A method and apparatus for anisotropic imaging of borehole-to-surface coupling

Abstract

This invention discloses a well-to-surface combined anisotropic imaging method and apparatus. By using sedimentary facies encoding as a constraint term, and anisotropic parameters at the well logging scale as the main variable and anisotropic parameters at the surface seismic scale as secondary variables, the cokriging estimation algorithm is used to accurately obtain the well-to-surface combined anisotropic parameters. This provides a high-precision well-to-surface combined anisotropic imaging method, making the imaging results more consistent with the actual underground geological conditions. This provides a reliable basis for geological interpretation, reservoir prediction, oil and gas exploration and development strategy formulation, and geological structure analysis, improving the accuracy and efficiency of related work.

Description

Technical Field

[0001] This application relates to the field of petroleum geophysical exploration technology, and more specifically, to a well-to-surface anisotropic imaging method and apparatus. Background Technology

[0002] The propagation paths of VSP rays in surface seismic and well seismic data differ, as do their industrial application methods and conditions. Consequently, the anisotropy parameters δ and ε obtained from these methods also differ, and matching processing is required for obtaining anisotropy parameters in well-constrained surface seismic data. Currently, there is a lack of methods for anisotropy extraction, modeling, and imaging of data obtained from combined well and surface seismic data.

[0003] Chinese patent application CN111323811B discloses a multi-well seismic data imaging method and system. The method includes: Step 1: generating a first imaging profile for each single well based on jointly acquired multi-well VSP data; Step 2: generating a multi-well imaging overlay interval based on the geodetic coordinates of each wellhead, the trajectory data of each well, and the imaging range of each single well; Step 3: calculating the contribution value of each well to each point within the multi-well imaging overlay interval; Step 4: generating a second imaging profile for each single well based on the contribution values ​​of each well and the first imaging profile of each single well; Step 5: overlaying the second imaging profiles of each single well to generate a multi-well imaging profile.

[0004] Based on the aforementioned comparative documents, it can be seen that existing technologies typically invert and obtain anisotropic parameters from ground seismic and VSP data separately, resulting in a mismatch between the parameters in terms of scale and physical meaning. Furthermore, simple interpolation or substitution is often used for fusion, ignoring the differences in well-seismic scales and geological patterns, which leads to unreasonable models in the inter-well region and ultimately limits the high-precision imaging effect. Summary of the Invention

[0005] This invention provides a method and apparatus for anisotropic imaging in well-to-surface mining, to solve the technical problems of the prior art as described in the background section. The method includes: Acquire co-detector data and preprocess the co-detector data, which includes seismic data, well logging data, and geological information; First arrival time data of direct waves are obtained from the preprocessed co-detector point data, and based on the first arrival time data of direct waves and logging data, anisotropy parameters at the logging scale are obtained through an inversion algorithm; The anisotropic parameters at the logging scale are corrected for normal time difference and calculated for forward travel time to obtain the quality-controlled anisotropic parameters; The quality-controlled anisotropy parameters are input into the well seismic difference empirical formula to obtain the anisotropy parameters at the surface seismic scale; Based on the geological sedimentary facies characteristics, sedimentary facies were coded for the study area. Using the Cokriging interpolation formula, the quality-controlled anisotropy parameters were fused with the anisotropy parameters at the surface seismic scale to obtain the well-seismic combined anisotropy parameters. An anisotropic reverse time migration algorithm is performed based on the anisotropic parameters of well-seismic coherence, and high-precision imaging of seismic data is achieved through the reverse time migration algorithm.

[0006] In some specific embodiments, the common detector data is preprocessed, including: The seismic data is subjected to format standardization, noise suppression, and geometric correction. Depth matching, anomaly removal, and anisotropy parameter calculation were performed on the well logging data; Geological information is digitized and spatially registered; Quantitative quality control is performed using signal-to-noise ratio and phase consistency, and cross-validation using multi-source data is combined to repair and supplement abnormal data, thereby ensuring the reliability of the co-detector point data.

