A far-detecting acoustic wave logging well-side velocity high-precision waveform inversion method and system
Patent Information
- Application Number
- CN202311108028.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-08-30
- Publication Date
- 2026-10-09
- Estimated Expiration
- 2043-08-30
AI Technical Summary
早期发展的Kirchhoff偏移和单程波偏移因处理效率高，在远探测声波测井实际资料处理中得到广泛应用，却因理论基础近似，无法适应测井观测系统下的强垂向变速、有效实现低倾角裂缝的偏移归位
[0039] This invention seeks to minimize the error between observational and simulated data within the framework of least squares optimization theory, achieving extremely high velocity inversion accuracy. It can provide a precise migration velocity model for long-range exploration and simultaneously promote the development of long-range exploration from quantitative structural interpretation to qualitative physical property interpretation.
Smart Images
Figure CN119535606B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of remote acoustic logging velocity inversion technology, and specifically relates to a high-precision waveform inversion method and system for remote acoustic logging wellside velocity. Background Technology
[0002] Long-range acoustic logging is a new generation of acoustic logging technology that utilizes seismic exploration data processing techniques to process acoustic logging data. When fractured-vuggy reflectors are present near the wellbore, the acoustic waves excited by the long-range acoustic logging instrument will generate reflected waves at the reflectors. These reflected waves are then received and recorded by the instrument after returning to the wellbore. Through data processing, an imaging profile of the reflector is obtained, thereby identifying and evaluating fractured-vuggy reservoirs outside the wellbore. Long-range acoustic logging combines the advantages of high vertical resolution of acoustic logging and long-range detection depth of seismic exploration, breaking through the resolution limits of seismic exploration. It extends the detection distance of array acoustic logging from three meters to about forty meters near the wellbore (observed by the dipole source of the XMAC-F1 instrument). It is an important tool for effectively identifying small-scale concealed reservoirs and achieving increased reserves and production in today's complex exploration environment. Similar to seismic exploration, the data processing flow of long-range acoustic logging includes reflected wave processing and migration imaging.
[0003] Based on different theoretical foundations, long-range acoustic logging migration imaging can be divided into ray migration and wave equation migration. Ray migration is represented by Kirchhoff migration and Gaussian beam migration, while common wave migrations include one-way wave migration and reverse-time migration. Early Kirchhoff migration and one-way wave migration, due to their high processing efficiency, were widely used in the processing of actual long-range acoustic logging data. However, due to their similar theoretical foundations, they could not adapt to the strong vertical velocity variations in logging observation systems and effectively achieve migration and relocation of low-dip fractures. Reverse-time migration imaging based on the full wave equation offers higher accuracy and can achieve migration and relocation of complex wave fields such as gyratory waves and prismatic waves. However, research shows that high-precision migration imaging algorithms require higher accuracy in velocity models, and velocity modeling in long-range acoustic logging remains a research gap. Common processing methods include using uniform velocity models or time difference curve interpolation. Summary of the Invention
[0004] To address the above problems, this invention provides a high-precision waveform inversion method for well-side velocity retrieval using remote acoustic logging, the method comprising:
[0005] An initial velocity model is constructed, and the source wavelet is input into the initial velocity model to perform a time-forward simulation to obtain simulation data and the forward propagation wave field;
[0006] Collect remote acoustic logging data and preprocess the remote acoustic logging data to obtain actual observation data;
[0007] The residual of the reflected wave is obtained based on the simulation data and the actual observation data;
[0008] The reflected wave residual is input into the initial velocity model for time-reverse simulation to obtain the reverse propagation wave field, and the velocity gradient is constructed by combining it with the forward propagation wave field.
[0009] The initial velocity model is iteratively updated based on the velocity gradient to obtain the final inversion result.
[0010] Preferably, the remote acoustic logging data includes reflected wave waveform curves, time difference curves, and gain curves.
[0011] Preferably, the preprocessing includes: extracting reflected waves, restoring amplitude, and deconvolution processing on the reflected wave curve.
[0012] Preferably, the initial velocity model is divided into a theoretical model and an actual model.
