Rock thickness identification method, device, equipment and medium

Abstract

The present application discloses a rock thickness identification method, apparatus, device, and medium, relating to the field of rock exploration. The method comprises: acquiring post-stack seismic data and target drilling data, wherein the post-stack seismic data is obtained by processing raw acquisition data corresponding to a target rock group through seismic data processing, and the target drilling data is used to indicate the distribution of drilling wells in the target rock group, wherein the target rock group includes a first type of rock; generating a sensitivity factor corresponding to the first type of rock using a preset model based on the post-stack seismic data and the target drilling data, wherein the sensitivity factor is used to indicate the lateral distribution of the thickness of the first type of rock; and determining the thickness data corresponding to the first type of rock based on the sensitivity factor. The lateral distribution of the thickness of the first type of rock is quickly identified based on the post-stack seismic data, thereby shortening the time required for rock measurement and improving the efficiency of rock thickness identification.

Description

Technical Field

[0001] The present application relates to the field of rock exploration, and in particular to a rock thickness identification method, device, equipment and medium. Background Art

[0002] Shale is a type of claystone, formed by dehydrated clay cementation. It easily breaks apart into distinct layers. Shale, including the Longmaxi Formation, is classified in southern Sichuan as reservoir and non-reservoir shale. Reservoir shale is further categorized as Type I, Type II, and Type III shale. Type I and II shales are collectively referred to as high-quality shales, and their thickness is often a key factor in achieving high gas yields. Rapidly and accurately describing the vertical and horizontal distribution of high-quality shales can strongly support the efficient, large-scale, and profitable development of shale gas reservoirs.

[0003] In related technologies, the identification of high-quality shale is mainly based on rock physics analysis and seismic inversion methods. This method has high vertical and horizontal resolution and is characterized by quantitative description.

[0004] However, in southern Sichuan, the most sensitive petrophysical parameter for distinguishing high-quality from low-quality shale in the Longmaxi Formation is often density, necessitating prestack seismic inversion. To obtain a stable and reliable density volume, this inversion requires certain seismic data requirements: large-offset, high-coverage seismic data acquisition, and high-fidelity, high-amplitude-preserving prestack high-resolution processing to generate seismic angular gathers with incident angles of 40 degrees or greater. These conditions reduce the efficiency of identifying high-quality shale thickness in the Longmaxi Formation, impacting exploration costs and project lifecycles, making it difficult to meet the rapid pace of shale gas reserve and production growth. Summary of the Invention

[0005] The embodiments of the present application provide a rock thickness identification method, device, equipment, and medium, which can improve the efficiency of identifying the thickness of specific types of rocks. The technical solution is as follows:

[0006] In one aspect, a rock thickness identification method is provided, which is applied to a computer device, and the method comprises:

[0007] Acquiring post-stack seismic data and target drilling data, wherein the post-stack seismic data is obtained by processing original acquired data corresponding to the target rock group, and the target drilling data is used to represent the distribution of drilling wells in the target rock group, wherein the target rock group includes the first type of rock;

[0008] Based on the post-stack seismic data and the target drilling data, generating a sensitivity factor corresponding to the first type of rock through a preset model, wherein the sensitivity factor is used to indicate a lateral distribution of the thickness of the first type of rock;

[0009] Thickness data corresponding to the first type of rock is determined based on the sensitivity factor.

[0010] In an optional embodiment, the target rock group further includes a second type of rock;

[0011] Generating the sensitivity factor corresponding to the first type of rock by a preset model based on the post-stack seismic data and the target drilling data includes:

[0012] generating a first data volume based on the post-stack seismic data, wherein the first data volume is used to represent the degree of difference between the first type of rock and the second type of rock;

[0013] generating attribute characteristics of the first type of rock based on the target drilling data, wherein the attribute characteristics are used to identify the thickness of the first type of rock;

[0014] Based on the attribute characteristics and the first data body, a sensitivity factor corresponding to the first type of rock is generated.

[0015] In an optional embodiment, the target rock group includes an interfering rock formation;

[0016] The step of generating a first data volume based on the post-stack seismic data comprises:

[0017] The first data volume is obtained by removing the lithologic interface reflection corresponding to the interfering rock layer from the post-stack seismic data.

