High-precision thin reservoir prediction method, system, medium and equipment

Abstract

The invention provides a high-precision thin reservoir prediction method and system, a medium and equipment, and belongs to the field of geophysical reservoir prediction. According to the method, firstly, the imaging homing problem of a high and steep structure is effectively solved through pre-stack depth migration; secondly, fine horizon interpretation is carried out on a target layer section, and besides the top and the bottom of a target layer, fine comparison tracking of inner small layers should be carried out; in addition, a high-precision initial model is constructed for a research area with few drilling wells, on the basis of fine small layer interpretation, interpolation is carried out between small layers in a waveform comparison tracking mode, and meanwhile the seismic data sampling rate is increased; and finally, a deterministic time-frequency domain joint inversion method is adopted, and the resolution of an inversion result is improved by adding frequency domain rate information. According to the method, processing and interpretation are integrated, high-precision imaging processing is converted into high-precision seismic data interpretation, thin-layer high-precision prediction for high and steep structures and areas with less drilling data is achieved, and reservoir prediction precision is effectively improved.

Description

Technical Field

[0001] The present invention belongs to the field of geophysical reservoir prediction, and in particular relates to a high-precision thin reservoir prediction method, system, medium and equipment. Background Art

[0002] With the gradual deepening of oil and gas exploration and development, the demand for the development of thin reservoirs is increasing, which puts higher demands on geophysicists for the prediction of thin layers. However, the resolution of conventional seismic inversion technology is limited, so it is necessary to find a suitable high-resolution and high-precision seismic inversion method.

[0003] Seismic inversion relies on seismic data, so it is equally important to ensure high-quality seismic data. At present, the prediction methods for thin reservoirs are mainly based on geostatistics, and the research mainly focuses on vertical resolution, lateral continuity, and computational efficiency. However, these methods are applicable to the special problems of specific research areas, such as high and steep structures and few wells. Summary of the invention

[0004] The purpose of the present invention is to solve the problems existing in the above-mentioned prior art and to provide a high-precision thin reservoir prediction method, system, medium and equipment, aiming to solve the problem of difficulty in predicting thin reservoirs due to high and steep formations and few drilled wells.

[0005] One of the purposes of the present invention is to provide a high-precision thin reservoir prediction method.

[0006] A second object of the present invention is to provide a high-precision thin reservoir prediction system.

[0007] A third object of the present invention is to provide a computer-readable storage medium.

[0008] A fourth object of the present invention is to provide a computer device.

[0009] The present invention is achieved through the following technical solutions:

[0010] A first aspect of the present invention provides a high-precision thin reservoir prediction method, the method comprising the following steps:

[0011] Step 1, high-precision imaging processing;

[0012] Step 2, detailed seismic horizon interpretation;

[0013] Step 3, construct a high-precision initial model;

[0014] Step 4: Thin reservoir prediction.

[0015] A further improvement of the present invention is:

[0016] The high-precision imaging processing in step 1 specifically includes:

[0017] Firstly, the target work area's landmark horizons are preliminarily interpreted and calibrated to determine the approximate structural morphology. On this basis, the velocity model is constructed by interpolation using the horizon constraints and combined with logging data.

[0018] Considering the abnormal structural factors, the obtained velocity model is smoothed at the corresponding scale, and the model is used for grid tomography velocity inversion to obtain the final prestack depth migration imaging results.

[0019] A further improvement of the present invention is:

[0020] The detailed seismic horizon interpretation in step 2 includes the following specific operations:

[0021] Performing deep-time conversion on the prestack depth migration imaging result obtained in step 1 to obtain relatively accurate prestack time migration seismic data;

[0022] Conduct fine-scale stratigraphic interpretation of target layers. For complex structural areas, use a grid no larger than 5×5 to interpret lines, traces and quality control lines.

[0023] In addition to the top and bottom of the target layer, detailed comparative tracking of the internal small layers should also be included.