[0013] Preferably, the construction of the theoretical model includes the following steps:
[0014] A velocity model was designed based on the development pattern of the fractures and cavities near the well.
[0015] The theoretical initial velocity model is obtained by smoothing the velocity model.
[0016] Preferably, the construction of the actual model includes the following steps:
[0017] A layered velocity model was designed by performing lateral interpolation on the time difference curve;
[0018] The actual initial velocity model is obtained by smoothing the layered velocity model.
[0019] Preferably, obtaining the reflected wave residual based on actual data and simulated data includes:
[0020] Divide the actual data by the simulated data;
[0021] The square of the difference result is divided by two to obtain the residual of the reflected wave.
[0022] Preferably, the construction of the velocity gradient includes:
[0023] The reflected wave residual is used as the source to simulate the propagation process of the wave in the iteratively updated initial velocity model to obtain the reverse propagation wave field. The velocity gradient is obtained by combining the forward propagation wave field and the cross-correlation gradient.
[0024] This invention also proposes a high-precision waveform inversion system for well-side velocity in remote acoustic logging, the system comprising a data acquisition module, a data collection module, a data calculation module, a gradient construction module, and an iterative inversion module;
[0025] The data acquisition module is used to construct the initial velocity model. The source wavelet is input into the initial velocity model to perform time-forward simulation to obtain simulation data and forward propagation wave field.
[0026] The data collection module is used to collect remote acoustic logging data and preprocess the remote acoustic logging data to obtain actual observation data;
[0027] The data calculation module is used to acquire simulated data and calculate the reflected wave residuals based on the simulated data and actual observation data;
[0028] The gradient construction module is used to obtain the reverse propagation wavefield by using the reverse propagation residual of the contrast wave, and to construct the velocity gradient by combining it with the forward propagation wavefield.
[0029] The iterative inversion module is used to iteratively update the initial velocity model based on the velocity gradient to obtain the final inversion result.
[0030] Preferably, the data calculation module obtains the reflected wave residual based on actual data and simulated data, including:
[0031] The data calculation module subtracts the actual data from the simulated data;
[0032] The square of the difference result is divided by two to obtain the residual of the reflected wave.
[0033] This invention proposes an electronic device, comprising:
[0034] Processor and memory;
[0035] The processor invokes the computer program stored in the memory to execute the high-precision waveform inversion method for well-side velocity in remote acoustic logging according to any one of the above.
[0036] This invention proposes a computer-readable storage medium.
[0037] The computer-readable storage medium stores a computer program that, when executed by a processor, enables the processor to perform the high-precision waveform inversion method for well-side velocity in remote acoustic logging as described above.
[0038] The present invention has the following beneficial effects:
[0039] This invention seeks to minimize the error between observational and simulated data within the framework of least squares optimization theory, achieving extremely high velocity inversion accuracy. It can provide a precise migration velocity model for long-range exploration and simultaneously promote the development of long-range exploration from quantitative structural interpretation to qualitative physical property interpretation.
[0040] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description and the drawings. Attached Figure Description
[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0042] Figure 1 This diagram illustrates a high-precision waveform inversion method for wellside velocity in remote acoustic logging, as described in an embodiment of the present invention.
[0043] Figure 2 This diagram illustrates the accurate velocity model and the smooth velocity model of the vertical fracture near the well in an embodiment of the present invention.
[0044] Figure 3 This diagram illustrates the velocity inversion results of the 50th and 100th iterations of the vertical model beside the well in an embodiment of the present invention.
[0045] Figure 4 This diagram illustrates the layered velocity model and smooth velocity model of well X in an embodiment of the present invention.
[0046] Figure 5 This diagram illustrates the full waveform inversion results of well X at different iteration numbers in an embodiment of the present invention.
[0047] Figure 6 This diagram illustrates a common offset gather of simulated and observed data from well X in an embodiment of the present invention.
[0048] Figure 7 This diagram illustrates a high-precision waveform inversion system for well-side velocity in remote acoustic logging, as shown in an embodiment of the present invention.
[0049] Figure 8 A diagram of an electronic device according to an embodiment of the present invention is shown. Detailed Implementation
[0050] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this disclosure more comprehensive and complete, and to fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more of the specific details omitted, or other methods, components, apparatus, steps, etc., can be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of this disclosure.