[0007] In some specific embodiments, the inversion algorithm is as follows: ; in, Indicates the velocity of the next layer. The velocity of the previous layer is represented by θ, and the ray angle is represented by θ. These are weak anisotropy parameters related to the vertical direction at the logging scale. The parameter represents the difference between the velocity in the horizontal direction and the velocity in the logging scale. sin represents the sine function and cos represents the cosine function. The velocity of the next layer is determined by the arrival time data of the direct wave.

[0008] In some specific embodiments, the anisotropic parameters at the logging scale are corrected for normal time difference and calculated for forward travel time to obtain the quality-controlled anisotropic parameters, specifically: Normal time difference correction is applied to the anisotropic parameters at the logging scale to obtain the seismic gather after eliminating the influence of shot-receiver offset; Forward traveltime calculations were performed on the anisotropic parameters at the logging scale to obtain theoretical traveltime data at each ray angle. The theoretical travel time data is compared with the travel time data of actual seismic data, and the anisotropy parameters at the logging scale are optimized based on the comparison results to obtain the quality-controlled anisotropy parameters.

[0009] In some specific embodiments, the anisotropy parameters at the logging scale are optimized based on the comparison results to obtain the quality-controlled anisotropy parameters, specifically: When the comparison result exceeds a preset threshold, the input co-detector point data is adjusted, and the anisotropy parameters at the logging scale are re-acquired based on the adjusted co-detector point data until the current comparison result does not exceed the preset threshold, and the anisotropy parameters at this time are output as the quality-controlled anisotropy parameters; When the comparison result does not exceed the preset threshold, the anisotropy parameter at this time is output as the anisotropy parameter after quality control.

[0010] In some specific embodiments, the empirical formula for well vibration difference is as follows: ; ; in, These are weak anisotropy parameters related to the vertical direction at the ground seismic scale. This is a parameter representing the difference in velocity between the ground seismic scale and the horizontal direction.

[0011] In some specific embodiments, the cokriging interpolation formula is as follows: ; ; in, These are estimates of the anisotropy parameters. It is the value of the well logging average anisotropy parameter. It is the weighted coefficient value corresponding to the well logging average anisotropy parameter value. These are the sampled values ​​of the earthquake mean anisotropy parameter. It is the weighting coefficient value of the sampled values ​​of the seismic mean anisotropy parameter. It is a sedimentary facies code. is the weighted coefficient value for the sedimentary facies encoding, where N is the number of sedimentary facies.

[0012] Accordingly, the present invention also proposes an anisotropic imaging device for well-to-surface combined mining, the device comprising: The first acquisition module is used to acquire co-detector point data and preprocess the co-detector point data, which includes seismic data, well logging data, and geological information. The inversion module is used to obtain the first arrival time data of the direct wave from the preprocessed co-detector point data, and based on the first arrival time data of the direct wave and the logging data, to obtain the anisotropy parameters at the logging scale through the inversion algorithm; The correction module is used to perform normal time difference correction and forward travel time calculation on the anisotropic parameters at the logging scale to obtain the quality-controlled anisotropic parameters; The second acquisition module is used to input the quality-controlled anisotropy parameters into the well seismic difference empirical formula to obtain the anisotropy parameters at the surface seismic scale; The fusion module is used to encode the sedimentary facies of the study area based on the geological sedimentary facies characteristics, and to fuse the quality-controlled anisotropy parameters with the anisotropy parameters at the ground seismic scale using the cokriging interpolation formula to obtain the well-seismic combined anisotropy parameters; The imaging module is used to perform anisotropic reverse time migration algorithm based on the anisotropic parameters of well-seismic co-location, and to perform high-precision imaging of seismic data through the reverse time migration algorithm.

[0013] One embodiment of the present invention also provides a computing device, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, wherein when the computer-executable instructions are executed by the processor, the steps of the well-to-surface anisotropic imaging method as described in any of the above claims are implemented.

[0014] One embodiment of the present invention also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the well-to-surface anisotropic imaging method as described in any of the above claims.