[0024] A further improvement of the present invention is:

[0025] In step 3, a high-precision initial model is constructed, and the specific operations include:

[0026] Interpolation is performed between small layers by waveform contrast and tracking. First, the stratigraphic interface at a certain time point in the top layer is selected, and the stratigraphic interface corresponding to the adjacent seismic traces is determined based on the seismic data waveform similarity analysis in the relevant time window, and the interpolated layers in the top layer are obtained in turn.

[0027] A further improvement of the present invention is:

[0028] The step 3 of constructing a high-precision initial model also includes:

[0029] Increase the sampling rate of seismic data.

[0030] A further improvement of the present invention is:

[0031] The thin reservoir prediction in step 4 is specifically as follows:

[0032] The deterministic time-frequency domain joint inversion method is adopted to improve the resolution of the inversion result by adding the frequency domain rate information, so as to realize the prediction of thin reservoirs.

[0033] A further improvement of the present invention is:

[0034] The objective function of the deterministic time-frequency domain joint inversion is:

[0035]

[0036] in,

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043] F(m) is the inversion objective function, d t and d f are the observed seismic data in the time domain and frequency domain respectively, G t and G f are the joint matrices of coupled wavelets and coefficient matrices in the time domain and frequency domain, respectively, c t and c f are the weight factors of time domain seismic data and frequency domain seismic data, respectively, p , w s , w d are the weight coefficients of the low-frequency model constraints for P-wave velocity, S-wave velocity and density, respectively.

[0044] A second aspect of the present invention provides a high-precision thin reservoir prediction system, comprising:

[0045] An imaging processing unit, used for high-precision imaging processing;

[0046] Horizon interpretation unit, used for fine seismic horizon interpretation;

[0047] A model building unit, used to build a high-precision initial model;

[0048] Prediction unit, used for thin reservoir prediction.

[0049] According to a third aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores at least one computer-executable program, and when the at least one program is executed by the computer, the computer executes the steps in the high-precision thin reservoir prediction method as described above.

[0050] A fourth aspect of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the high-precision thin reservoir prediction method as described above.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] The present invention provides a high-precision thin reservoir prediction method, which realizes the fine prediction of thin reservoirs of high-steep structures through four steps: high-precision imaging processing, fine seismic horizon interpretation, high-precision initial model establishment and thin reservoir prediction. First, the imaging homing problem of high-steep structures is effectively solved by pre-stack depth migration, wherein in order to improve the accuracy of the velocity model, the structural morphology is combined with the logging data for iterative update; secondly, the target layer segment is finely interpreted, and in addition to the top and bottom of the target layer, the fine comparison and tracking of the internal small layers should also be included; in addition, a high-precision initial model is constructed for the research area with few wells, and on the basis of the fine small layer interpretation, the small layers are interpolated by waveform comparison and tracking, and the sampling rate of seismic data is improved at the same time; finally, a deterministic time-frequency domain joint inversion method is adopted to improve the resolution of the inversion result by adding frequency domain rate information.

[0053] The present invention integrates processing and interpretation, from high-precision imaging processing to high-precision seismic data interpretation, to achieve high-precision prediction of thin layers in high-steep structures and areas with less drilling data, effectively improving the accuracy of reservoir prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a flow chart of a high-precision thin reservoir prediction method of the present invention;

[0055] Figure 2 It is the data converted from prestack depth migration to time domain in a certain study area;

[0056] Figure 3 It is the inversion result of P-wave velocity obtained based on the conventional inversion method;

[0057] Figure 4 It is a high-precision longitudinal wave velocity inversion result obtained based on the present invention. DETAILED DESCRIPTION

[0058] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0059] [Example 1]

[0060] The embodiment of the present invention provides a high-precision thin reservoir prediction method, such as Figure 1 As shown, the method comprises the following steps:

[0061] Step 1, high-precision imaging processing:

[0062] The low signal-to-noise ratio and inaccurate imaging of seismic data in high-steep structures will lead to failure in subsequent drilling, so it is very important to ensure high-precision seismic data.