[0051] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware units or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.
[0052] The flowchart shown in the attached diagram is merely an illustrative example and does not necessarily include all steps. For example, some steps may be broken down, while others may be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.
[0053] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein.
[0054] Furthermore, the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, such that a process, method, system, product, or device that includes a series of steps or sub-modules is not necessarily limited to those steps or sub-modules that are explicitly listed, but may include other steps or sub-modules that are not explicitly listed or that are inherent to such process, method, product, or device.
[0055] like Figure 1 As shown, this invention proposes a high-precision waveform inversion method for well-side velocity retrieval using remote acoustic logging. The method includes the following steps:
[0056] S1. Construct an initial velocity model and input the source wavelet into the initial velocity model to obtain simulation data and forward propagation wave field;
[0057] The initial velocity model is divided into a theoretical model and an actual model;
[0058] The construction of the theoretical model includes the following steps: designing a velocity model based on the development pattern of fractures and cavities near the well; and smoothing the velocity model to obtain the theoretical initial velocity model.
[0059] The construction of the actual model includes the following steps: designing a layered velocity model by lateral interpolation of the time difference curve; smoothing the layered velocity model to obtain the actual initial velocity model; and simulating the propagation process of the wave in the initial velocity model by solving the wave equation to obtain simulation data and the forward propagation wave field.
[0060] S2. Collect remote acoustic logging data and preprocess the remote acoustic logging data;
[0061] Remote acoustic logging data includes reflected wave waveform curves, time difference curves, and gain curves.
[0062] Preprocessing includes: extracting reflected waves, restoring amplitude, and deconvolution processing of the reflected wave curve to obtain actual observation data.
[0063] S3. Obtain the simulation data and calculate the reflected wave residual based on the simulation data and the actual observation data;
[0064] The residual of the reflected wave is obtained based on actual data and simulated data, including: subtracting the actual data from the simulated data; squaring the result of the difference and dividing it by two to obtain the residual of the reflected wave.
[0065] Specifically, the simulation data is obtained by solving the wave equation (1), where v is the velocity model and u is the wave field component. Let δ(xx) represent the partial derivatives in the t, x, and y directions, respectively. s ) indicates the location of the earthquake source, f s (t) represents the source function. The error function is defined by formula (2), where d mod and d obs These represent simulated data and observed data, respectively.
[0066]
[0067] E(m)=(dmod-dobs)2 / 2 (2)
[0068] S4. Obtain the reverse propagation wave field by using the reverse propagation residual of the contrast wave, and construct the velocity gradient by combining it with the forward propagation wave field.
[0069] Constructing the velocity gradient involves: using the reflected wave residual as the source to simulate the propagation process of the wave in the iteratively updated initial velocity model to obtain the reverse propagating wave field, and combining the forward propagating wave field and the cross-correlation gradient to construct the conditions for obtaining the velocity gradient.
[0070] Specifically, the gradient is constructed according to the error functional and the conjugate state method as shown in Equation (3), where g(v) is the velocity gradient, which can be expressed as the derivative of the error functional E(v) with respect to the velocity v. Ru is the wave field received at the detector point, which can be represented as a simulated waveform. * This indicates that a backpropagating sound source is set at the receiver point. Define the forward modeling operator. And L * Let L be the conjugate of the wave field, representing wave field backpropagation. In summary, the gradient g(v) can be defined as the partial derivative of the error function with respect to the model parameters, or it can be described as the second derivative of the forward-modeled wave field with respect to time. Backpropagation L from error data * (R * (Ru-d obs The interrelation of ))
[0071]
[0072] S5. Iteratively update the initial velocity model based on the velocity gradient to obtain the final inversion result.
[0073] Specifically, the speed is iteratively updated based on different optimization methods, with the main differences being in step size setting, gradient direction search, and convergence speed.
[0074] Combination Figures 2-6 The specific embodiments of the present invention will be further described in detail below.