[0015] By applying the above technical solutions, a well-to-surface anisotropic imaging method is proposed. The method includes: acquiring common-detector data and preprocessing the common-detector data, which includes seismic data, well logging data, and geological information; obtaining direct wave arrival time data from the preprocessed common-detector data, and based on the direct wave arrival time data and well logging data, obtaining anisotropic parameters at the well logging scale using an inversion algorithm; performing normal time difference correction and forward travel time calculation on the anisotropic parameters at the well logging scale to obtain quality-controlled anisotropic parameters; and inputting the quality-controlled anisotropic parameters into the well seismic difference empirical formula to obtain the surface seismic scale. Anisotropic parameters were obtained from well logging and seismic data. Based on the geological sedimentary facies characteristics, sedimentary facies were encoded in the study area. Using the cokriging interpolation formula, the quality-controlled anisotropic parameters were fused with the anisotropic parameters at the surface seismic scale to obtain well-seismic combined anisotropic parameters. Anisotropic reverse time migration algorithm was performed based on the well-seismic combined anisotropic parameters, and high-precision imaging of seismic data was achieved through the reverse time migration algorithm. By using sedimentary facies encoding as a constraint term, with well logging anisotropic parameters as the main variable and surface seismic anisotropic parameters as secondary variables, the cokriging estimation algorithm was used to accurately obtain the well-seismic combined anisotropic parameters, making the imaging results more consistent with the actual underground geological conditions. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of an anisotropic imaging method for well-to-surface mining provided in an embodiment of this application; Figure 2 This is a schematic diagram of an acoustic imaging result provided in an embodiment of this application; Figure 3 This is a schematic diagram of an anisotropic imaging result provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an anisotropic imaging device for well-to-surface mining provided in an embodiment of this application; Figure 5 This is a structural block diagram of a computing device provided in an embodiment of this application. Detailed Implementation

[0018] Many specific details are set forth in the following description to provide a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0019] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of one or more embodiments of this specification. The singular forms “a” and “the” as used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0020] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this specification, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when" or "in response to a determination".

[0021] Specifically, such as Figure 1As shown, this application proposes an anisotropic imaging method for well-to-surface combined mining, the method comprising the following steps: Step S101: Obtain co-detector data and preprocess the co-detector data, which includes seismic data, well logging data and geological information.

[0022] In one possible implementation, the common detector data is preprocessed, including: The seismic data is subjected to format standardization, noise suppression, and geometric correction. Depth matching, anomaly removal, and anisotropy parameter calculation were performed on the well logging data; Geological information is digitized and spatially registered; Quantitative quality control is performed using signal-to-noise ratio and phase consistency, and cross-validation using multi-source data is combined to repair and supplement abnormal data, thereby ensuring the reliability of the co-detector point data.

[0023] Specifically, the first step is to collect and input common receiver data, including seismic data, well logging data, and relevant geological information. This data forms the basis for subsequent anisotropy inversion and imaging. Ensuring the quality and integrity of the data, especially the accuracy of seismic wavefield records and well logging curves, is crucial to providing reliable input for subsequent steps.

[0024] In this embodiment, to ensure data quality and integrity, preprocessing of the co-detector point data is required. The preprocessing includes: format standardization, noise suppression, and geometric correction of the co-detector point seismic data; depth matching, anomaly removal, and anisotropy parameter calculation of well logging data; and digitization and spatial registration of geological information. Simultaneously, to ensure quality, quantitative quality control is implemented through indicators such as signal-to-noise ratio and phase consistency; cross-validation of multi-source data is performed; and abnormal data is repaired and supplemented to ensure data reliability.

[0025] Step S102: Obtain the first arrival time data of the direct wave from the preprocessed co-detector point data, and based on the first arrival time data of the direct wave and the logging data, obtain the anisotropy parameters at the logging scale through the inversion algorithm.

[0026] In one possible implementation, the inversion algorithm is specifically as follows: ; in, Indicates the velocity of the next layer. The velocity of the previous layer is represented by θ, the ray angle is represented by δ and ε, the anisotropy parameters are represented by sin and cos, and the velocity of the next layer is determined by the arrival time data of the direct wave.

[0027] In this embodiment, anisotropy inversion is performed layer by layer using direct wave first arrival time data combined with well logging data. The anisotropy parameters ε1 and δ1 at the well logging scale are calculated using the inversion algorithm. These parameters reflect the anisotropic characteristics of the subsurface medium and provide important basis for subsequent seismic data processing.