[0063] The pre-stack depth migration processing method can effectively solve the imaging homing problem of high-steep structures, in which the accurate establishment of the velocity model is the key. In order to improve the accuracy of the velocity model, the structural morphology is combined with the logging data for iterative update.

[0064] The structural morphology is combined with logging data to perform iterative updating and high-precision imaging processing. Specifically, the landmark layers of the target work area are preliminarily interpreted and calibrated to determine the approximate structural morphology. On this basis, the velocity model is interpolated and constructed using the layer constraints and logging data. In addition, factors such as abnormal structures are considered and the obtained velocity model is smoothed to the corresponding scale. The model is used for subsequent grid tomography velocity inversion to obtain the final pre-stack depth migration imaging result.

[0065] Step 2, detailed seismic horizon interpretation:

[0066] After obtaining relatively accurate prestack depth migration seismic results, deep-time conversion is performed to obtain relatively accurate prestack time migration seismic data. In order to improve the prediction accuracy of thin layers, fine stratigraphic interpretation is performed on the target layer segment. For complex structural areas, a grid of no more than 5×5 is used for line, trace and quality control line interpretation. In addition to the top and bottom of the target layer, fine comparative tracking of internal small layers should also be included. Especially for high and steep structural areas, if there are geological interfaces such as onlap and truncation, further refined interpretation is required. This involves understanding the early geological sedimentary environment, comparing and tracking waveform characteristics for different sedimentary interfaces, determining the seismic reflection interface, and then interpreting the small layers.

[0067] Step 3: Establishment of high-precision initial model:

[0068] Based on the detailed interpretation of small layers, an initial model is constructed for the study area with few wells. There are two aspects that need to be noted here:

[0069] First, in the high and steep parts, if there are few logging data, it is easy to penetrate the layers during the extrapolation and interpolation process, which will affect the accuracy of subsequent reservoir prediction. Here, we consider interpolating between small layers by waveform comparison and tracking based on the fine interpretation of small layers. First, we select the stratigraphic interface at a certain time point in the top and bottom layers, and determine the stratigraphic interface corresponding to the adjacent seismic traces based on the seismic data waveform similarity analysis in the relevant time window, and then obtain the interpolated layers in the top and bottom layers in turn;

[0070] On this basis, a steep-angle fine stratigraphic framework is constructed, and then the logging data is interpolated and extrapolated to improve the model accuracy. Secondly, the resolution of logging data is high, and the resolution of seismic data is relatively low. In order to improve the model resolution, the sampling rate of seismic data can be increased from the conventional 2ms to 0.5ms. In this way, for a formation with a velocity of 5000m / s, the theoretical thin layer recognition thickness can be up to 2.5m.

[0071] Step 4, thin reservoir prediction:

[0072] Conventional geostatistical methods have certain requirements on the number of wells that have been drilled. The present invention adopts a deterministic time-frequency domain joint inversion method to improve the resolution of the inversion result by adding frequency domain rate information. This method integrates the influence of time domain and frequency domain information in the construction of the inversion objective function, assumes that the prior information and likelihood function of the parameters to be inverted obey the Cauchy and Gaussian distributions respectively, considers the correlation between the parameters to be inverted, and adopts low-frequency model constraints. This method improves the inversion resolution while having high noise resistance, and can be used as a powerful technical means for reservoir prediction and oil and gas detection.

[0073] The objective function of AVA inversion in the time-frequency domain is:

[0074]

[0075] in,

[0076]

[0077]

[0078]

[0079]

[0080]

[0081]

[0082] F(m) is the inversion objective function, d t and d f are the observed seismic data in the time domain and frequency domain respectively, G t and G f are the joint matrices of coupled wavelets and coefficient matrices in the time domain and frequency domain, respectively, c t and c f are the weight factors of time domain seismic data and frequency domain seismic data, respectively, p , w s , w d are the weight coefficients of the low-frequency model constraints for P-wave velocity, S-wave velocity and density, respectively.