[0075] (1) Collection of simulated or measured data from remote sensing
[0076] The actual data from long-range detection requires data collection and preprocessing. Data preprocessing includes gain recovery and time difference extraction, while data processing includes using FK filtering to extract reflected waves, using inverse Q filtering to recover amplitude, and using deconvolution to improve data resolution.
[0077] (2) Design the theoretical initial velocity model and the actual initial velocity model
[0078] For the theoretical initial velocity model, the simulation data velocity model can be set according to the common development patterns of well-side fractured-cavity reflectors, such as... Figure 2 The vertical crack model shown in the image, after smoothing, can be used as the initial velocity model for waveform inversion. For the actual initial velocity model, a horizontal velocity model can be established based on the time difference curve, and then smoothed as the initial velocity model for waveform inversion, such as... Figure 4As shown.
[0079] (3) Velocity gradient construction and iterative update
[0080] Figure 3 The results of velocity inversion for the 50th and 100th iterations of the vertical fracture model near the well are shown. It can be seen that as the number of iterations increases, the vertical fracture velocity gradually converges from a low-frequency smooth velocity to a high-frequency precise velocity. Figure 5 The waveform inversion results of Well X with different iteration numbers are shown. From left to right, they are 10 iterations, 20 iterations, 30 iterations and 40 iterations. As the number of iterations increases, the waveform inversion technique gradually depicts a low-velocity slot reflector from the smooth background velocity model, as shown by the circle in the figure. Figure 6 The shared offset gather of simulated and observed data from Well X is shown. From left to right, the data consists of the initial model data, the model data from 40 iterations, and the observed data. Since the simulated data comes from the forward modeling of the velocity inversion results, it can be indirectly verified that the velocity model gradually approaches the actual formation velocity as the number of iterations increases. This confirms that the remote sounding acoustic logging waveform inversion technology can effectively achieve high-precision inversion of wellside velocity.
[0081] Using the remote acoustic logging data of well X tested with this invention, it can be seen that as the number of iterations increases, the remote acoustic logging waveform inversion technology gradually delineates low-velocity fracture-cavity reflectors from the smooth background velocity, indicating that this technology can effectively realize well-side velocity inversion in remote acoustic logging.
[0082] like Figure 7 As shown, the present invention also proposes a high-precision waveform inversion system for well-side velocity in remote acoustic logging, the system comprising a data acquisition module 10, a data collection module 20, a data calculation module 30, a gradient construction module 40, and an iterative inversion module 50;
[0083] The data acquisition module 10 is used to construct an initial velocity model. The source wavelet is input into the initial velocity model to perform time-forward simulation to obtain simulation data and forward propagation wave field.
[0084] Data collection module 20 is used to collect remote sounding logging data and preprocess the remote sounding logging data to obtain actual observation data;
[0085] The data calculation module 30 is used to acquire simulation data and calculate the reflected wave residual based on the simulation data and actual observation data;
[0086] The gradient construction module 40 is used to obtain the reverse propagation wave field by using the reverse propagation residual of the contrast wave, and to construct the velocity gradient by combining it with the forward propagation wave field.
[0087] The iterative inversion module 50 is used to iteratively update the initial velocity model based on the velocity gradient to obtain the final inversion result.
[0088] like Figure 8 As shown, corresponding to the high-precision waveform inversion method for well-side velocity in remote acoustic logging provided above, this invention also provides an electronic device. Since the embodiment of this device is similar to the above method embodiment, the description is relatively simple; relevant details can be found in the description of the above method embodiment section. The device described below is merely illustrative. This device may include: a processor 1, a memory 2, a communication bus (i.e., the aforementioned device bus), and a lookup engine. The processor 1 and memory 2 communicate with each other via the communication bus and communicate with external systems via a communication interface. The processor 1 can call logical instructions in the memory 2 to execute the high-precision waveform inversion method for well-side velocity in remote acoustic logging.
[0089] Furthermore, the logical instructions in the aforementioned memory 2 can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as memory chips, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0090] On the other hand, embodiments of the present invention also provide a processor-readable storage medium storing a computer program 3, which, when executed by a processor 1, implements the high-precision waveform inversion method for remote sounding logging velocity along the wellbore provided in the above embodiments.
