High-precision waveform inversion method and system for near-well velocity of far exploration acoustic logging

Abstract

The invention belongs to the technical field of remote detection acoustic logging speed inversion, and particularly relates to a remote detection acoustic logging well-side speed high-precision waveform inversion method and system. The method comprises the following steps: constructing an initial speed model, and inputting a seismic source wavelet into the initial speed model to carry out time forward simulation so as to obtain simulation data and a forward transmission wave field; collecting remote detection acoustic logging data and preprocessing the remote detection acoustic logging data to obtain actual observation data; calculating a reflected wave residual error according to the simulation data and the actual observation data; and inputting the reflected wave residual into the initial velocity model to perform time reverse simulation so as to obtain a back propagation wave field, and constructing a velocity gradient in combination with the forward propagation wave field. According to the method, error minimization of observation data and simulation data is sought under a least square optimization theory framework, the method has extremely high speed inversion precision, an accurate migration speed model can be provided for remote exploration, and meanwhile, the development process of remote exploration from quantitative structure interpretation to qualitative physical property interpretation is promoted.

Description

Technical Field

[0001] The invention belongs to the technical field of remote detection sonic logging velocity inversion, and in particular relates to a remote detection sonic logging wellside velocity high-precision waveform inversion method and system. Background Art

[0002] Remote detection acoustic logging is a new generation of acoustic logging technology that uses seismic exploration data processing technology to process acoustic logging data. When fracture-cave reflectors are developed near the well, the sound waves stimulated by the remote detection acoustic logging instrument will generate reflection waves at the reflectors, which are then received and recorded by the instrument after being transmitted back to the wellbore. The imaging profile of the reflector is obtained through data processing, and then the fracture-cave reservoir outside the wellbore is identified and evaluated. Remote detection acoustic logging combines the advantages of high vertical detection resolution of acoustic logging and long depth of seismic detection, breaking through the resolution limit of seismic exploration, and extending the detection distance of array acoustic logging from three meters near the well to about 40 meters near the well (XMAC-F1 instrument dipole sound source observation). It is an important tool for effectively identifying small-scale hidden reservoirs and achieving increased reserves and production in today's complex exploration background. Similar to seismic exploration, the data processing process of remote detection acoustic logging includes reflection wave processing and offset imaging.

[0003] According to different theoretical foundations, remote detection migration imaging can be divided into ray migration and wave equation migration, among which ray migration is represented by Kirchhoff migration and Gaussian beam migration, while common wave migration includes one-way wave migration and reverse time migration. The early developed Kirchhoff migration and one-way wave migration have been widely used in the actual data processing of remote detection acoustic logging due to their high processing efficiency. However, due to their approximate theoretical basis, they cannot adapt to the strong vertical velocity changes under the logging observation system and effectively realize the migration and relocation of low-angle fractures. Reverse time migration imaging based on the full wave equation has higher accuracy and can realize the migration and relocation of complex wave fields such as rotation waves and prismatic waves. However, studies have shown that high-precision migration imaging algorithms require higher accuracy of velocity models, and there is always a research gap in remote detection acoustic logging velocity modeling. The common processing method is to use a uniform velocity model or time difference curve interpolation. Summary of the invention

[0004] In view of the above problems, the present invention provides a method for high-precision waveform inversion of wellbore velocity in remote detection acoustic logging, the method comprising:

[0005] Construct an initial velocity model, input the source wavelet into the initial velocity model for time forward simulation to obtain simulation data and forward wave field;

[0006] Collecting remote detection sonic logging data and preprocessing the remote detection sonic logging data to obtain actual observation data;

[0007] Obtain the reflected wave residual based on the simulated data and the actual observed data;

[0008] The reflected wave residual is input into the initial velocity model for time-reverse simulation to obtain the reverse wave field, and the velocity gradient is constructed in combination with the forward wave field.

[0009] The initial velocity model is iteratively updated according to the velocity gradient to obtain the final inversion result.