[0018] In an optional embodiment, the target rock group includes a target rock formation;

[0019] The step of removing the lithologic interface reflection corresponding to the interfering rock layer from the post-stack seismic data to obtain the first data volume includes:

[0020] Based on the post-stack seismic data, performing spectrum analysis on the target rock formation to obtain analysis results;

[0021] Based on the analysis results, a wavelet library corresponding to the target rock group is constructed, wherein the wavelet library is used to characterize the characteristics of the seismic signal in the target rock layer;

[0022] The wavelet corresponding to the interfering rock layer in the wavelet library is removed to obtain the first data volume.

[0023] In an optional embodiment, the seismic data processing includes at least one processing method of spherical divergence correction processing, Q compensation processing for formation absorption, amplitude processing, wavelet deconvolution processing, surface consistency static correction processing, velocity analysis processing, dynamic correction processing, and residual static correction processing.

[0024] In an optional embodiment, generating the attribute characteristics of the first type of rock based on the target drilling data includes:

[0025] generating compressional wave velocity and density parameters corresponding to the target rock group based on the target drilling data;

[0026] Determining a first forward analysis result based on the longitudinal wave velocity and the density parameter;

[0027] determining a second forward analysis result based on the first data volume;

[0028] The first forward analysis result and the second forward analysis result are compared to determine the attribute characteristics.

[0029] In an optional embodiment, determining thickness data corresponding to the first type of rock based on the sensitivity factor includes:

[0030] tracking the rock top position corresponding to the first type of rock based on the sensitive factor;

[0031] The thickness data is determined based on the rock roof position and the original acquired data.

[0032] In another aspect, a rock thickness identification device is provided, comprising:

[0033] an acquisition module, configured to acquire post-stack seismic data and target drilling data, wherein the post-stack seismic data is obtained by processing the original acquisition data, and the target drilling data is used to represent the distribution of drilling wells in the target rock group, wherein the target rock group includes the first type of rock;

[0034] a generating module, configured to generate a sensitivity factor corresponding to the first type of rock based on the post-stack seismic data and the target drilling data, wherein the sensitivity factor is used to indicate a lateral distribution of the thickness of the first type of rock;

[0035] A determination module is used to determine thickness data corresponding to the first type of rock based on the sensitivity factor.

[0036] On the other hand, a computer device is provided, comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, at least one program, the code set, or the instruction set is loaded and executed by the processor to implement any of the rock thickness identification methods described in the embodiments of the present application.

[0037] On the other hand, a computer-readable storage medium is provided, in which at least one program code is stored. The program code is loaded and executed by a processor to implement any rock thickness identification method described in the embodiments of the present application.

[0038] In another aspect, a computer program product or computer program is provided. The computer program product or computer program includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the rock thickness identification method described in any of the above embodiments.

[0039] The technical solution provided by this application includes at least the following beneficial effects:

[0040] Based on the post-stack seismic data and the target drilling data, a sensitivity factor corresponding to the first type of rock is generated through a preset model, and the thickness data corresponding to the first type of rock is determined based on the sensitivity factor, wherein the sensitivity factor is used to indicate the lateral distribution corresponding to the thickness of the first type of rock, that is, the lateral distribution corresponding to the thickness of the first type of rock can be quickly identified through the post-stack seismic data, thereby shortening the corresponding rock measurement time and improving the efficiency of rock thickness identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0042] Figure 1 This is a block diagram of a computer device structure provided by an exemplary embodiment of the present application;

[0043] Figure 2 is a flow chart of a rock thickness identification method provided by an exemplary embodiment of the present application;

[0044] Figure 3 is a flow chart of a rock thickness identification method provided by another exemplary embodiment of the present application;

[0045] Figure 4 is a cross-sectional schematic diagram provided by an exemplary embodiment of the present application;

[0046] Figure 5 is a cross-sectional schematic diagram provided by another exemplary embodiment of the present application;

[0047] Figure 6 is a schematic diagram of a first forward analysis model provided by an exemplary embodiment of the present application;

[0048] Figure 7 is a schematic diagram of a second forward analysis model provided by an exemplary embodiment of the present application;

[0049] Figure 8 is a thickness prediction plan view provided by an exemplary embodiment of the present application;

[0050] Figure 9 This is a flow chart of a method for identifying high-quality shale thickness provided by an exemplary embodiment of the present application;

