Far exploration acoustic logging high-precision iteration reverse time migration method and system

Abstract

The invention belongs to the technical field of remote detection acoustic logging migration imaging, and particularly relates to a high-precision iteration reverse time migration method and system for remote detection acoustic logging. The method comprises the following steps: collecting remote detection acoustic logging data, and processing the remote detection acoustic logging data to obtain reflected wave actual measurement data; performing remote detection reverse time migration processing on the reflected wave actual measurement data to obtain an initial imaging result; establishing a wave equation based on the initial imaging result, and solving the wave equation to obtain reflected wave prediction data; obtaining residual data according to the reflected wave prediction data and the reflected wave actual measurement data; and carrying out wave field back propagation by taking the residual data as a geophone source, constructing an imaging gradient and obtaining an imaging result. According to the method, the reverse time migration is used for replacing Kirchhoff migration and F-K migration commonly used in remote exploration acoustic logging actual data processing, vertical speed change in a logging observation system can be better adapted, and low-dip-angle fracture-cavity migration homing is achieved.

Description

Technical Field

[0001] The invention belongs to the technical field of long-range acoustic logging offset imaging, and in particular relates to a long-range acoustic logging high-precision iterative reverse time migration method and system. Background Art

[0002] Seismic exploration detects stratigraphic structure and reservoir information by stimulating and receiving underground reflected signals on the surface. It has the advantages of wide coverage and deep detection depth, but its detection resolution is limited by the main frequency of the data. Array acoustic logging uses instruments in the well to stimulate and receive sliding waves propagating along the well wall to identify reservoirs next to the well wall. It has ultra-high vertical resolution, but is limited by the shallow detection depth of sliding waves, and there is always an exploration gap between it and seismic technology. The emergence of long-range detection acoustic logging technology has effectively filled this gap. It is an important tool for identifying small-scale hidden reservoirs and achieving increased reserves and production. It applies seismic data processing technology to acoustic logging technology. The main process includes two steps: reflection wave extraction and offset imaging.

[0003] Reverse time migration imaging is a high-precision migration imaging technology developed in recent years in seismic exploration. It realizes the forward and reverse extension of the wave field by numerically solving the full wave equation, and realizes the imaging of the reflector based on imaging conditions such as cross-correlation. Compared with ray theory, wave theory is more adaptable to vertical velocity changes, and can avoid the problem that one-way waves cannot return the return wave offset. Therefore, compared with Kirchhoff migration and FK migration commonly used in actual data processing of long-range detection sonic logging, reverse time migration has higher imaging accuracy. Studies have shown that the energy of the reflected signal is much weaker than the wellbore mode wave (about two orders of magnitude), and due to the limitation of instrument size, the long-range detection offset distance is usually short, resulting in weak imaging energy and low resolution in deep formations. Summary of the invention

[0004] In view of the above problems, the present invention provides a high-precision iterative reverse time migration method for long-range acoustic logging, the method comprising:

[0005] Collect and process the remote detection sonic logging data to obtain the reflected wave measured data;

[0006] Perform remote detection reverse time migration processing on the reflected wave measured data to obtain the initial imaging results;

[0007] A wave equation is established based on the initial imaging results, and the wave equation is solved to obtain the reflection wave prediction data;

[0008] Obtain residual data according to the reflection wave prediction data and the reflection wave measured data;

[0009] Use the residual data as the source of the detection point to perform wavefield backpropagation, construct imaging gradients and obtain imaging results;

[0010] The imaging results are iteratively optimized and the optimized imaging results are output.

[0011] Preferably, the remote detection acoustic logging data includes waveform data, gain and arrival time curve data.

[0012] Preferably, the processing of the remote detection sonic logging data comprises:

[0013] Extract the measured data of reflected waves through frequency wavenumber domain filtering;

[0014] The amplitude of the reflected wave measured data is restored through attenuation compensation;

[0015] Multiples are suppressed by predictive deconvolution.

[0016] Preferably, performing remote detection reverse time migration processing on the reflected wave measured data to obtain the initial imaging result includes:

[0017] The reflection wave extraction results are combined with the velocity model and simulated source to obtain the forward and reverse wave fields, and the initial imaging results are obtained based on the cross-correlation imaging conditions.