[0028] Layer-by-layer inversion can be expressed by formula (1): (1) in, Indicates the velocity of the next layer. The velocity of the previous layer is represented by θ, and the ray angle is represented by θ. These are weak anisotropy parameters related to the vertical direction at the logging scale. The parameter represents the difference between the velocity in the horizontal direction and the velocity in the logging scale. sin represents the sine function and cos represents the cosine function. The velocity of the next layer is determined by the arrival time data of the direct wave.

[0029] It should be noted that although the arrival time data of the direct wave does not appear directly in Formula 1, Vp(θ) in the formula is calculated from the arrival time data of the direct wave (the arrival time and velocity have the relationship of "velocity = distance / arrival time"). Therefore, the calculation of Vp(θ) depends on the arrival time data of the direct wave.

[0030] Step S103: Perform normal time difference correction and forward travel time calculation on the anisotropic parameters at the logging scale to obtain the quality-controlled anisotropic parameters.

[0031] In one possible implementation, the anisotropic parameters at the logging scale are corrected for normal time difference and calculated using forward travel time to obtain the quality-controlled anisotropic parameters, specifically: Normal time difference correction is applied to the anisotropic parameters at the logging scale to obtain the seismic gather after eliminating the influence of shot-receiver offset; Forward traveltime calculations were performed on the anisotropic parameters at the logging scale to obtain theoretical traveltime data at each ray angle. The theoretical travel time data is compared with the travel time data of actual seismic data, and the anisotropy parameters at the logging scale are optimized based on the comparison results to obtain the quality-controlled anisotropy parameters.

[0032] In one possible implementation, the anisotropy parameters at the logging scale are optimized based on the comparison results to obtain the quality-controlled anisotropy parameters, specifically: When the comparison result exceeds a preset threshold, the input co-detector point data is adjusted, and the anisotropy parameters at the logging scale are re-acquired based on the adjusted co-detector point data until the current comparison result does not exceed the preset threshold, and the anisotropy parameters at this time are output as the quality-controlled anisotropy parameters; When the comparison result does not exceed the preset threshold, the anisotropy parameter at this time is output as the anisotropy parameter after quality control.

[0033] In this embodiment, after obtaining the well logging anisotropy parameters, normal time difference correction and forward travel time calculation are performed. Quality control is conducted by comparing the travel time of the actual seismic record with the forward travel time to ensure the accuracy of the inversion results. If deviations are found, the inversion parameters need to be adjusted or the input data needs to be rechecked.

[0034] In this embodiment, the accuracy of the parameters is calibrated by comparing the travel times of actual seismic records with those calculated from these inversion parameters. During the normal time difference correction process, the theoretical normal time difference is calculated using formulas based on well logging anisotropy parameters, and time difference compensation is applied to the actual seismic gathers to eliminate travel time differences caused by the shot-receiver offset. During the forward travel time calculation process, the theoretical travel times at each ray angle are calculated by substituting the inverted ε and δ values ​​into the travel time formulas, and then compared with the actual seismic record travel times for quality control.

[0035] In this embodiment, the deviation between the theoretical and actual travel times is first compared. If the deviation is large, the common detection point data (such as seismic / well logging data quality) in step S101 is checked again, and then the anisotropic inversion in step S102 (fine-tuning the inversion algorithm parameters) is repeated to recalculate ε1 and δ1.

[0036] Step S104: Input the quality-controlled anisotropy parameters into the well seismic difference empirical formula to obtain the anisotropy parameters on the surface seismic scale.

[0037] In one possible implementation, the empirical formula for wellbore seismic difference is specifically as follows: ; ; in, and The anisotropy parameter at the ground seismic scale is denoted as .

[0038] In this embodiment, based on the differences between well logging anisotropy parameters and surface seismic data, and combined with empirical formulas, the anisotropy parameters ε2 and δ2 at the surface seismic scale are derived. These parameters reflect the anisotropy characteristics at a larger scale, providing a basis for subsequent well-seismic joint inversion.

[0039] The empirical formula for wellbore seismic difference can be expressed by formulas (2)-(3): (2) (3) in, These are weak anisotropy parameters related to the vertical direction at the ground seismic scale. This is a parameter representing the difference in velocity between the ground seismic scale and the horizontal direction.

