stationary MLM, non-stationary MEM, or a combination of both may be applied to actual dipole data (in the following example). Figure 5 In some embodiments, for example, the Figure 1 One or more of the steps shown in , until a termination condition is reached. In some embodiments, Figure 1 One or more of the various steps shown in the may be performed as multiple separate steps, or Figure 1 One or more subsets of the steps shown in can be combined and performed as a single step. In some embodiments, Figure 1 One or more of the individual steps shown in FIG. 1 may also be omitted from the example method 100 .

[0063] Figure 2 An example image 200 of synthetic FWS data and corresponding dispersion curves according to some embodiments is shown. To explore the efficiency and accuracy of non-stationary MLM, synthetic FWS data is used. For example, synthetic data is simulated on 8 channels equally spaced at 0.5 feet. The first channel is 10.75 feet away from the transmitter. Each track in image 205 contains 400 samples in the time direction, with a time interval of 20 microseconds. Figure 2 , two wave components with opposite dispersion characteristics are simulated in the synthetic FWS data. The corresponding dispersion curves (ie, the theoretical dispersion curves of the two wave components) are shown in image 210.

[0064] Figure 3 An example image 300 of an estimated scatter spectrum using conventional MLM according to some embodiments is shown. For example, the Berg algorithm for MLM spectrum estimation described previously is applied to Figure 2 In order to improve the covariance matrix estimation, pseudo ensemble averaging with different frequency averaging radii is performed. Figure 3 In the figure, the curve represented by the dotted line is the actual dispersion. Image 305 shows the estimated dispersion spectrum with a 1-point length radius (125 Hz). Image 310 shows the estimated dispersion spectrum with a 2-point length radius (250 Hz). Image 315 shows the estimated dispersion spectrum with a 4-point length radius (500 Hz). Figure 3 As shown, pseudo ensemble averaging is not a good way to capture and preserve the characteristics of non-stationary signals, so conventional MLM cannot generate high-resolution scatter spectra. In addition, as the frequency radius increases, the resolution of the obtained scatter spectra decreases.

[0065] Figure 4 An example image 400 of an estimated scatter spectrum using a non-stationary MLM and a non-stationary maximum entropy method (MEM) according to some embodiments is shown. For example, the previously described non-stationary MLM is applied to Figure 2 Synthetic FWS data in . Figure 4 In FIG. 4 , image 405 shows the estimated dispersion spectrum of the non-stationary MLM, while image 410 shows the estimated dispersion spectrum of the second-order non-stationary MEM (i.e., i=2). As mentioned above, the non-stationary MLM is independent of the partial LMO correction and pseudo ensemble averaging. Therefore, the non-stationary MLM can effectively handle non-stationary signals from the perspective of non-stationary PEF inversion and produce focused and accurate results on dispersion characterization (i.e., image 405). As shown in FIG. Figure 4 As shown, non-stationary MLM and second-order non-stationary MEM outperform in terms of accuracy and reliability. Figure 3 In addition, Figure 4 It is shown that non-stationary MLM is able to achieve high resolution in characterizing velocity dispersion. Therefore, the estimated dispersion spectrum using non-stationary MLM is preferred and informative for inferring petrophysical properties near, for example, the wellbore region.

[0066] Figure 5 An example image 500 of an estimated scatter spectrum using a conventional MLM and a non-stationary MLM according to some embodiments is shown. For example, the conventional MLM and the non-stationary MLM described previously are applied to real dipole data. Figure 5 The actual dipole data used in comes from the embedded component, which is generated by the embedded dipole source (also called XX component) on the sandstone reservoir. Figure 5 In FIG. 5 , image 505 shows the estimated scatter spectrum from a conventional MLM, while image 510 shows the estimated scatter spectrum from a non-stationary MLM. Figure 5 As shown, image 510 shows that the slowness for the non-stationary MLM increases nonlinearly with increasing frequency in the frequency range of 2000-4500 Hz. However, image 505 does not show the clear dispersion characteristics of the conventional MLM. Therefore, the non-stationary MLM can generate a more reasonable and high-resolution dispersion representation than the conventional MLM.