[0051] Figure 10 This is a structural block diagram of a rock thickness identification device provided by an exemplary embodiment of the present application;

[0052] Figure 11 It is a structural block diagram of a rock thickness identification device provided by another exemplary embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0054] First, a brief introduction to the terms involved in the embodiments of this application is given:

[0055] Seismic data processing: Seismic data processing involves the use of digital computers to process and transform raw data obtained from field seismic exploration in order to obtain high-quality, reliable seismic information, providing an intuitive and reliable basis and relevant geological information for subsequent data interpretation. Field seismic data contain information about underground structures and lithology, but this information is superimposed on a background of interference and distorted by external factors. The information is often intertwined and not suitable for direct geological interpretation. Therefore, field seismic data must be processed.

[0056] Longmaxi Shale: This rock formation consists of black graptolite shales in the lower part and blue-gray to yellow-green argillaceous or silty shales in the upper part, containing minor graptolites (such as the graptolite groups of the Sedum and Sedum zones). It forms a conformable contact with the underlying Guanyinqiao Member of the Wufeng Formation. This formation is 400-1,300 meters thick. Its key characteristics include: conformable above the Pagoda Formation and below the Xintan Formation, graptolite-rich black shales, carbonaceous shales, cherts, and carbonaceous siliceous shales, interbedded with convex bodies of marl (Guanyinqiao Formation), and rich in brachiopods, trilobites, and graptolite fossils. It is primarily distributed in Hubei, Hunan, Yunnan, Guizhou, Sichuan, and Shaanxi.

[0057] Drilling: usually refers to the engineering of drilling wells and large-diameter water wells for the exploration or development of liquid and gaseous minerals such as oil and natural gas.

[0058] For example, the rock thickness identification method shown in the embodiment of the present application can be applied to a computer device, see Figure 1 , Figure 1 This is a block diagram of a computer device structure provided by an exemplary embodiment of the present application. The computer device 101 includes a processor 102 and a memory 103. The memory 103 stores at least one instruction, which is loaded and executed by the processor 102 to implement the rock thickness identification method as described in the various method embodiments of the present application.

[0059] In this application, the computer device 101 is an electronic device capable of processing data and completing calculations. The computer device 101 includes two major parts: a hardware system and a software system. The computer device 101 can receive data obtained by measuring instruments on the compressor, organize and calculate the above data, obtain the energy consumption evaluation results of the compressor, and output the energy consumption evaluation results.

[0060] Processor 102 may include one or more processing cores. Using various interfaces and circuits, processor 102 connects various components within the entire terminal and executes instructions, programs, code sets, or instruction sets stored in memory 103, as well as accesses data stored in memory 103, thereby performing various functions of computer device 101 and processing data.

[0061] The memory 103 may include a random access memory (RAM) or a read-only memory (ROM). Optionally, the memory 103 includes a non-transitory computer-readable storage medium. The memory 103 may be used to store instructions, programs, codes, code sets, or instruction sets. The memory 103 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as compressor power calculation, compressor energy consumption evaluation result generation, etc.), instructions for implementing the following various method embodiments, etc.; the data storage area may store data involved in the following various method embodiments, etc.

[0062] Please refer to Figure 2 , which shows a rock thickness identification method provided by an embodiment of the present application. The method can be applied to the computer device shown above, and the method includes:

[0063] Step 201: Acquire post-stack seismic data and target drilling data.

[0064] In an embodiment of the present application, post-stack seismic data is obtained by processing the original acquired data corresponding to the target rock group through seismic data. The original acquired data is the original, unprocessed data collected by technicians through field exploration and input into a computer device. The original acquired data is subjected to seismic data processing to obtain post-stack seismic data for thickness identification. Illustratively, a processor reads the original acquired data from a memory, performs seismic data processing on it, and obtains post-stack seismic data. The memory then stores the post-stack seismic data. In one example, the memory also stores the rock type corresponding to the post-stack seismic data. When rock thickness identification is required, the processor retrieves the corresponding post-stack seismic data from the memory based on the rock type.

[0065] Schematically, seismic data processing includes at least one processing method of spherical divergence correction processing, Q compensation processing for formation absorption, amplitude processing, wavelet deconvolution processing, surface consistency static correction processing, velocity analysis processing, dynamic correction processing, and residual static correction processing.