[0083] The inversion objective function formula includes six items, among which the first item is the difference between the time domain observation data and the forward modeling results; the second item is the difference between the frequency domain seismic data and the forward modeling results; the third item is the Cauchy constraint item; the fourth, fifth and sixth items are the P-wave velocity, S-wave velocity and density low-frequency model constraint items respectively.

[0084] [Example 2]

[0085] This embodiment takes a certain study area as an example and uses the method provided by the present invention to perform high-precision thin reservoir prediction.

[0086] As described in step 1, high-precision imaging processing: The low signal-to-noise ratio of seismic data of high-steep structures and inaccurate imaging homing will lead to failure in later drilling, so it is very important to ensure high-precision seismic data. Prestack depth migration can effectively solve the imaging homing problem of high-steep structures, among which the accurate establishment of the velocity model is the key. In order to improve the accuracy of the velocity model, the structural morphology is combined with logging data for iterative update.

[0087] like Figure 2 As shown in the figure, the pre-stack depth migration data of the study area is converted to the time domain data. The structural burial depths in this work area vary greatly, with a depth variation range of about 700-4100m. It is a steep-angle structure. The high-steep structural morphology can be effectively restored through high-precision imaging migration processing methods.

[0088] As described in step 2, fine seismic stratigraphic interpretation: After obtaining relatively accurate seismic data, in order to improve the accuracy of thin layer prediction, a fine stratigraphic interpretation is performed on the target layer segment. In addition to the top and bottom of the target layer, it should also include fine comparative tracking of small internal layers, especially for high and steep structural areas. If there are geological interfaces such as onlap and truncation, further fine interpretation is required.

[0089] As described in step 3, the high-precision initial model is established: on the basis of fine interpretation of small layers, the initial model is constructed for the research area with few wells. There are two aspects that need to be noted here. First, in the high and steep parts, if there are few logging data, it is easy to penetrate the layers during the extrapolation and interpolation process, which affects the subsequent reservoir prediction accuracy. Here, based on the fine interpretation of small layers, the small layers are interpolated by waveform contrast tracking to build a steep angle fine stratigraphic framework, and then the logging data is interpolated and extrapolated to improve the model accuracy; secondly, the resolution of logging data is high, and the resolution of seismic data is relatively low. In order to improve the model resolution, the sampling rate of seismic data can be increased from the conventional 2ms to 0.5ms. In this way, for the formation with a velocity of 5000m / s, the theoretical thin layer recognition thickness can be up to 2.5m.

[0090] According to the structural morphology of the study area, taking into account the large changes in stratum dip angles and the development of high-steep structures, the stratigraphic framework of the low-frequency model adopts small-layer constraints to improve the prediction accuracy of apparent thickness in high-steep structural areas.

[0091] As described in step 4, thin reservoir prediction: conventional geostatistical methods have certain requirements on the number of wells that have been drilled. Here, a deterministic time-frequency domain joint inversion method is used to improve the resolution of the inversion result by adding frequency domain rate information. This method integrates the influence of time domain and frequency domain information in the construction of the inversion objective function, assumes that the prior information and likelihood function of the parameters to be inverted obey the Cauchy and Gaussian distributions respectively, considers the correlation between the parameters to be inverted, and uses low-frequency model constraints. This method improves the inversion resolution while having high noise resistance, and can be used as a powerful technical means for reservoir prediction and oil and gas detection.

[0092] like Figure 3 and Figure 4 As shown, the P-wave velocity inversion result obtained based on the conventional inversion method and the high-precision P-wave velocity inversion result obtained based on the present invention, wherein the seismic data used by the two methods are consistent, but the former does not adopt high-precision modeling and inversion methods, while the latter uses the method of the present invention. It can be seen that compared with the conventional method, the means provided by the present invention is more applicable to high-steep strata, the strata are stably distributed, and are in line with the geological sedimentary environment.