[0010] Preferably, the remote detection acoustic logging data includes a reflection wave waveform curve, a time difference curve and a gain curve.

[0011] Preferably, the preprocessing includes: performing reflection wave extraction, amplitude recovery and deconvolution processing on the reflection wave curve.

[0012] Preferably, the initial velocity model is divided into a theoretical model and a practical model.

[0013] Preferably, the construction of the theoretical model comprises the following steps:

[0014] Design a velocity model based on the development pattern of fractures and caves near the well;

[0015] The velocity model is smoothed to obtain the theoretical initial velocity model.

[0016] Preferably, the construction of the actual model comprises the following steps:

[0017] Designing a layered velocity model by lateral interpolation of the time difference curves;

[0018] The layered velocity model is smoothed to obtain the actual initial velocity model.

[0019] Preferably, obtaining the reflected wave residual according to the actual data and the simulated data includes:

[0020] Subtract the actual data from the simulated data;

[0021] The difference result is squared and divided by two to obtain the reflected wave residual.

[0022] Preferably, the constructing the velocity gradient comprises:

[0023] The reflected wave residual is used as the source to simulate the wave propagation process in the iteratively updated initial velocity model to obtain the back propagation wave field, and the forward wave field and the cross-correlation gradient construction condition are combined to obtain the velocity gradient.

[0024] The present invention also proposes a high-precision waveform inversion system for long-range acoustic logging wellbore velocity, 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 an initial velocity model, input the source wavelet into the initial velocity model for time forward simulation to obtain simulation data and forward wave field;

[0026] The data collection module is used to collect remote detection sonic logging data and pre-process the remote detection sonic logging data to obtain actual observation data;

[0027] The data calculation module is used to obtain simulation data and obtain the reflection wave residual based on the simulation data and actual observation data;

[0028] The gradient construction module is used to obtain the reverse propagation wave field by using the residual of the contrast wave, and to construct the velocity gradient by combining the forward propagation wave field;

[0029] The iterative inversion module is used to iteratively update the initial velocity model according to the velocity gradient to obtain the final inversion result.

[0030] Preferably, the data calculation module obtains the reflected wave residual according to the actual data and the simulated data, including:

[0031] The data calculation module makes a difference between the actual data and the simulated data;

[0032] The difference result is squared and divided by two to obtain the reflected wave residual.

[0033] The present invention provides an electronic device, comprising:

[0034] Processor and memory;

[0035] The processor calls the computer program stored in the memory to execute the high-precision waveform inversion method for borehole velocity in long-range acoustic logging according to any one of the above items.

[0036] The present invention provides a computer-readable storage medium.

[0037] The computer readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor is enabled to execute any one of the above-mentioned methods for high-precision waveform inversion of wellbore velocity of remote detection acoustic logging.

[0038] The present invention has the following beneficial effects:

[0039] The present invention seeks to minimize the error between observed data and simulated data under the framework of least squares optimization theory, has extremely high velocity inversion accuracy, can provide an accurate migration velocity model for remote detection, and at the same time promotes the development of remote detection from quantitative structural interpretation to qualitative physical property interpretation.

[0040] Other features and advantages of the present invention will be described in the following description, and partly become obvious from the description, or be understood by implementing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 A diagram showing a method for high-precision waveform inversion of wellbore velocity in remote detection acoustic logging according to an embodiment of the present invention;

[0043] Figure 2 A schematic diagram showing an accurate velocity model and a smooth velocity model of a vertical fracture near a well in an embodiment of the present invention is shown;

[0044] Figure 3 A schematic diagram showing the 50th iteration and the 100th velocity inversion result of the wellside vertical model in an embodiment of the present invention is shown;

[0045] Figure 4 A schematic diagram of a layered velocity model and a smoothed velocity model of Well X in an embodiment of the present invention is shown;

[0046] Figure 5 A schematic diagram showing full waveform inversion results of Well X with different iteration times in an embodiment of the present invention is shown;