[0066] Target drilling data represents the distribution of wells within the target rock group. The target rock group includes Type I rocks. For example, the target rock group is the Longmaxi Formation shale, and Type I rocks are high-quality shales within the Longmaxi Formation. The Longmaxi Formation shale can be divided into two categories: reservoir and non-reservoir. Reservoirs can be further divided into Type I, Type II, and Type III shales. Types I and II shales are collectively referred to as high-quality shales, i.e., Type I shales. For an example, see Table 1, where the reservoir classification is based on four criteria: total organic carbon content, porosity, brittleness index, and gas content.

[0067] Table 1

[0068] parameter Class Ⅰ (high quality) Class II (better) Class III (poor) Non-reservoir Total organic carbon content (%) ＞3 2～3 1～2 ＜1 Porosity (%) ＞5 3～5 2～3 ＜2 Brittleness index (%) ＞55 45～55 30～45 ＜30 Gas content (m^3 / t) ＞3 2～3 1～2 ＜1

[0069] Schematically, the distribution of the above-mentioned drilling wells includes drilling type, drilling depth and drilling distribution location. Schematically, the drilling type is a typical well in the work area.

[0070] Step 202: Based on the post-stack seismic data and the target drilling data, a sensitivity factor corresponding to the first type of rock is generated by a preset model.

[0071] In an embodiment of the present application, the target rock group also includes a second type of rock. Schematically, when the target rock group is the Longmaxi Formation shale, the second type of rock corresponds to the non-high-quality shale in the Longmaxi Formation shale, that is, the Longmaxi Formation shale other than Type I and Type II shale.

[0072] In the embodiment of the present application, the above-mentioned sensitivity factor is used to indicate the lateral distribution corresponding to the thickness of the first type of rock.

[0073] In an embodiment of the present application, based on post-stack seismic data and target drilling data, generating a sensitivity factor corresponding to the first type of rock through a preset model includes: generating a first data body based on the post-stack seismic data, wherein the first data body is used to represent the degree of difference between the first type of rock and the second type of rock; based on the target drilling data, generating attribute characteristics of the first type of rock, wherein the attribute characteristics are used to identify the thickness of the first type of rock; based on the attribute characteristics and the first data body, generating a sensitivity factor corresponding to the first type of rock.

[0074] Schematically, taking the Longmaxi Formation shale as an example, based on the post-stack seismic data, the lithologic interface reflections of the Longmaxi Formation, Wufeng Formation shale, and Pagoda Formation limestone are removed to generate a first data volume that highlights the response differences between high-quality shale and non-high-quality shale. The above-mentioned method for removing lithologic interface reflections includes: conducting seismic data spectrum analysis on the target layer to determine the main frequency; constructing a positive polarity wavelet library based on the main frequency, wherein the wavelet library is used to characterize the seismic signal characteristics in the target rock layer; and using the matching pursuit method to remove the wavelet response corresponding to the lithologic interface to obtain a wavelet reconstruction data volume, i.e., the first data volume. The above-mentioned matching pursuit method decomposes the signal corresponding to the wavelet response corresponding to the lithologic interface in a complete wavelet library, and finally obtains the wavelet reconstruction data volume.

[0075] Then, based on the target drilling data, the sensitivity factor is determined by a preset model. The preset model is an image model generated by a computer by reading the target drilling data and the first data body. Schematically, the preset model includes a first forward analysis model and a second forward analysis model. The first forward analysis model determines the first forward analysis result based on the actual geological conditions of the target drilling, and the second forward analysis model determines the second forward analysis result based on the geological conditions corresponding to the target rock after removing the lithologic interface reflection. Schematically, based on typical wells in the work area, a forward analysis of the high-quality shale response characteristic model after removing the lithologic interface is carried out to find the attribute that can most sensitively identify the thickness of high-quality shale. Wherein, it includes step A: determining the longitudinal wave velocity and density parameters corresponding to the first type of rock and the second type of rock according to the target drilling data, and constructing a seismic forward model based on the longitudinal wave velocity and density parameters, carrying out a forward analysis, and obtaining a first forward analysis result. In one example, based on representative wells drilled in the target area, the P-wave velocity and density parameters of the high-quality shale, Type III shale, non-reservoir formations, and limestone of the Longmaxi Formation's first member were determined. A seismic forward model was constructed and a forward analysis was performed. Step B: The lithologic interface differences between the first and second rock types were removed, and a forward analysis was performed to obtain a second forward analysis result. Step C: The first and second forward analysis results were compared to determine attribute characteristics.