[0093] [Example 3]

[0094] The embodiment of the present invention provides a high-precision thin reservoir prediction system, comprising:

[0095] The imaging processing unit is used for high-precision imaging processing and performs the following operations:

[0096] Firstly, the target work area's landmark horizons are preliminarily interpreted and calibrated to determine the approximate structural morphology. On this basis, the velocity model is constructed by interpolation using the horizon constraints and combined with logging data.

[0097] Considering the abnormal structural factors, the obtained velocity model is smoothed at the corresponding scale, and the model is used for grid tomography velocity inversion to obtain the final prestack depth migration imaging results.

[0098] The horizon interpretation unit is used for fine seismic horizon interpretation and performs the following operations:

[0099] After obtaining relatively accurate prestack depth migration seismic results, deep-time conversion is performed to obtain relatively accurate prestack time migration seismic data. In order to improve the prediction accuracy of thin layers, fine stratigraphic interpretation is performed on the target layer segment. For complex structural areas, a grid of no more than 5×5 is used for line, trace and quality control line interpretation. In addition to the top and bottom of the target layer, fine comparative tracking of internal small layers should also be included. Especially for high and steep structural areas, if there are geological interfaces such as onlap and truncation, further refined interpretation is required. This involves understanding the early geological sedimentary environment, comparing and tracking waveform characteristics for different sedimentary interfaces, determining the seismic reflection interface, and then interpreting the small layers.

[0100] The model building unit is used to build a high-precision initial model and perform the following operations:

[0101] Based on the detailed interpretation of small layers, an initial model is constructed for the study area with few wells. There are two aspects that need to be noted here:

[0102] First, in the high and steep parts, if there are few logging data, it is easy to penetrate the layers during the extrapolation and interpolation process, which will affect the accuracy of subsequent reservoir prediction. Here, we consider interpolating between small layers by waveform comparison and tracking based on the fine interpretation of small layers. First, we select the stratigraphic interface at a certain time point in the top and bottom layers, and determine the stratigraphic interface corresponding to the adjacent seismic traces based on the seismic data waveform similarity analysis in the relevant time window, and then obtain the interpolated layers in the top and bottom layers in turn;

[0103] On this basis, a steep-angle fine stratigraphic framework is constructed, and then the logging data is interpolated and extrapolated to improve the model accuracy. Secondly, the resolution of logging data is high, and the resolution of seismic data is relatively low. In order to improve the model resolution, the sampling rate of seismic data can be increased from the conventional 2ms to 0.5ms. In this way, for a formation with a velocity of 5000m / s, the theoretical thin layer recognition thickness can be up to 2.5m.

[0104] The prediction unit, used for thin reservoir prediction, performs the following operations:

[0105] The deterministic time-frequency domain joint inversion method is adopted to improve the resolution of the inversion result by adding frequency domain rate information. This method integrates the influence of time domain and frequency domain information in the construction of the inversion objective function, assumes that the prior information and likelihood function of the parameters to be inverted obey the Cauchy and Gaussian distributions respectively, considers the correlation between the parameters to be inverted, and adopts low-frequency model constraints. This method improves the inversion resolution while having high noise resistance, and can be used as a powerful technical means for reservoir prediction and oil and gas detection.

[0106] The objective function of AVA inversion in the time-frequency domain is:

[0107]

[0108] in,

[0109]

[0110]

[0111]

[0112]

[0113]

[0114]

[0115] F(m) is the inversion objective function, d t and d f are the observed seismic data in the time domain and frequency domain respectively, G t and G f are the joint matrices of coupled wavelets and coefficient matrices in the time domain and frequency domain, respectively, c t and c f are the weight factors of time domain seismic data and frequency domain seismic data, respectively, p , w s , w d are the weight coefficients of the low-frequency model constraints for P-wave velocity, S-wave velocity and density, respectively.

[0116] The inversion objective function formula includes six items, among which the first item is the difference between the time domain observation data and the forward modeling results; the second item is the difference between the frequency domain seismic data and the forward modeling results; the third item is the Cauchy constraint item; the fourth, fifth and sixth items are the P-wave velocity, S-wave velocity and density low-frequency model constraint items respectively.