[0047] Figure 6 A schematic diagram of a common offset gather of simulated data and observed data of Well X in an embodiment of the present invention is shown;

[0048] Figure 7 A diagram showing a high-precision waveform inversion system for long-range acoustic logging wellbore velocity in an embodiment of the present invention is shown;

[0049] Figure 8 A diagram of an electronic device in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0050] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as being limited to the examples set forth herein; on the contrary, these embodiments are provided so that the present disclosure will be more comprehensive and complete, and the concepts of the example embodiments are fully conveyed to those skilled in the art. The described features, structures, or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known technical solutions are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0051] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware units or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0052] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the steps. For example, some steps may be decomposed, while some steps may be combined or partially combined, so the actual execution order may change according to the actual situation.

[0053] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein, for example.

[0054] In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or sub-modules is not necessarily limited to those steps or sub-modules explicitly listed, but may include other steps or sub-modules not explicitly listed or inherent to these processes, methods, products, or apparatuses.

[0055] like Figure 1 As shown, the present invention proposes a high-precision waveform inversion method for long-range acoustic logging wellbore velocity, the method comprising the following steps:

[0056] S1, constructing an initial velocity model, inputting the source wavelet into the initial velocity model to obtain simulation data and forward wave field;

[0057] The initial velocity model is divided into theoretical model and practical model;

[0058] The construction of the theoretical model includes the following steps: designing a velocity model according to the development pattern of fractures and caves near the well; smoothing the velocity model to obtain a 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 solving the wave equation to simulate the propagation process of the wave in the initial velocity model to obtain simulation data and forward wave field.

[0060] S2, collecting remote detection sonic logging data and preprocessing the remote detection sonic logging data;

[0061] Remote detection acoustic logging data include reflection wave waveform curve, time difference curve and gain curve.

[0062] The preprocessing includes: reflection wave extraction, amplitude recovery and deconvolution processing on the reflection wave curve to obtain the actual observation data.

[0063] S3, obtaining simulation data, and calculating the reflected wave residual according to the simulation data and the actual observation data;

[0064] Obtaining the reflected wave residual according to the actual data and the simulated data includes: subtracting the actual data from the simulated data; squaring the subtraction result and dividing it by two to obtain the reflected wave residual.

[0065] Specifically, the wave equation (1) is solved to obtain simulation data, where v is the velocity model, u is the wave field component, They represent the partial derivatives in the t direction, x direction and y direction respectively, δ(xx s ) represents the earthquake source location, f s (t) represents the source function. The error function is defined as formula (2), where d mod and d obs represent simulated data and observed data, respectively.

[0066]

[0067] E(m)=(dmod-dobs)2 / 2 (2)

[0068] S4, using the contrast wave residual to back propagate to obtain the back propagation wave field, and combining it with the forward propagation wave field to construct the velocity gradient;

[0069] Constructing a velocity gradient includes: using the reflection wave residual as a seismic source to simulate the wave propagation process in the iteratively updated initial velocity model to obtain a reverse wave field, and combining the forward wave field and the cross-correlation gradient construction condition to obtain a velocity gradient.

[0070] Specifically, the gradient is constructed according to the error functional and the conjugate state method as shown in formula (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 by the detector point, which can be expressed as a simulated waveform, R * Indicates setting the back propagation sound source at the detection point. Define the forward modeling operator And L * is the conjugate device of L, which represents the wavefield back propagation. In summary, the gradient g(v) can be defined as the partial derivative of the error function with respect to the model parameters, and can also be described as the second-order derivative of the forward wavefield with respect to time. Back propagation with error data L * (R * (Ru-d obs ))'s cross-correlation.

[0071]

[0072] S5. Iteratively update the initial velocity model according to the velocity gradient to obtain the final inversion result.

[0073] Specifically, the speed is iteratively updated based on different optimization methods, and the main differences are reflected in the step size setting, gradient direction search and convergence speed.