[0067] Figure 6600 is a block diagram of an example computer system 600 for providing computing functions associated with algorithms, methods, functions, processes, flows and procedures as described in the present disclosure, according to an embodiment. The computer 602 shown is intended to include any computing device, such as a server, a desktop computer, a laptop / notebook computer, a wireless data port, a smart phone, a personal digital assistant (PDA), a tablet computing device, or one or more processors within these devices, or any other suitable processing device (including physical and / or virtual instances of computing devices (or both)). Additionally, the computer 602 may include a computer that includes an input device (such as a keypad, keyboard, or touch screen or other device) that can accept user information and an output device that conveys information associated with the operation of the computer 602, including digital data, visual or audio information (or a combination of information) or a graphical user interface GUI.

[0068] The computer 602 can function as a client, a network component, a server, a database or other persistent or any other component (or combination thereof) of a computer system for performing the subject matter described in the present disclosure. The illustrated computer 602 is communicatively coupled to a network 630. In some implementations, one or more components of the computer 602 can be configured to operate in an environment (or combination of environments) including a cloud-based, local, global, or other environment.

[0069] At a high level, the computer 602 is an electronic computing device operable to receive, send, process, store, or manage data and information associated with the described subject matter. According to some embodiments, the computer 602 may also include or be communicatively coupled to an application server, an email server, a web server, a cache server, or a streaming data server, or other server (or combination of servers).

[0070] Computer 602 may receive requests from a client application (e.g., an application executing on another computer) over network 630 and respond to received requests by processing the received requests using an appropriate software application. In addition, requests may be sent to computer 602 from internal users (e.g., from a command console or through other appropriate access methods), external or third parties, other automated applications, and any other appropriate entity, person, system, or computer.

[0071] Each of the components of computer 602 can communicate using system bus 603. In some embodiments, any or all components (hardware and / or software (or a combination of hardware and software)) of computer 602 can use application programming interface (API) 612 or service layer 613 (or a combination of API 612 and service layer 613) to interact with each other or interface 604 (or a combination of both) through system bus 603. API 612 may include specifications for routines, data structures, and object classes. API 612 may be independent of or dependent on computer languages ​​and may refer to a complete interface, a single function, or even a set of APIs. Service layer 613 provides software services to computer 602 or other components (whether or not shown) that are communicatively coupled to computer 602. The functions of computer 602 may be accessible to all service consumers using the service layer. Software services (e.g., software services provided by service layer 613) provide reusable, defined business functions through defined interfaces. For example, the interface may be software written in JAVA, or C++, or other suitable languages ​​that provide data in an extensible markup language (XML) format or other suitable format. Although shown as an integrated component of the computer 602, alternative embodiments may show the API 612 or service layer 613 as a separate component relative to other components of the computer 602 or communicatively coupled to other components of the computer 602 (whether or not shown). In addition, without departing from the scope of the present disclosure, any or all portions of the API 612 or service layer 613 may be implemented as a sub-module or sub-module of another software module, enterprise application, or hardware module.

[0072] Computer 602 includes interface 604. Although Figure 6 604, but two or more interfaces 604 may be used depending on the particular needs, desires, or particular implementations of the computer 602. The interface 604 is used by the computer 602 to communicate with other systems (whether or not shown) in a distributed environment connected to a network 630. Typically, the interface 604 includes logic encoded in software or hardware (or a combination of software and hardware) and is operable to communicate with the network 630. More specifically, the interface 604 may include software supporting one or more communication protocols associated with the communication, so that the network 630 or the hardware of the interface is operable to transmit physical signals within and outside the computer 602 shown.

[0073] Computer 602 includes processor 605. Although Figure 6A single processor 605 is shown in the figure, but two or more processors may be used depending on the particular needs, desires, or particular implementation of the computer 602. In general, the processor 605 executes instructions and manipulates data to perform the operations of the computer 602 and any algorithms, methods, functions, processes, procedures, and processes as described in this disclosure.