[0117] [Example 4]

[0118] An embodiment of the present invention provides a computer-readable storage medium, which stores at least one computer-executable program. When the at least one program is executed by the computer, the computer executes the steps in the high-precision thin reservoir prediction method described in Example 1.

[0119] [Example 5]

[0120] An embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the high-precision thin reservoir prediction method as described in Example 1.

[0121] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0122] The above technical solution is only one implementation mode of the present invention. For those skilled in the art, it is easy to make various types of improvements or modifications based on the principles disclosed in the present invention, and it is not limited to the technical solution described in the above specific embodiments of the present invention. Therefore, the above description is only preferred and does not have a restrictive meaning.

Claims

1. A high-precision thin reservoir prediction method, characterized in that: The method comprises the following steps: Step 1, high-precision imaging processing; Step 2, detailed seismic horizon interpretation; Step 3, construct a high-precision initial model; Step 4: Thin reservoir prediction.

2. The high-precision thin reservoir prediction method according to claim 1, characterized in that: The high-precision imaging processing in step 1 specifically includes: Firstly, the target work area's landmark horizons are preliminarily interpreted and calibrated to determine the approximate structural morphology. On this basis, the velocity model is constructed by interpolation using the horizon constraints and combined with logging data. Considering the abnormal structural factors, the obtained velocity model is smoothed at the corresponding scale, and the model is used for grid tomography velocity inversion to obtain the final prestack depth migration imaging results.

3. The high-precision thin reservoir prediction method according to claim 2, characterized in that: The detailed seismic horizon interpretation in step 2 includes the following specific operations: Performing deep-time conversion on the prestack depth migration imaging result obtained in step 1 to obtain relatively accurate prestack time migration seismic data; Conduct fine-scale stratigraphic interpretation of target layers. For complex structural areas, use a grid no larger than 5×5 to interpret lines, traces and quality control lines. In addition to the top and bottom of the target layer, detailed comparative tracking of the internal small layers should also be included.

4. The high-precision thin reservoir prediction method according to claim 3, characterized in that: In step 3, a high-precision initial model is constructed, and the specific operations include: Interpolation is performed between small layers by waveform contrast and tracking. First, the stratigraphic interface at a certain time point in the top layer is selected, and the stratigraphic interface corresponding to the adjacent seismic traces is determined based on the seismic data waveform similarity analysis in the relevant time window, and the interpolated layers in the top layer are obtained in turn.

5. The high-precision thin reservoir prediction method according to claim 4, characterized in that: The step 3 of constructing a high-precision initial model also includes: Increase the sampling rate of seismic data.

6. The high-precision thin reservoir prediction method according to claim 5, characterized in that: The thin reservoir prediction in step 4 is specifically as follows: The deterministic time-frequency domain joint inversion method is adopted to improve the resolution of the inversion result by adding the frequency domain rate information, so as to realize the prediction of thin reservoirs.

7. The high-precision thin reservoir prediction method according to claim 6, characterized in that: The objective function of the deterministic time-frequency domain joint inversion is: in, F(m) is the inversion objective function, d t and d f are the observed seismic data in the time domain and frequency domain respectively, G t and G f are the joint matrices of coupled wavelets and coefficient matrices in the time domain and frequency domain, respectively, c t and c f are the weight factors of time domain seismic data and frequency domain seismic data, respectively, p , w s , w d are the weight coefficients of the low-frequency model constraints for P-wave velocity, S-wave velocity and density, respectively.

8. A high-precision thin reservoir prediction system, characterized in that: include: An imaging processing unit, used for high-precision imaging processing; Horizon interpretation unit, used for fine seismic horizon interpretation; A model building unit, used to build a high-precision initial model; Prediction unit, used for thin reservoir prediction.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one computer-executable program, and when the at least one program is executed by the computer, the computer executes the steps in the high-precision thin reservoir prediction method according to any one of claims 1 to 7.

10. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the high-precision thin reservoir prediction method according to any one of claims 1 to 7.

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