[0018] Preferably, establishing a wave equation based on the initial imaging result and solving the wave equation to obtain reflected wave prediction data comprises:

[0019] The initial imaging results are combined with the velocity model and forward wave field to establish the wave equation, which is then solved to obtain the reflection wave prediction data.

[0020] Preferably, obtaining residual data according to the reflected wave prediction data and the reflected wave measured data includes:

[0021] The predicted reflected wave data is subtracted from the measured reflected wave data to obtain the residual data.

[0022] Preferably, the constructing of the imaging gradient comprises:

[0023] The forward propagation of source wavelet and the reverse propagation of residual data are used to obtain the forward and reverse wave fields respectively, and the imaging gradient is obtained by combining the cross-correlation imaging condition.

[0024] The present invention also proposes a high-precision iterative reverse time migration system for long-range acoustic logging, the system comprising a data collection module, a data processing module, a data solution module, a calculation module, an imaging acquisition module and an optimization module;

[0025] The data collection module is used to collect and process the remote detection acoustic wave logging data to obtain the reflected wave measured data;

[0026] The data processing module is used to perform remote detection reverse time migration processing on the reflected wave measured data to obtain the initial imaging results;

[0027] The data solution module establishes a wave equation based on the initial imaging results and solves the wave equation to obtain the reflection wave prediction data;

[0028] The calculation module is used to obtain residual data according to the reflection wave prediction data and the reflection wave measured data;

[0029] The imaging acquisition module is used to use the residual data as the detection point source to perform wave field backpropagation, construct imaging gradients and obtain imaging results;

[0030] The optimization module is used to iteratively optimize the imaging results and output the optimized imaging results.

[0031] Preferably, the data collection module is used to process the remote detection acoustic logging data, including:

[0032] The data collection module extracts the reflected wave measured data through frequency wavenumber domain filtering;

[0033] The amplitude of the reflected wave measured data is restored through attenuation compensation.

[0034] Preferably, the solution module is used to perform remote detection reverse time migration processing on the reflected wave measured data to obtain an initial imaging result, including:

[0035] The reflection wave extraction results are combined with the velocity model and simulated source to obtain the forward and reverse wave fields, and the initial imaging results are obtained based on the cross-correlation imaging conditions.

[0036] Preferably, the calculation module is used to obtain residual data according to the reflected wave prediction data and the reflected wave measured data, including:

[0037] The calculation module subtracts the reflected wave prediction data from the reflected wave measured data to obtain residual data.

[0038] The present invention also provides an electronic device, comprising:

[0039] Processor and memory;

[0040] The processor calls the computer program stored in the memory to execute any one of the above-mentioned high-precision iterative reverse time migration methods for long-range acoustic logging.

[0041] The present invention also provides a computer-readable storage medium.

[0042] 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 high-precision iterative reverse time migration methods for long-range acoustic logging.

[0043] The present invention has the following beneficial effects:

[0044] (1) The present invention uses reverse time migration to replace Kirchhoff migration and FK migration commonly used in actual data processing of remote sound wave logging, which can better adapt to the vertical speed change in the logging observation system and realize the migration homing of low-angle fractures and caves;

[0045] (2) The present invention effectively improves the imaging amplitude and resolution of deep formations through iterative correction.

[0046] 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

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces 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 creative work.

[0048] Figure 1 A diagram showing a high-precision iterative reverse time migration method for long-range acoustic logging according to an embodiment of the present invention;

[0049] Figure 2 A flowchart of iterative reverse time migration of long-range acoustic logging in an embodiment of the present invention is shown;

[0050] Figure 3 A vertical fracture model and a smooth velocity model diagram beside a well in an embodiment of the present invention are shown;

[0051] Figure 4 It shows the reverse time migration result diagram of different iteration times of the vertical fracture model beside the well in the embodiment of the present invention;

[0052] Figure 5 It shows the X-well iterative reverse time migration velocity model and smoothed velocity model diagram in an embodiment of the present invention;

[0053] Figure 6 The least squares reverse time migration imaging result diagram of Well X with different iteration times in the embodiment of the present invention is shown;

[0054] Figure 7 A diagram showing a high-precision iterative reverse time migration system for long-range acoustic logging in an embodiment of the present invention is shown;

[0055] Figure 8A diagram of an electronic device in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] In order to further improve the accuracy of long-range detection imaging and improve the imaging effect of deep formations, the present invention proposes a high-precision iterative reverse time migration method and system for long-range detection acoustic logging, which uses the reverse time migration result as the initial value and adopts optimization methods such as conjugate gradient to iteratively update the imaging result, effectively improving the resolution of long-range detection migration imaging and improving the imaging effect of deep fractures and caves.