[0074] Computer 602 also includes database 606, which can store data for computer 602 or other components (whether or not shown) that can be connected to network 630 (or a combination of both). For example, database 606 can be internal memory, conventional, or other types of databases that store data consistent with the present disclosure. In some embodiments, database 606 can be a combination of two or more different database types (e.g., a mix of internal memory and conventional databases), depending on the specific needs, desires, or specific implementation of computer 602 and the functionality described. Although in Figure 6 602, two or more databases (of the same or combined types) may be used depending on the particular needs, desires, or particular implementation and functionality described for the computer 602. Although the database 606 is shown as an integral component of the computer 602, in alternative implementations, the database 606 may be external to the computer 602.

[0075] The computer 602 also includes a memory 607 that can hold data for the computer 602 or other components (whether or not shown) that can be connected to the network 630 (or a combination of both). For example, the memory 607 can be a random access memory (RAM), a read-only memory (ROM), an optical disk, a magnetic disk, etc., storing data consistent with the present disclosure. In some embodiments, the memory 607 can be a combination of two or more different types of memory (e.g., a combination of RAM and magnetic storage), depending on the particular needs, desires, or specific implementation and described functionality of the computer 602. Although in Figure 6 Although shown as a single memory 607 in the computer 602, two or more memories 607 (of the same or combined types) may be used depending on the particular needs, desires, or particular implementation and described functionality of the computer 602. Although the memory 607 is shown as an integral component of the computer 602, in alternative implementations, the memory 607 may be external to the computer 602.

[0076] The application 608 is an algorithmic software engine that provides functionality, particularly with respect to the functionality described in the present disclosure, according to the specific needs, desires, or specific implementations of the computer 602. For example, the application 608 may be implemented as one or more components, modules, applications, etc. Furthermore, although illustrated as a single application 608, the application 608 may be implemented as multiple applications 608 on the computer 602. Additionally, although illustrated as being integrated with the computer 602, in alternative implementations, the application 608 may be external to the computer 602.

[0077] There may be any number of computers 602 associated with or external to the computer system containing computer 602, each of which communicates via network 630. In addition, the terms "client," "user," and other appropriate terms may be used interchangeably as appropriate without departing from the scope of the present disclosure. In addition, the present disclosure encompasses that many users may use one computer 602, or that one user may use multiple computers 602.

[0078] The embodiments of the subject matter and functional operations described in this specification may be implemented in digital electronic circuits, in tangibly implemented computer software or firmware, in computer hardware, including the structures disclosed in this specification and their structural equivalents, or in a combination of one or more of them. The embodiments of the subject matter described in this specification may be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible non-transitory computer-readable computer storage medium for execution by a data processing device or for controlling the operation of a data processing device. Alternatively or additionally, program instructions may be encoded on an artificially generated propagation signal (e.g., a machine-generated electrical, optical, or electromagnetic signal), which is generated to encode information for transmission to a suitable receiver device for execution by a data processing device. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of computer storage media.

[0079] The term "data processing device", "computer" or "electronic computer equipment" (or equivalents understood by those of ordinary skill in the art) refers to data processing hardware, and includes various devices, equipment and machines for processing data, for example, including a programmable processor, a computer, or multiple processors or computers. The device may also be or also include a dedicated logic circuit, for example, a central processing unit (CPU), an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit). In some embodiments, the data processing device or dedicated logic circuit (or a combination of data processing devices or dedicated logic circuits) may be hardware-based or software-based (or a combination of hardware-based and software-based). Optionally, the device may include code that creates an execution environment for a computer program, for example, code that constitutes a processor firmware, a protocol stack, a database management system, an operating system, or a combination of an execution environment. The present disclosure contemplates the use of a data processing device with or without a conventional operating system (e.g., LINUX, UNIX, WINDOWS, MACOS, ANDROID, IOS, or any other suitable conventional operating system).