[0076] Based on the above attribute characteristics, a sensitive factor is extracted from the first data volume, and the sensitive factor is used to indicate the lateral distribution corresponding to the thickness of the first type of rock.

[0077] Step 203: Determine thickness data corresponding to the first type of rock based on the sensitivity factor.

[0078] Schematically, the top position of the first type of rock is tracked based on the sensitivity factor, and the thickness data is determined based on the top position and the original acquisition data. For example, if the first type of rock is high-quality shale in the Longmaxi Formation, after determining the top position of the first type of rock, the thickness data corresponding to the first type of rock = (time value corresponding to the trough - time value of the bottom boundary of the Wufeng Formation) * interval velocity / 2.

[0079] For example, taking the first type of rock corresponding to the high-quality shale of the Longmaxi Formation as an example, once the thickness data of the above-mentioned high-quality shale is determined, the specific location for subsequent exploration or collection of shale gas can be determined based on the thickness data, thereby improving the efficiency of shale gas extraction.

[0080] In summary, the rock thickness identification method provided in the present application generates a sensitivity factor corresponding to the first type of rock through a preset model based on post-stack seismic data and target drilling data, and determines the thickness data corresponding to the first type of rock based on the sensitivity factor, wherein the sensitivity factor is used to indicate the lateral distribution corresponding to the thickness of the first type of rock, that is, the lateral distribution corresponding to the thickness of the first type of rock can be quickly identified through the post-stack seismic data, shortening the corresponding rock measurement time and improving the efficiency of rock thickness identification.

[0081] Please refer to Figure 3 , which illustrates a rock thickness identification method provided in an embodiment of the present application. In this embodiment, the Long-1-1 submember to the Wufeng Formation of the Silurian Longmaxi Formation in the L203 well area of ​​the Sichuan Basin is selected as the research object. This method is used to determine the lateral distribution of high-quality shale. The method includes:

[0082] Step 301: Acquire post-stack seismic data and target drilling data.

[0083] Technicians obtain raw data through field exploration and acquisition, input the raw data into a computer, and process the data to obtain post-stack seismic data. Schematically, the computer also stores target drilling data, which represents the distribution of wells drilled within the target rock group. The target rock group includes Type I and Type II rocks. Schematically, the target rock group is the Longyi Submember to the Wufeng Formation of the Silurian Longmaxi Formation in the L203 well area of ​​the Sichuan Basin. Type I rocks correspond to the high-quality shale of the Longmaxi Formation, and Type II rocks are rocks within the target rock group other than the high-quality shale of the Longmaxi Formation.

[0084] Step 302: Generate a first data volume based on post-stack seismic data.

[0085] In this embodiment of the present application, the target rock group includes interfering rock layers and target rock layers. Illustratively, the first data volume is obtained by removing the lithologic interface reflections corresponding to the interfering rock layers from the post-stack seismic data. Specifically, a spectral analysis is performed on the target rock layers based on the post-stack seismic data to obtain analysis results. Based on the analysis results, a wavelet library corresponding to the target rock group is constructed. The wavelets corresponding to the interfering rock layers in the wavelet library are removed to obtain the first data volume.

[0086] In one example, for the target layer Longmaxi Formation, with the bottom boundary of the Wufeng Formation as a constraint, 80ms up and 20ms down, the fast Fourier transform is used to extract the main frequency plane attributes of the seismic data. Through histogram statistics, it is found that the main frequency variation range is 28HZ-34HZ. Indicatively, the above-mentioned spectrum analysis method can also be other time-frequency methods in addition to the above-mentioned fast Fourier transform. In view of the fact that most of the Longmaxi Formation in southern Sichuan is deep-water shelf phase sedimentary, the reservoir is relatively continuous and stable laterally, and the bottom boundary of the Wufeng Formation is a geological and seismic response feature with strong peak reflection, a positive polarity 28-34HZ based on dynamic matching wavelet library is constructed according to the spectrum analysis results, and the matching pursuit method is used to match the wavelet with the highest correlation, and the wavelet is subtracted from the seismic trace in the original post-stack seismic data to obtain the first data body after removing the lithologic interface. Figure 4 FIG5 shows a migration stacked cross-section 400 along the L203-H well trajectory, where the L203-H well 401 is shown. FIG6 shows a migration stacked cross-section 500 along the L203-H well trajectory after removing the lithologic interface reflection, including the L203-H well 501, the high-quality shale top 502, and the high-quality shale bottom 503.