[0062] like Figure 1 As shown, the present invention provides a high-precision iterative reverse time migration method for long-range acoustic logging, the method comprising the following steps:

[0063] S1. Collecting and processing the long-range acoustic logging data to obtain reflected wave measured data;

[0064] Remote detection sonic logging data includes waveform data, gain and arrival time curve data.

[0065] Processing of remote acoustic logging data includes: extracting reflected wave measured data through frequency wavenumber domain filtering (FK filtering); recovering the amplitude of reflected wave measured data through attenuation compensation, where attenuation compensation often uses inverse Q filtering; suppressing multiple waves through predictive deconvolution to improve the resolution of reflected waves.

[0066] Attached Figure 1 The workflow of iterative reverse time migration of long-range acoustic logging is demonstrated, which generally includes three steps: iterative reverse time migration, iterative reverse time migration and iterative update of imaging results. First, it is necessary to collect the measured data or simulated data of long-range acoustic logging as the data basis for reverse time migration and iterative reverse time migration. The measured data also needs to be preprocessed, including time difference extraction and waveform recovery, as well as reflection wave processing, including reflection wave extraction, amplitude recovery and deconvolution.

[0067] S2, performing remote detection reverse time migration processing on the reflected wave measured data to obtain the initial imaging result;

[0068] It includes: combining the reflection wave extraction results with the velocity model and simulated earthquake source to obtain the forward and reverse wave fields, and obtaining the initial imaging results based on the cross-correlation imaging conditions.

[0069] Specifically, firstly, the far-sounding reverse time migration is used as the initial value of the iterative reverse time migration imaging result. Formula (1) shows the initial imaging result obtained by reverse time migration, where f(x, t; x s ) represents the source function, x, t represent the spatial and temporal variables respectively, x s represents the sound source position, v0 represents the velocity model, represents the Laplace operator; d O (x r ,t;xs ) represents the observed data, x r Indicates the location of the detection point; and represent the forward wave field and the reverse wave field respectively, m0(x; x s ) represents the initial imaging result obtained by the cross-correlation imaging condition.

[0070]

[0071] S3, establishing a wave equation based on the initial imaging result, and solving the wave equation to obtain reflection wave prediction data;

[0072] A wave equation is established based on the initial imaging result, including: combining the initial imaging result with a velocity model and a forward wave field to establish the wave equation.

[0073] Specifically, the source function is constructed using the initial imaging results, velocity model, and the forward wave field in the previous step. And according to the wave equation (2) we get the predicted data d P (x r ,t;x s ).

[0074]

[0075] S4, obtaining residual data according to the reflection wave prediction data and the reflection wave measured data;

[0076] It includes: subtracting the reflected wave predicted data from the reflected wave measured data to obtain residual data.

[0077] S5, using the residual data as the source of the detection point to perform wave field back propagation, construct imaging gradients and obtain imaging results;

[0078] The imaging gradient is constructed, including: forward propagation of source wavelet and reverse propagation of residual data to obtain forward and reverse wave fields respectively, and combining the cross-correlation imaging conditions to obtain the imaging gradient, that is, the iterative update direction of the imaging result.

[0079] Specifically, the residual data δd(x r ,t;x s ) can be expressed as the predicted data d P (x r ,t;x s ) and the observed data d O (x r ,t;x s ). Using the residual data as the detection point sound source for wave field backpropagation, we can obtain the reverse propagation wave field p R (x,t; x s ), combined with the above forward propagation wave field The imaging result m of the i-th iteration can be obtained according to the cross-correlation imaging condition i (x; x s ), the algorithm can be expressed as formula (3), where represents the cross-correlation imaging condition.

[0080]

[0081] S6. Iteratively optimize the imaging result and output the optimized imaging result.

[0082] Common methods of iterative optimization include gradient method and conjugate gradient method;

[0083] For example, the gradient method includes: using a linear search method to calculate the update step size of each iteration, and the update amount of each iteration can be expressed as the product of the imaging gradient and the update step size.

[0084] Specifically, an optimization method such as the gradient method or the conjugate gradient method is selected to iteratively update the imaging results. The processing flow includes setting up the initial imaging results, obtaining the update step size, and obtaining the update direction based on the imaging gradient.