[0080] Computer programs (which may also be referred to or described as programs, software, software applications, modules, software modules, scripts or codes) may be written in any form of programming language, including compiled or interpreted languages, or declarative or procedural languages, and computer programs may be deployed in any form, including as separate programs or as modules, components, subroutines, or other units suitable for use in a computing environment. Computer programs may but need not correspond to files in a file system. Programs may be stored in a portion of a file that stores other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple collaborative files (e.g., files storing one or more modules, subroutines or code portions). Computer programs may be deployed to execute on one computer or on multiple computers located at one site or distributed at multiple sites and interconnected by a communication network. Although portions of the programs shown in the figures are shown as individual modules that implement various features and functions through various objects, or methods or other processes, the programs may alternatively include multiple submodules, third-party services, components, libraries, etc., when appropriate. Rather, the features and functionality of the various components may be combined into a single component as appropriate.The thresholds used to make computational determinations may be determined statistically, dynamically, or both statistically and dynamically.

[0081] The methods, processes, or logic flows described in this specification may be performed by one or more programmable computers that execute one or more computer programs to perform functions by operating input data and generating output. The methods, processes, or logic flows may also be performed by a dedicated logic circuit (e.g., a CPU, FPGA, or ASIC), and the apparatus may also be implemented as a dedicated logic circuit (e.g., a CPU, FPGA, or ASIC).

[0082] The computer that is suitable for executing a computer program can be based on a general or special microprocessor, both or any other type of CPU. Typically, the CPU will receive instructions and data from a read-only memory (ROM) or a random access memory (RAM) or both. The essential element of a computer is a CPU for executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include one or more large-capacity storage devices (e.g., disks, magneto-optical disks or optical disks) for storing data, or operably coupled so as to receive or send data or both from the one or more large-capacity storage devices. However, the computer does not need to have these devices. In addition, the computer can be embedded in another device, for example, a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver or a portable storage device (e.g., a universal serial bus (USB) flash drive), which is just to give a few examples.

[0083] Computer-readable media (temporary or non-temporary) suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including, for example, semiconductor memory devices, such as erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM) and flash memory devices; magnetic disks (such as internal hard disks or removable disks); magneto-optical disks; and CD ROM, DVD+ / -R, DVD-RAM and DVD-ROM disks. The memory can store a variety of objects or data, including: cache areas, classes, frameworks, applications, backup data, jobs, web pages, web page templates, database tables, knowledge bases storing dynamic information, and any other appropriate information including any parameters, variables, algorithms, instructions, rules, constraints, references thereto. In addition, the memory may include any other appropriate data, such as logs, policies, security or access data, report files, etc. The processor and memory can be supplemented by or incorporated into special purpose logic circuits.

[0084] To provide interaction with a user, the subject matter described in this specification may be implemented on a computer having a display device (e.g., a CRT (cathode ray tube), LCD (liquid crystal display), LED (light emitting diode), or plasma monitor) for displaying information to the user and a keyboard and pointing device (e.g., a mouse, trackball, or trackpad) through which the user can provide input to the computer. A touch screen (e.g., a tablet computer surface with pressure sensitivity, a multi-touch screen using capacitance or electrical sensing, or other types of touch screens) may also be used to provide input to the computer. Other types of devices may also be used to provide interaction with a user; for example, the feedback provided to the user may be any form of sensory feedback, such as visual feedback, auditory feedback, or tactile feedback; and input from the user may be received in any form, including sound, voice, or tactile input. In addition, a computer may interact with a user by sending or receiving documents to or from a device used by the user; for example, by sending a web page to a web browser in response to a request received from a web browser on a user's client device.

[0085] The term "graphical user interface" or "GUI" may be used in the singular or plural to describe one or more graphical user interfaces and each display of a particular graphical user interface. Thus, a GUI may represent any graphical user interface, including but not limited to a web browser, a touch screen, or a command line interface (CLI) that processes information and efficiently presents the results of the information to a user. Typically, a GUI may include a plurality of user interface (UI) elements, some or all of which are associated with a web browser, such as interactive fields, drop-down lists, and buttons. These and other UI elements may be related to or represent the functionality of a web browser.