[0087] Step 303: Generate attribute characteristics of the first type of rock based on the target drilling data.

[0088] In an embodiment of the present application, based on the target drilling data, the P-wave velocity and density parameters corresponding to the target rock group are generated; based on the P-wave velocity and density parameters, a first forward analysis result is determined; based on the first data body, a second forward analysis result is determined; and the first forward analysis result and the second forward analysis result are compared to determine the attribute characteristics.

[0089] In one example, based on typical wells in the work area, a forward analysis of the high-quality shale response characteristic model after removing the lithologic interface was carried out to construct the attribute that can most sensitively identify the thickness of high-quality shale. That is, based on the typical wells drilled in the area, the P-wave velocity and density curves were processed using low-pass filtering or square wave conversion methods to determine the P-wave velocity and density parameters of the high-quality shale in the first section of the Longmaxi Formation, Class III shale, non-reservoir layer, and limestone in the Pagoda Formation. Then, combined with the logging stratification and interpretation conclusions, a seismic forward model was constructed and forward analysis was carried out, such as Figure 6As shown, it shows the first forward analysis model 600, that is, the actual geological model 610 based on the typical well, the forward section 620 of the actual address model and the forward parameters 630. Then, the lithologic interface differences between the Longmaxi Formation, Wufeng Formation shale and Baota Formation limestone are removed and the forward analysis is carried out, as shown in FIG. Figure 7 As shown, it shows the second forward analysis model 700, that is, the geological model 710 without the lithologic interface, the forward section 720 without the lithologic interface model, and the forward parameters 730. By comparing the forward results of the two models, the sensitive factors of the high-quality shale response are found and constructed. Figure 6 and Figure 7 It can be seen that after removing the lithologic interface, the top of the high-quality shale can be clearly traced, so the thickness of the high-quality shale can be calculated by the time-thickness method. In addition, the change in the thickness of the high-quality shale can be indicated by the number of positive points.

[0090] Step 304: Generate a sensitivity factor corresponding to the first type of rock based on the attribute characteristics and the first data volume.

[0091] On the reconstructed data volume, the high-quality shale response sensitivity factor is extracted to indicate the lateral distribution of high-quality shale thickness. Figure 8 , which shows a plane diagram 800 of the predicted thickness of high-quality shale from the Longyi Submember of the Longmaxi Formation of the Silurian System to the Wufeng Formation in the L203 well area, and shows the corresponding lateral distribution of high-quality shale. The dotted area 810 in the figure is an area with a large thickness of high-quality shale, which shows that the thickness of high-quality shale is relatively continuous and stable.

[0092] Step 305: Determine thickness data corresponding to the first type of rock based on the sensitivity factor.

[0093] In one example, after tracking the top of high-quality shale, the thickness of high-quality shale = (time value corresponding to the trough - time value of the bottom boundary of the Wufeng Formation) * layer velocity / 2; or positive points are extracted to indicate the thickness of high-quality shale, and the time window is the time point range from the bottom of the Wufeng Formation to the first maximum trough upwards.

[0094] like Figure 9 As shown, it shows a flow chart of the high-quality shale thickness identification method in an embodiment of the present application, namely, reading three-dimensional seismic data 901; performing spectrum analysis along the target layer 902; constructing a wavelet library, using the matching pursuit method to remove the correlated wavelet with the largest positive polarity representing the lithologic interface response, and obtaining a first data body 903; typical well analysis in the work area and well curve preprocessing 904; model forward modeling comparison analysis: original model forward modeling, model forward modeling with lithologic interface removed 905; constructing a sensitive factor characterizing the high-quality shale thickness 906; high-quality shale thickness prediction and effect analysis 907.