[0040] Step S105: Based on the geological sedimentary facies characteristics, the sedimentary facies of the study area are encoded. Using the Cokriging interpolation formula, the quality-controlled anisotropic parameters are fused with the anisotropic parameters at the ground seismic scale to obtain the well-seismic combined anisotropic parameters.

[0041] In one possible implementation, the cokriging interpolation formula is specifically as follows: ; ; in, These are estimates of the anisotropy parameters. It is the value of the well logging average anisotropy parameter. It is the weighted coefficient value corresponding to the well logging average anisotropy parameter value. These are the sampled values ​​of the earthquake mean anisotropy parameter. It is the weighting coefficient value of the sampled values ​​of the seismic mean anisotropy parameter. It is a sedimentary facies code. is the weighted coefficient value for the sedimentary facies encoding, where N is the number of sedimentary facies.

[0042] In this embodiment, sedimentary facies are encoded for the study area based on geological sedimentary facies characteristics. Using the cokriging interpolation method, anisotropic parameters at the well logging scale and anisotropic parameters at the surface seismic scale are fused to obtain combined well-seismic anisotropic parameters. This step effectively combines the advantages of well data and seismic data, improving the accuracy and spatial resolution of the anisotropic parameters.

[0043] Cokriging interpolation can be expressed by formulas (4)-(5): (4) (5) in, These are estimates of the anisotropy parameters. It is the value of the well logging average anisotropy parameter. It is the weighted coefficient value corresponding to the well logging average anisotropy parameter value. These are the sampled values ​​of the earthquake mean anisotropy parameter. It is the weighting coefficient value of the sampled values ​​of the seismic mean anisotropy parameter. It is a sedimentary facies code. is the weighted coefficient value for the sedimentary facies encoding, where N is the number of sedimentary facies.

[0044] Step S106: Based on the anisotropic parameters of well-seismic co-location, perform anisotropic reverse time migration algorithm, and use the reverse time migration algorithm to perform high-precision imaging of seismic data.

[0045] In this embodiment, anisotropic reverse-time migration imaging is finally performed using the anisotropic parameters from the combined well-seismic data. Through the reverse-time migration algorithm, high-precision imaging of the seismic data is achieved, generating imaging profiles reflecting subsurface structures and anisotropic characteristics. The output results can be used for subsequent work such as geological interpretation, reservoir prediction, and oil and gas exploration.

[0046] In this embodiment, the reverse time migration algorithm can employ zero-delay cross-correlation imaging conditions, which can be expressed mathematically as follows: ; Image represents the imaging result, S represents the source wavefield, R represents the receiver wavefield, and t represents the image quality. max The image represents the shot recording duration, x represents the horizontal coordinate of the imaging area, z represents the depth coordinate of the imaging area, and t represents the seismic wave propagation time.

[0047] Compared with the prior art, the present invention has the following significant advantages: By extracting and modeling anisotropic information from well-to-surface data, the anisotropic characteristics of formations can be more realistically reflected. This allows for a more accurate analysis of the distribution patterns of reservoir parameters such as elasticity, porosity, and permeability, providing more reliable geological data for oil exploration. Accurate anisotropic modeling and imaging can help determine the optimal development strategy for oil and gas reservoirs, such as determining reasonable well locations, well spacing, and injection-production schemes to improve oil and gas recovery rates. Anisotropic modeling and imaging can clearly display the morphology and characteristics of subsurface geological structures, such as faults, folds, and fractures. This is of great significance for understanding the evolutionary history of regional geological structures and analyzing geological hazard risks.