[0074] Combination Figure 2-Figure 6 , the specific implementation modes of the present invention are further described in detail.

[0075] (1) Remote detection simulation or actual measurement data collection

[0076] Actual data from long-range detection requires data collection and preprocessing and processing. Data preprocessing includes gain recovery and time difference extraction, and data processing includes reflection wave extraction using FK filtering, amplitude recovery using inverse Q filtering, and deconvolution to improve data resolution.

[0077] (2) Design theoretical initial velocity model and actual initial velocity model

[0078] For the theoretical initial velocity model, the simulation data velocity model can be set according to the common development mode of fracture-cavity reflectors near the well, such as Figure 2 The vertical fracture model shown in the figure can be smoothed to serve as the initial velocity model for waveform inversion. For the actual initial velocity model, a flat layer velocity model can be established based on the time difference curve and smoothed to serve as the initial velocity model for waveform inversion, such as Figure 4shown.

[0079] (3) Velocity gradient construction and iterative update

[0080] Figure 3 The velocity inversion results of 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 accurate 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 technology gradually depicts a low-velocity fracture-hole reflector from the smooth background velocity model, as shown by the circle in the figure. Figure 6 The co-offset gathers of the simulated data and observed data of Well X are displayed. From left to right, they are the initial model data, the 40th iteration model data, and the observed data. Since the simulated data come from the forward simulation of the velocity inversion results, it can be verified from the side that the velocity model gradually approaches the actual formation velocity as the number of iterations increases, proving that the long-range acoustic logging waveform inversion technology can effectively achieve high-precision inversion of wellside velocity.

[0081] By testing the X-well long-range acoustic logging data using the present invention, it can be seen that with the increase in the number of iterations, the long-range acoustic logging waveform inversion technology gradually depicts low-velocity fracture-cavity reflectors from the smooth background velocity, indicating that the technology can effectively realize the wellside velocity inversion in long-range acoustic logging.

[0082] like Figure 7 As shown, the present invention also proposes a high-precision waveform inversion system for long-range acoustic logging wellbore velocity, 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, input the source wavelet into the initial velocity model for time forward simulation to obtain simulation data and forward wave field;

[0084] The data collection module 20 is used to collect remote detection sonic logging data and pre-process the remote detection sonic logging data to obtain actual observation data;

[0085] The data calculation module 30 is used to obtain simulation data and obtain the reflected wave residual according to the simulation data and the actual observation data;

[0086] The gradient construction module 40 is used to obtain the reverse propagation wave field by using the contrast wave residual to back propagate, and to construct the velocity gradient in combination with the forward propagation wave field;

[0087] The iterative inversion module 50 is used to iteratively update the initial velocity model according to the velocity gradient to obtain the final inversion result.

[0088] like Figure 8 As shown, corresponding to the high-precision waveform inversion method for borehole velocity of remote detection acoustic logging provided above, the present invention also provides an electronic device. Since the embodiment of the device is similar to the above method embodiment, the description is relatively simple. For the relevant parts, please refer to the description of the above method embodiment part. The device described below is only schematic. The device may include: a processor (processor) 1, a memory (memory) 2 and a communication bus (i.e., the above device bus) and a search engine, wherein the processor 1 and the memory 2 communicate with each other through the communication bus and communicate with the outside through the communication interface. The processor 1 can call the logic instructions in the memory 2 to execute the high-precision waveform inversion method for borehole velocity of remote detection acoustic logging.

[0089] In addition, the logic instructions in the above-mentioned memory 2 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: a storage chip, a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk, and other media that can store program codes.

[0090] On the other hand, an embodiment of the present invention further provides a processor-readable storage medium, on which a computer program 3 is stored. When the computer program 3 is executed by the processor 1, it is implemented to execute the high-precision waveform inversion method for long-range acoustic logging wellbore velocity provided in the above-mentioned embodiments.

[0091] The processor-readable storage medium can be any available medium or data storage device that can be accessed by the processor 1, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (such as CD, DVD, BD, HVD, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NANDFLASH), solid-state drive (SSD)), etc.