[0095] In summary, the rock thickness identification method provided in the present application generates a sensitivity factor corresponding to the first type of rock through a preset model based on post-stack seismic data and target drilling data, and determines the thickness data corresponding to the first type of rock based on the sensitivity factor, wherein the sensitivity factor is used to indicate the lateral distribution corresponding to the thickness of the first type of rock, that is, the lateral distribution corresponding to the thickness of the first type of rock can be quickly identified through the post-stack seismic data, shortening the corresponding rock measurement time and improving the efficiency of rock thickness identification.

[0096] Please refer to Figure 10 , which shows a block diagram of a rock thickness identification device provided by an exemplary embodiment of the present application. The rock thickness identification device can be implemented as all or part of a computer device through software, hardware, or a combination of both. The device includes:

[0097] An acquisition module 1010 is configured to acquire post-stack seismic data and target drilling data, wherein the post-stack seismic data is obtained by processing the original acquisition data, and the target drilling data is used to represent the distribution of drilling wells in the target rock group, wherein the target rock group includes the first type of rock;

[0098] A generating module 1020 is configured to generate a sensitivity factor corresponding to the first type of rock based on the post-stack seismic data and the target drilling data, wherein the sensitivity factor is used to indicate a lateral distribution of the thickness of the first type of rock;

[0099] The determination module 1030 is configured to determine thickness data corresponding to the first type of rock based on the sensitivity factor.

[0100] In an optional embodiment, the target rock group further includes a second type of rock;

[0101] The generating module 1020 is further configured to generate a first data volume based on the post-stack seismic data, wherein the first data volume is configured to represent the degree of difference between the first type of rock and the second type of rock;

[0102] The generating module 1020 is further configured to generate attribute characteristics of the first type of rock based on the target drilling data, wherein the attribute characteristics are used to identify the thickness of the first type of rock;

[0103] The generating module 1020 is further configured to generate a sensitivity factor corresponding to the first type of rock based on the attribute characteristics and the first data body.

[0104] In an optional embodiment, the target rock group includes an interfering rock formation;

[0105] The generating module 1020 is further configured to remove the lithologic interface reflection corresponding to the interfering rock layer in the post-stack seismic data to obtain the first data volume.

[0106] In an alternative embodiment, please refer to Figure 11 , the target rock group includes a target rock formation;

[0107] The device further comprises:

[0108] An analysis module 1040 is configured to perform spectrum analysis on the target rock formation based on the post-stack seismic data to obtain analysis results;

[0109] The generating module 1020 is further configured to construct a wavelet library corresponding to the target rock group based on the analysis results, wherein the wavelet library is configured to characterize the seismic signal characteristics in the target rock layer;

[0110] The generating module 1020 is further configured to remove the wavelet corresponding to the interfering rock layer in the wavelet library to obtain the first data volume.

[0111] In an optional embodiment, the seismic data processing includes at least one processing method of spherical divergence correction processing, Q compensation processing for formation absorption, amplitude processing, wavelet deconvolution processing, surface consistency static correction processing, velocity analysis processing, dynamic correction processing, and residual static correction processing.

[0112] In an optional real-time, the generating module 1020 is further configured to generate the P-wave velocity and density parameters corresponding to the target rock group based on the target drilling data;

[0113] The device further comprises:

[0114] The determining module 1030 is further configured to determine a first forward analysis result based on the longitudinal wave velocity and the density parameter;

[0115] The determining module 1030 is further configured to determine a second forward analysis result based on the first data volume;

[0116] The determination module 1030 is further configured to compare the first forward analysis result with the second forward analysis result to determine the attribute feature.

[0117] In an optional embodiment, the device further comprises:

[0118] A tracking module 1050 is configured to track a rock top position corresponding to the first type of rock based on the sensitive factor;

[0119] The determination module 1030 is further configured to determine the thickness data based on the rock top position and the original acquired data.

[0120] In summary, the rock thickness identification device provided in the embodiment of the present application generates a sensitivity factor corresponding to the first type of rock through a preset model based on post-stack seismic data and target drilling data, and determines the thickness data corresponding to the first type of rock based on the sensitivity factor, wherein the sensitivity factor is used to indicate the lateral distribution corresponding to the thickness of the first type of rock, that is, the lateral distribution corresponding to the thickness of the first type of rock is quickly identified through the post-stack seismic data, shortening the corresponding rock measurement time and improving the efficiency of rock thickness identification.