[0048] In summary, this invention proposes a well-to-surface anisotropic imaging method. The method includes: acquiring co-detector point data and preprocessing the co-detector point data, which includes seismic data, well logging data, and geological information; obtaining direct wave arrival time data from the preprocessed co-detector point data, and based on the direct wave arrival time data and well logging data, obtaining anisotropic parameters at the well logging scale using an inversion algorithm; performing normal time difference correction and forward travel time calculation on the anisotropic parameters at the well logging scale to obtain quality-controlled anisotropic parameters; inputting the quality-controlled anisotropic parameters into the well seismic difference empirical formula to obtain anisotropic parameters at the surface seismic scale; and then, based on geological sedimentation... Sedimentary facies characteristics were identified, and the study area was encoded using sedimentary facies. The quality-controlled anisotropic parameters were then fused with the anisotropic parameters at the surface seismic scale using the cokriging interpolation formula to obtain well-seismic combined anisotropic parameters. Based on these well-seismic combined anisotropic parameters, an anisotropic reverse time migration algorithm was performed, and high-precision imaging of the seismic data was achieved using the reverse time migration algorithm. By using sedimentary facies encoding as a constraint term, with well logging anisotropic parameters as the main variable and surface seismic anisotropic parameters as secondary variables, the cokriging estimation algorithm was used to accurately obtain the well-seismic combined anisotropic parameters, making the imaging results more consistent with the actual underground geological conditions.

[0049] Now, in conjunction with specific application scenarios, such as Figure 2-3 The principles of this application will be further explained.

[0050] Figure 2 The imaging results based on acoustic data are presented. These results reflect the acoustic velocity distribution characteristics of the subsurface medium, providing a basis for preliminary geological interpretation. However, due to the lack of consideration for anisotropy effects, the imaging results may have certain limitations under complex geological conditions.

[0051] Figure 3 Anisotropic imaging results obtained based on the method of this patent are shown. Figure 5 In comparison, this imaging result significantly improves the ability to resolve complex geological structures, especially in areas with significant anisotropic characteristics, where imaging accuracy is significantly enhanced. This result verifies the effectiveness and superiority of the method proposed in practical applications.

[0052] This patented method successfully extracted anisotropic parameters along the 0-degree and 90-degree line-layer directions using combined well-to-surface data and cokriging interpolation algorithms, revealing the anisotropic characteristics of the subsurface medium. These parameters provide important evidence for seismic imaging, geological interpretation, reservoir modeling, and hydrocarbon prediction. The anisotropic imaging results based on this patented method significantly improve the resolution of complex geological structures, especially in areas with pronounced anisotropic characteristics, where imaging accuracy is greatly enhanced, demonstrating the effectiveness and superiority of this method in practical applications.

[0053] This application also proposes an anisotropic imaging device for combined well and surface mining, such as... Figure 4 As shown, the device includes: The first acquisition module 10 is used to acquire co-detector point data and preprocess the co-detector point data, which includes seismic data, well logging data, and geological information. The inversion module 20 is used to obtain the first arrival time data of the direct wave from the preprocessed co-detector point data, and based on the first arrival time data of the direct wave and the logging data, to obtain the anisotropy parameters at the logging scale through an inversion algorithm; The correction module 30 is used to perform normal time difference correction and forward travel time calculation on the anisotropic parameters at the logging scale to obtain the quality-controlled anisotropic parameters; The second acquisition module 40 is used to input the quality-controlled anisotropy parameters into the well seismic difference empirical formula to obtain the anisotropy parameters at the surface seismic scale; The fusion module 50 is used to encode the sedimentary facies of the study area based on the geological sedimentary facies characteristics, and to fuse the quality-controlled anisotropy parameters with the anisotropy parameters at the ground seismic scale using the cokriging interpolation formula to obtain the well-seismic combined anisotropy parameters; The imaging module 60 is used to perform anisotropic reverse time migration algorithm based on the anisotropic parameters of well-seismic co-location, and to perform high-precision imaging of seismic data through the reverse time migration algorithm.

[0054] Figure 5 A structural block diagram of a computing device 400 according to one embodiment of this specification is shown. The components of the computing device 400 include, but are not limited to, a memory 410 and a processor 420. The processor 420 is connected to the memory 410 via a bus 430, and a database 450 is used to store data.

[0055] The computing device 400 also includes an access device 440, which enables the computing device 400 to communicate via one or more networks 460. Examples of these networks include Public Switched Telephone Network (PSTN), Local Area Network (LAN), Wide Area Network (WAN), Personal Area Network (PAN), or combinations of communication networks such as the Internet. The access device 440 may include one or more of any type of wired or wireless network interface (e.g., network interface card (NIC)), such as an IEEE 802.11 Wireless Local Area Network (WLAN) wireless interface, a Wi-MAX (Worldwide Interoperability for Microwave Access) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, or a Near Field Communication (NFC) interface.