[0085] Combination Figure 2-Figure 6 , the specific embodiments of the present invention are further described in detail:

[0086] (1) Collection of actual or simulated data from remote acoustic logging.

[0087] like Figure 2 As shown, Figure 2 The workflow of iterative reverse time migration of long-range acoustic logging is demonstrated, which generally includes three steps: iterative reverse time migration, iterative reverse time migration and iterative update of imaging results. First, it is necessary to collect the measured data or simulated data of long-range acoustic logging as the data basis for reverse time migration and iterative reverse time migration. The measured data also needs to be preprocessed, including time difference extraction and waveform recovery, as well as reflection wave processing, including reflection wave extraction, amplitude recovery and deconvolution.

[0088] (2) Migration velocity model design.

[0089] The present invention designs a vertical fracture model near the well and smoothes it as a migration velocity model. Figure 3 As shown; we further selected the measured data of Well X for velocity modeling, and used the time difference curve for lateral difference to obtain the following Figure 5 The horizontal layered velocity model shown in a) is smoothed to construct the actual data migration velocity model.

[0090] (3) Reverse time migration processing as the initial imaging result.

[0091] The reverse time migration imaging results of the vertical fracture model near the well are as follows: Figure 4 As shown in the initial structure of a), it can be seen that the reverse time migration imaging accuracy is high, but the energy balance of shallow and deep fracture imaging is poor, and the arc noise is more serious in the shallow strong reflection section; the reverse time migration results of well X are shown in Figure 6 As shown in the initial imaging result (a), it can be seen that the imaging energy of the reflector is intertwined, and the reflector is almost unrecognizable under a gain background compared with the imaging results after subsequent multiple iterations.

[0092] (4) Iterative update of the reverse time migration imaging results.

[0093] The imaging results of the 30th and 50th iterations of the vertical fracture model near the well are shown in Figure 2. Figure 4 b) The 30th iteration result and Figure 4 As shown in the 50th iteration result in c), the comparison shows that as the number of iterations increases, the deep imaging results are significantly improved, the imaging energy of the three vertical fractures is further balanced, and the arc noise is effectively suppressed; similarly, for the actual data processing results of Well X, as the number of iterations increases, the wellside reflectors gradually stand out, including small-scale cave reflectors in the upper strata, marked with ellipses in the figure, and large-scale fracture reflectors in the lower strata, marked with arrows in the figure. The model data and actual data processing results confirm that the long-range acoustic logging iterative reverse time migration technology can effectively further improve the migration imaging effect under the background of short offset distance and weak signal-to-noise ratio, improve imaging resolution, and suppress imaging noise.

[0094] like Figure 7 As shown, the present invention provides a high-precision iterative reverse time migration system for long-range acoustic logging, the system comprising a data collection module 10, a data processing module 20, a data solution module 30, a calculation module 40, an imaging acquisition module 50 and an optimization module 60;

[0095] The data collection module 10 is used to collect remote detection sonic logging data, and process the remote detection sonic logging data to obtain reflected wave measured data;

[0096] The data processing module 20 is used to perform remote detection reverse time migration processing on the reflected wave measured data to obtain the initial imaging result;

[0097] The data solving module 30 establishes a wave equation based on the initial imaging result, and solves the wave equation to obtain the reflection wave prediction data;

[0098] The calculation module 40 is used to obtain residual data according to the reflected wave prediction data and the reflected wave measured data;

[0099] The imaging acquisition module 50 is used to use the residual data as the detection point source to perform wave field back propagation, construct imaging gradients and obtain imaging results;

[0100] The optimization module 60 is used to iteratively optimize the imaging result and output the optimized imaging result.

[0101] like Figure 8 As shown, corresponding to the high-precision iterative reverse time migration method for long-range acoustic logging provided above, the present invention also provides an electronic device. Since the embodiment of the device is similar to the above-mentioned method embodiment, the description is relatively simple. For relevant parts, please refer to the description of the above-mentioned 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-mentioned 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 iterative reverse time migration method for long-range acoustic logging.

[0102] 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.

[0103] 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 iterative reverse time migration method for long-range acoustic logging provided in the above embodiments.

[0104] 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.