[0121] It should be noted that the rock thickness identification device provided in the above embodiment is merely an example of the division of the aforementioned functional modules. In actual applications, the aforementioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the virtual environment-based prop usage device and the virtual environment-based prop usage method embodiment provided in the above embodiment are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0122] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0123] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A rock thickness identification method, characterized in that: Applied to a computer device, the method comprises: Acquiring post-stack seismic data and target drilling data, wherein the post-stack seismic data is obtained by processing original acquisition data corresponding to the target rock group, and the target drilling data is used to represent the distribution of drilling wells in the target rock group, wherein the target rock group includes a first type of rock and a second type of rock, wherein the first type of rock is high-quality shale in the reservoir, and the second type of rock is low-quality shale; generating a first data volume based on the post-stack seismic data, wherein the first data volume is used to represent the degree of difference between the first type of rock and the second type of rock; generating compressional wave velocity and density parameters corresponding to the target rock group based on the target drilling data; Determining a first forward analysis result based on the longitudinal wave velocity and the density parameter; determining a second forward analysis result based on the first data volume; Comparing the first forward analysis result and the second forward analysis result to determine an attribute feature, wherein the attribute feature is used to identify the thickness of the first type of rock; generating a sensitivity factor corresponding to the first type of rock based on the attribute characteristics and the first data volume, wherein the sensitivity factor is used to indicate a lateral distribution of the thickness of the first type of rock; Thickness data corresponding to the first type of rock is determined based on the sensitivity factor.

2. The method according to claim 1, characterized in that The target rock group includes disturbed rock formations; The step of generating a first data volume based on the post-stack seismic data comprises: The first data volume is obtained by removing the lithologic interface reflection corresponding to the interfering rock layer from the post-stack seismic data.

3. The method according to claim 2, characterized in that The target rock group includes a target rock formation; The step of removing the lithologic interface reflection corresponding to the interfering rock layer from the post-stack seismic data to obtain the first data volume includes: Based on the post-stack seismic data, performing spectrum analysis on the target rock formation to obtain analysis results; Based on the analysis results, a wavelet library corresponding to the target rock group is constructed, wherein the wavelet library is used to characterize the characteristics of the seismic signal in the target rock layer; The wavelet corresponding to the interfering rock layer in the wavelet library is removed to obtain the first data volume.

4. The method according to claim 1, wherein The seismic data processing includes at least one processing method of spherical divergence correction processing, Q compensation processing for formation absorption, amplitude processing, wavelet deconvolution processing, surface consistency static correction processing, velocity analysis processing, dynamic correction processing, and residual static correction processing.

5. The method according to any one of claims 1 to 4, characterized in that: The determining of thickness data corresponding to the first type of rock based on the sensitive factor includes: tracking the rock top position corresponding to the first type of rock based on the sensitive factor; The thickness data is determined based on the rock roof position and the original acquired data.

6. A rock thickness identification device, characterized in that: The device comprises: an acquisition module, configured to acquire post-stack seismic data and target drilling data, wherein the post-stack seismic data is obtained by processing the original acquisition data, and the target drilling data is used to represent the distribution of drilling wells in the target rock group, wherein the target rock group includes a first type of rock and a second type of rock, wherein the first type of rock is high-quality shale in the reservoir, and the second type of rock is low-quality shale; a generation module configured to generate a first data volume based on the post-stack seismic data, the first data volume being used to represent the degree of difference between the first type of rock and the second type of rock; generate P-wave velocity and density parameters corresponding to the target rock group based on the target drilling data; determine a first forward analysis result based on the P-wave velocity and the density parameter; determine a second forward analysis result based on the first data volume; compare the first forward analysis result with the second forward analysis result to determine an attribute feature, the attribute feature being used to identify the thickness of the first type of rock; and generate a sensitivity factor corresponding to the first type of rock based on the attribute feature and the first data volume, the sensitivity factor being used to indicate the lateral distribution of the thickness of the first type of rock; A determination module is used to determine thickness data corresponding to the first type of rock based on the sensitivity factor.

7. A computer device, characterized in that: The computer device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the rock thickness identification method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores at least one program code, and the program code is loaded and executed by the processor to implement the rock thickness identification method according to any one of claims 1 to 5.

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