[0056] In one embodiment of this specification, the above-described components of the computing device 400 and Figure 5 Other components not shown can also be connected to each other, for example, via a bus. It should be understood that... Figure 5 The block diagram of the computing device shown is for illustrative purposes only and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0057] The computing device 400 can be any type of stationary or mobile computing device, including mobile computers or mobile computing devices (e.g., tablet computers, personal digital assistants, laptop computers, notebook computers, netbooks, etc.), mobile phones (e.g., smartphones), wearable computing devices (e.g., smartwatches, smart glasses, etc.) or other types of mobile devices, or stationary computing devices such as desktop computers or personal computers (PCs). The computing device 400 can also be a mobile or stationary server.

[0058] The processor 420 executes the following computer-executable instructions, which, when executed by the processor, implement the steps of the above-described well-to-surface anisotropic imaging method. The above is a schematic representation of a computing device according to this embodiment. It should be noted that the technical solution of this computing device and the technical solution of the above-described well-to-surface anisotropic imaging method belong to the same concept. Details not described in detail in the technical solution of the computing device can be found in the description of the technical solution of the above-described well-to-surface anisotropic imaging method.

[0059] An embodiment of this specification also provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the above-described well-to-surface anisotropic imaging method.

[0060] The above is an illustrative scheme of a computer-readable storage medium according to this embodiment. It should be noted that the technical solution of this storage medium belongs to the same concept as the technical solution of the above-described well-to-surface anisotropic imaging method. For details not described in detail in the technical solution of the storage medium, please refer to the description of the technical solution of the above-described well-to-surface anisotropic imaging method.

[0061] An embodiment of this specification also provides a computer program, wherein when the computer program is executed in a computer, it causes the computer to perform the steps of the above-described well-to-ground anisotropic imaging method.

[0062] The above is an illustrative scheme of a computer program according to this embodiment. It should be noted that the technical solution of this computer program and the technical solution of the above-mentioned well-to-surface anisotropic imaging method belong to the same concept. For details not described in detail in the technical solution of the computer program, please refer to the description of the technical solution of the above-mentioned well-to-surface anisotropic imaging method.

[0063] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0064] The computer instructions include computer program code, which may be in the form of source code, object code, executable file, or certain intermediate forms. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium may be appropriately added to or subtracted according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media may not include electrical carrier signals and telecommunication signals.

[0065] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments in this specification are not limited to the described order of actions, because according to the embodiments in this specification, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments in this specification.

[0066] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0067] The preferred embodiments disclosed above are merely illustrative of this specification. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the embodiments described in this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the embodiments, thereby enabling those skilled in the art to better understand and utilize this specification. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. A method for anisotropic imaging in well-to-surface mining, characterized in that... The method includes: Acquire co-detector data and preprocess the co-detector data, which includes seismic data, well logging data, and geological information; First arrival time data of direct waves are obtained from the preprocessed co-detector point data, and based on the first arrival time data of direct waves and logging data, anisotropy parameters at the logging scale are obtained through an inversion algorithm; The anisotropic parameters at the logging scale are corrected for normal time difference and calculated for forward travel time to obtain the quality-controlled anisotropic parameters; The quality-controlled anisotropy parameters are input into the well seismic difference empirical formula to obtain the anisotropy parameters at the surface seismic scale; Based on the geological sedimentary facies characteristics, sedimentary facies were coded for the study area. Using the Cokriging interpolation formula, the quality-controlled anisotropy parameters were fused with the anisotropy parameters at the surface seismic scale to obtain the well-seismic combined anisotropy parameters. An anisotropic reverse time migration algorithm is performed based on the anisotropic parameters of well-seismic coherence, and high-precision imaging of seismic data is achieved through the reverse time migration algorithm.

2. The method according to claim 1, characterized in that... The common detector data is preprocessed, including: The seismic data is subjected to format standardization, noise suppression, and geometric correction. Depth matching, anomaly removal, and anisotropy parameter calculation were performed on the well logging data; Geological information is digitized and spatially registered; Quantitative quality control is performed using signal-to-noise ratio and phase consistency, and cross-validation using multi-source data is combined to repair and supplement abnormal data, thereby ensuring the reliability of the co-detector point data.