[0086] Implementations of the subject matter described in this specification may be implemented in a computing system that includes a back-end component (e.g., a data server), or includes a middleware component (e.g., an application server), or includes a front-end component (e.g., a client computer with a graphical user interface or a web browser through which a user can interact with an implementation of the subject matter described in this specification), or any combination of one or more such back-end components, middleware components, or front-end components. The components of the system may be interconnected by a medium or any form (e.g., a communication network) of wired or wireless digital data communication (or a combination of data communications). Examples of communication networks include a local area network (LAN), a radio access network (RAN), a metropolitan area network (MAN), a wide area network (WAN), a world interoperability for microwave access (WIMAX), a network (WLAN) using, for example, 802.11a / b / g / n or 802.20 (or a combination of 802.11x and 802.20 or other protocols consistent with the present disclosure), all or a portion of the Internet, or any other communication system (or combination of communication networks) at one or more locations. The network may transport, for example, Internet Protocol (IP) packets, Frame Relay frames, Asynchronous Transfer Mode (ATM) cells, voice, video, or data, or other suitable information (or combination of communication types) between network addresses.

[0087] A computer system may include clients and servers. Clients and servers are generally remote from each other and typically interact through a communication network. The relationship of client and server is created by computer programs running on the respective computers and having a client-server relationship to each other.

[0088] Although this specification contains many specific implementation details, these details should not be interpreted as limitations on the scope that can be claimed, but rather as descriptions of features that are peculiar to specific examples. Specific features described in this specification in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, the various features described in the context of a single embodiment may also be implemented separately or in any suitable sub-combination in multiple embodiments. Moreover, although the previously described features may be described as working in certain combinations and even initially claimed to be so protected, in some cases, one or more features from the claimed combination may be deleted from the combination, and the claimed combination may refer to a sub-combination or a variation of a sub-combination.

[0089] Specific embodiments of the subject matter have been described. It will be apparent to those skilled in the art that other embodiments, variations, and permutations of the described embodiments are within the scope of the claims below. Although operations are described in a particular order in the drawings or claims, this should not be construed as requiring that the operations be performed in the particular order shown or in a sequential order, or requiring that all of the operations shown be performed (some operations may be considered optional), in order to achieve the desired results. In some cases, multitasking or parallel processing (or a combination of multitasking and parallel processing) may be advantageous and performed as appropriate.

[0090] In addition, the separation or integration of various system modules and components in the foregoing embodiments should not be understood as requiring such separation or integration in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0091] Therefore, the foregoing exemplary embodiments do not define or limit this specification. Other changes, substitutions and variations are possible without departing from the spirit and scope of the present disclosure.

[0092] Furthermore, any claimed embodiments are considered applicable to at least one computer-implemented method; a non-transitory computer-readable medium storing computer-readable instructions for performing the computer-implemented method; and a computer system comprising a computer memory interoperably coupled to a hardware processor configured to perform the computer-implemented method or the instructions stored on the non-transitory computer-readable medium.

Claims

1. A method for estimating the scatter spectrum of full waveform acoustic FWS logging, comprising: Receiving full waveform acoustic FWS data, wherein the FWS data is an acoustic waveform recorded by a receiver array including M receivers, the acoustic waveform being excited by a transmitter and propagating along a wellbore on a hydrocarbon reservoir, and M is an integer greater than 1; performing a frequency-space FX transform on the FWS data; Using non-stationary prediction error filter PEF inversion instead of the Berg algorithm on the transformed FWS data to obtain frequency-dependent PEF coefficients and corresponding frequency-dependent PEF prediction errors, wherein the frequency-dependent PEF coefficients capture non-stationary features contained in the FWS data, the non-stationary features including dispersion features; estimating a local matrix L and a local matrix P, wherein the estimated local matrix L is formed using the frequency-dependent PEF coefficients and the estimated local matrix P is formed using the corresponding frequency-dependent PEF prediction errors; Calculating an inverse covariance matrix based on the estimated local matrix L and the estimated local matrix P; Obtaining a non-stationary maximum likelihood method MLM spectrum based on the inverse covariance matrix, wherein the non-stationary MLM spectrum uses the captured non-stationary features to generate a high-resolution scattered spectrum independent of linear time difference correction; and The petrophysical properties of the oil and gas reservoir near the wellbore are obtained based on the non-stationary MLM spectrum.