[0091] The processor-readable storage medium can be any available medium or data storage device that the processor 1 can access, including but not limited to magnetic memory (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO)), optical memory (e.g., CD, DVD, BD, HVD), and semiconductor memory (e.g., ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)).
[0092] Those skilled in the art should understand that, despite the detailed description of the present invention with reference to the foregoing embodiments, modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A high-precision waveform inversion method for well-side velocity retrieval using long-range acoustic logging, characterized in that, The method includes: An initial velocity model is constructed, and the source wavelet is input into the initial velocity model to perform a time-forward simulation to obtain simulation data and the forward propagation wave field; Collect remote acoustic logging data and preprocess the remote acoustic logging data to obtain actual observation data; the remote acoustic logging data includes reflected wave waveform curves, time difference curves, and gain curves; the preprocessing includes: reflecting wave extraction, amplitude recovery, and deconvolution processing of the reflected wave curves; The residual of the reflected wave is obtained by calculating the difference between the actual data and the actual observation data, including: subtracting the actual data from the simulated data; squaring the difference result and dividing it by two to obtain the residual of the reflected wave. The reflected wave residual is input into the initial velocity model for time-reverse simulation to obtain the reverse propagation wave field, and the velocity gradient is constructed by combining it with the forward propagation wave field. The initial velocity model is divided into a theoretical model and an actual model. The construction of the theoretical model includes the following steps: designing a velocity model based on the development pattern of the fracture-cavity near the well; smoothing the velocity model to obtain the theoretical initial velocity model. The construction of the actual model includes the following steps: designing a layered velocity model by lateral interpolation of the time difference curve; smoothing the layered velocity model to obtain the actual initial velocity model. The initial velocity model is iteratively updated based on the velocity gradient to obtain the final inversion result.
2. The high-precision waveform inversion method for well-side velocity retrieval using long-range acoustic logging according to claim 1, characterized in that, The construction velocity gradient includes: The reflected wave residual is used as the source to simulate the propagation process of the wave in the iteratively updated initial velocity model to obtain the reverse propagation wave field. The velocity gradient is obtained by combining the forward propagation wave field and the cross-correlation gradient.
3. A high-precision waveform inversion system for well-side velocity retrieval using long-range acoustic logging, characterized in that, The system includes a data acquisition module, a data collection module, a data calculation module, a gradient construction module, and an iterative inversion module; The data acquisition module is used to construct the initial velocity model. The source wavelet is input into the initial velocity model to perform time-forward simulation to obtain simulation data and forward propagation wave field. The data collection module is used to collect remote acoustic logging data and preprocess the remote acoustic logging data to obtain actual observation data; The remote acoustic logging data includes reflected wave waveform curves, time difference curves, and gain curves; the preprocessing includes: reflecting wave extraction, amplitude recovery, and deconvolution processing of the reflected wave curves; The data calculation module is used to acquire simulated data and calculate the reflected wave residual based on the simulated data and actual observation data. This includes: the data calculation module subtracts the actual data from the simulated data; the squared result of the subtraction is then divided by two to obtain the reflected wave residual. The gradient construction module is used to obtain the back-propagating wavefield using the residual back propagation of the contrast wave, and to construct the velocity gradient by combining it with the forward propagating wavefield. The initial velocity model is divided into a theoretical model and an actual model. The construction of the theoretical model includes the following steps: designing a velocity model based on the development pattern of the fracture-cavity near the well; smoothing the velocity model to obtain the theoretical initial velocity model. The construction of the actual model includes the following steps: designing a layered velocity model by performing lateral interpolation on the time difference curve; smoothing the layered velocity model to obtain the actual initial velocity model. The iterative inversion module is used to iteratively update the initial velocity model based on the velocity gradient to obtain the final inversion result.
4. An electronic device, characterized in that, include: Processor and memory; The processor calls the computer program stored in the memory to execute the high-precision waveform inversion method for well-side velocity in remote acoustic logging according to any one of claims 1 to 2.
5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the processor to perform the high-precision waveform inversion method for well-side velocity in remote acoustic logging as described in any one of claims 1 to 2.