3. The method according to claim 2, characterized in that... The inversion algorithm is specifically as follows: ; in, Indicates the velocity of the next layer. The velocity of the previous layer is represented by θ, and the ray angle is represented by θ. These are weak anisotropy parameters related to the vertical direction at the logging scale. The parameter represents the difference between the velocity in the horizontal direction and the velocity in the logging scale. sin represents the sine function and cos represents the cosine function. The velocity of the next layer is determined by the arrival time data of the direct wave.

4. The method according to claim 3, characterized in that... The anisotropic parameters at the logging scale are corrected for normal time difference and calculated for forward travel time to obtain the quality-controlled anisotropic parameters, specifically: Normal time difference correction is applied to the anisotropic parameters at the logging scale to obtain the seismic gather after eliminating the influence of shot-receiver offset; Forward traveltime calculations were performed on the anisotropic parameters at the logging scale to obtain theoretical traveltime data at each ray angle. The theoretical travel time data is compared with the travel time data of actual seismic data, and the anisotropy parameters at the logging scale are optimized based on the comparison results to obtain the quality-controlled anisotropy parameters.

5. The method according to claim 4, characterized in that... Based on the comparison results, the anisotropy parameters at the logging scale were optimized to obtain the quality-controlled anisotropy parameters, specifically: When the comparison result exceeds a preset threshold, the input co-detector point data is adjusted, and the anisotropy parameters at the logging scale are re-acquired based on the adjusted co-detector point data until the current comparison result does not exceed the preset threshold, and the anisotropy parameters at this time are output as the quality-controlled anisotropy parameters; When the comparison result does not exceed the preset threshold, the anisotropy parameter at this time is output as the anisotropy parameter after quality control.

6. The method according to claim 5, characterized in that... The empirical formula for well vibration difference is as follows: ; ; in, These are weak anisotropy parameters related to the vertical direction at the ground seismic scale. This is a parameter representing the difference in velocity between the ground seismic scale and the horizontal direction.

7. The method according to claim 6, characterized in that... The cokriging interpolation formula is as follows: ; ; in, These are estimates of the anisotropy parameters. It is the value of the well logging average anisotropy parameter. It is the weighted coefficient value corresponding to the well logging average anisotropy parameter value. These are the sampled values ​​of the earthquake mean anisotropy parameter. It is the weighting coefficient value of the sampled values ​​of the seismic mean anisotropy parameter. It is a sedimentary facies code. is the weighted coefficient value for the sedimentary facies encoding, where N is the number of sedimentary facies.

8. An anisotropic imaging device for combined well and surface mining, characterized in that... The device includes: The first acquisition module is used to acquire co-detector point data and preprocess the co-detector point data, which includes seismic data, well logging data, and geological information. The inversion module is used to obtain the first arrival time data of the direct wave from the preprocessed co-detector point data, and based on the first arrival time data of the direct wave and the logging data, to obtain the anisotropy parameters at the logging scale through the inversion algorithm; The correction module is used to perform normal time difference correction and forward travel time calculation on the anisotropic parameters at the logging scale to obtain the quality-controlled anisotropic parameters; The second acquisition module is used to input the quality-controlled anisotropy parameters into the well seismic difference empirical formula to obtain the anisotropy parameters at the surface seismic scale; The fusion module is used to encode the sedimentary facies of the study area based on the geological sedimentary facies characteristics, and to fuse the quality-controlled anisotropy parameters with the anisotropy parameters at the ground seismic scale using the cokriging interpolation formula to obtain the well-seismic combined anisotropy parameters; The imaging module is used to perform anisotropic reverse time migration algorithm based on the anisotropic parameters of well-seismic co-location, and to perform high-precision imaging of seismic data through the reverse time migration algorithm.

9. A computing device, characterized in that... ,include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the well-to-ground anisotropic imaging method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that... It stores computer-executable instructions, which, when executed by a processor, implement the steps of the well-to-ground anisotropic imaging method according to any one of claims 1 to 7.

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