2. The method according to claim 1 further comprises obtaining a non-stationary maximum entropy method MEM spectrum based on the estimated local matrix L and the local matrix P.

3. The method according to claim 1, wherein: The inverse covariance matrix is ​​calculated using the Burg equation.

4. The method according to claim 1, wherein: The FX transform is a one-dimensional Fourier transform and obtains the non-stationary MLM spectrum independently of the linear time difference correction.

5. An apparatus for estimating the scatter spectrum of full waveform acoustic wave FWS logging, comprising: Memory; as well as A processing unit is arranged to perform operations, the operations comprising: Receiving full waveform acoustic FWS data, wherein the FWS data is an acoustic waveform recorded by a receiver array including M receivers, the acoustic waveform being excited by a transmitter and propagating along a wellbore on a hydrocarbon reservoir, and M is an integer greater than 1; performing a frequency-space FX transform on the FWS data; Using non-stationary prediction error filter PEF inversion instead of the Berg algorithm on the transformed FWS data to obtain frequency-dependent PEF coefficients and corresponding frequency-dependent PEF prediction errors, wherein the frequency-dependent PEF coefficients capture non-stationary features contained in the FWS data, the non-stationary features including dispersion features; estimating a local matrix L and a local matrix P, wherein the estimated local matrix L is formed using the frequency-dependent PEF coefficients and the estimated local matrix P is formed using the corresponding frequency-dependent PEF prediction errors; Calculating an inverse covariance matrix based on the estimated local matrix L and the estimated local matrix P; Obtaining a non-stationary maximum likelihood method MLM spectrum based on the inverse covariance matrix, wherein the non-stationary MLM spectrum uses the captured non-stationary features to generate a high-resolution scattered spectrum independent of linear time difference correction; and The petrophysical properties of the oil and gas reservoir near the wellbore are obtained based on the non-stationary MLM spectrum. 6 . The apparatus of claim 5 , the operation further comprising obtaining a non-stationary maximum entropy method (MEM) spectrum based on the estimated local matrix L and the local matrix P.

7. The device according to claim 5, wherein: The inverse covariance matrix is ​​calculated using the Burg equation.

8. The device according to claim 5, wherein: The FX transform is a one-dimensional Fourier transform and obtains the non-stationary MLM spectrum independently of the linear time difference correction.

9. A non-transitory computer-readable medium storing instructions executable by a computer system to perform operations comprising: Receiving full waveform acoustic FWS data, wherein the FWS data is an acoustic waveform recorded by a receiver array including M receivers, the acoustic waveform being excited by a transmitter and propagating along a wellbore on a hydrocarbon reservoir, and M is an integer greater than 1; performing a frequency-space FX transform on the FWS data; Using non-stationary prediction error filter PEF inversion instead of the Berg algorithm on the transformed FWS data to obtain frequency-dependent PEF coefficients and corresponding frequency-dependent PEF prediction errors, wherein the frequency-dependent PEF coefficients capture non-stationary features contained in the FWS data, the non-stationary features including dispersion features; estimating a local matrix L and a local matrix P, wherein the estimated local matrix L is formed using the frequency-dependent PEF coefficients and the estimated local matrix P is formed using the corresponding frequency-dependent PEF prediction errors; Calculating an inverse covariance matrix based on the estimated local matrix L and the estimated local matrix P; Obtaining a non-stationary maximum likelihood method MLM spectrum based on the inverse covariance matrix, wherein the non-stationary MLM spectrum uses the captured non-stationary features to generate a high-resolution scattered spectrum independent of linear time difference correction; and The petrophysical properties of the oil and gas reservoir near the wellbore are obtained based on the non-stationary MLM spectrum. 10 . The medium of claim 9 , the operations further comprising obtaining a non-stationary maximum entropy method (MEM) spectrum based on the estimated local matrix L and the local matrix P.

11. The medium according to claim 9, wherein The inverse covariance matrix is ​​calculated using the Burg equation.

12. The medium according to claim 9, wherein: The FX transform is a one-dimensional Fourier transform, and the non-stationary MLM spectrum is obtained independently of the linear time difference correction.

Images