[0092] Those skilled in the art should understand that although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible to modify the technical solutions described in the aforementioned embodiments, or to make equivalent replacements for some of the technical features therein; and these modifications or replacements 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 wellbore velocity in long-range acoustic logging, characterized in that: The method comprises: Construct an initial velocity model, input the source wavelet into the initial velocity model for time forward simulation to obtain simulation data and forward wave field; Collecting remote detection sonic logging data and preprocessing the remote detection sonic logging data to obtain actual observation data; Obtain the reflected wave residual based on the simulated data and the actual observed data; The reflected wave residual is input into the initial velocity model for time-reverse simulation to obtain the reverse wave field, and the velocity gradient is constructed in combination with the forward wave field. The initial velocity model is iteratively updated according to the velocity gradient to obtain the final inversion result.

2. The high-precision waveform inversion method for long-range acoustic logging wellbore velocity according to claim 1 is characterized in that: The remote detection acoustic logging data includes a reflection wave waveform curve, a time difference curve and a gain curve.

3. The high-precision waveform inversion method for long-range acoustic logging wellbore velocity according to claim 2 is characterized in that: The preprocessing includes: performing reflection wave extraction, amplitude recovery and deconvolution processing on the reflection wave curve.

4. The high-precision waveform inversion method for long-range acoustic logging wellbore velocity according to claim 1 is characterized in that: The initial velocity model is divided into a theoretical model and a practical model.

5. The high-precision waveform inversion method for long-range acoustic logging wellbore velocity according to claim 4 is characterized in that: The construction of the theoretical model includes the following steps: Design a velocity model based on the development pattern of fractures and caves near the well; The velocity model is smoothed to obtain the theoretical initial velocity model.

6. The high-precision waveform inversion method for long-range acoustic logging wellbore velocity according to claim 4 is characterized in that: The construction of the actual model includes the following steps: Designing a layered velocity model by lateral interpolation of the time difference curves; The layered velocity model is smoothed to obtain the actual initial velocity model.

7. The high-precision waveform inversion method for long-range acoustic logging wellbore velocity according to claim 1 is characterized in that: The step of obtaining the reflected wave residual according to the actual data and the simulated data includes: Subtract the actual data from the simulated data; The difference result is squared and divided by two to obtain the reflected wave residual.

8. The high-precision waveform inversion method for wellbore velocity in remote acoustic logging according to claim 1 is characterized in that: The constructing speed gradient comprises: The reflected wave residual is used as the source to simulate the wave propagation process in the iteratively updated initial velocity model to obtain the back propagation wave field, and the forward wave field and the cross-correlation gradient construction condition are combined to obtain the velocity gradient.

9. A high-precision waveform inversion system for long-range acoustic logging wellbore velocity, 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 an initial velocity model, input the source wavelet into the initial velocity model for time forward simulation to obtain simulation data and forward wave field; The data collection module is used to collect remote detection sonic logging data and pre-process the remote detection sonic logging data to obtain actual observation data; The data calculation module is used to obtain simulation data and obtain the reflection wave residual based on the simulation data and actual observation data; The gradient construction module is used to obtain the reverse propagation wave field by using the residual of the contrast wave, and to construct the velocity gradient by combining the forward propagation wave field; The iterative inversion module is used to iteratively update the initial velocity model according to the velocity gradient to obtain the final inversion result.

10. The high-precision waveform inversion system for long-range acoustic logging wellbore velocity according to claim 9, characterized in that: The data calculation module obtains the reflected wave residual according to the actual data and the simulated data, including: The data calculation module makes a difference between the actual data and the simulated data; The difference result is squared and divided by two to obtain the reflected wave residual.

11. 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 borehole velocity of long-range acoustic logging according to any one of claims 1 to 8.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor is enabled to execute the high-precision waveform inversion method for borehole velocity in long-range acoustic logging as described in any one of claims 1 to 8.

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