[0105] 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 iterative reverse time migration method for long-range acoustic logging, characterized in that: The method comprises: Collect and process the remote detection sonic logging data to obtain the reflected wave measured data; Perform remote detection reverse time migration processing on the reflected wave measured data to obtain the initial imaging results; A wave equation is established based on the initial imaging results, and the wave equation is solved to obtain the reflection wave prediction data; Obtain residual data according to the reflection wave prediction data and the reflection wave measured data; Use the residual data as the source of the detection point to perform wavefield backpropagation, construct imaging gradients and obtain imaging results; The imaging results are iteratively optimized and the optimized imaging results are output.

2. The high-precision iterative reverse time migration method for long-range acoustic logging according to claim 1, characterized in that: The remote detection acoustic logging data includes waveform data, gain and arrival time curve data.

3. The high-precision iterative reverse time migration method for long-range acoustic logging according to claim 1, characterized in that: The processing of the remote detection sonic logging data includes: Extract the measured data of reflected waves through frequency wavenumber domain filtering; The amplitude of the reflected wave measured data is restored through attenuation compensation; Multiples are suppressed by predictive deconvolution.

4. The high-precision iterative reverse time migration method for long-range acoustic logging according to claim 1, characterized in that: The process of performing remote detection reverse time migration processing on the reflected wave measured data to obtain an initial imaging result includes: The reflection wave extraction results are combined with the velocity model and simulated source to obtain the forward and reverse wave fields, and the initial imaging results are obtained based on the cross-correlation imaging conditions.

5. The high-precision iterative reverse time migration method for long-range acoustic logging according to claim 1, characterized in that: The step of establishing a wave equation based on the initial imaging result and solving the wave equation to obtain reflection wave prediction data includes: The initial imaging results are combined with the velocity model and forward wave field to establish the wave equation, which is then solved to obtain the reflection wave prediction data.

6. The high-precision iterative reverse time migration method for long-range acoustic logging according to claim 1, characterized in that: The obtaining of residual data according to the reflected wave prediction data and the reflected wave measured data includes: The predicted reflected wave data is subtracted from the measured reflected wave data to obtain the residual data.

7. The high-precision iterative reverse time migration method for long-range acoustic logging according to claim 1, characterized in that: The constructing of the imaging gradient comprises: The forward propagation of source wavelet and the reverse propagation of residual data are used to obtain the forward and reverse wave fields respectively, and the imaging gradient is obtained by combining the cross-correlation imaging condition.

8. A high-precision iterative reverse time migration system for long-range acoustic logging, characterized in that: The system includes a data collection module, a data processing module, a data solution module, a calculation module, an imaging acquisition module and an optimization module; The data collection module is used to collect and process the remote detection acoustic wave logging data to obtain the reflected wave measured data; The data processing module is used to perform remote detection reverse time migration processing on the reflected wave measured data to obtain the initial imaging results; The data solution module establishes a wave equation based on the initial imaging results and solves the wave equation to obtain the reflection wave prediction data; The calculation module is used to obtain residual data according to the reflection wave prediction data and the reflection wave measured data; The imaging acquisition module is used to use the residual data as the detection point source to perform wave field backpropagation, construct imaging gradients and obtain imaging results; The optimization module is used to iteratively optimize the imaging results and output the optimized imaging results.

9. The high-precision iterative reverse time migration system for long-range acoustic logging according to claim 8, characterized in that: The data collection module is used to process the remote detection sonic logging data, including: The data collection module extracts the reflected wave measured data through frequency wavenumber domain filtering; The amplitude of the reflected wave measured data is restored through attenuation compensation.

10. The high-precision iterative reverse time migration system for long-range acoustic logging according to claim 8, characterized in that: The solution module is used to perform remote detection reverse time migration processing on the reflected wave measured data to obtain the initial imaging result, including: The reflection wave extraction results are combined with the velocity model and simulated source to obtain the forward and reverse wave fields, and the initial imaging results are obtained based on the cross-correlation imaging conditions.

11. The high-precision iterative reverse time migration system for long-range acoustic logging according to claim 8, characterized in that: The calculation module is used to obtain residual data based on the reflection wave prediction data and the reflection wave measured data, including: The calculation module subtracts the reflected wave prediction data from the reflected wave measured data to obtain residual data.

12. 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 iterative reverse time migration method for long-range acoustic logging according to any one of claims 1 to 7.

13. 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 iterative reverse time migration method for long-range acoustic logging according to any one of claims 1 to 7.

Images