A reservoir identification method, apparatus, storage medium, and electronic device

Abstract

This disclosure presents a reservoir identification method. The method includes: acquiring a set of seismic data and a set of well logging data for the target reservoir; acquiring seismic data and well logging data; acquiring first well seismic calibration results based on the seismic data and well logging data; acquiring first waveform indication inversion results based on the first well seismic calibration results, seismic data, and well logging data; acquiring second well seismic calibration results and second waveform indication inversion results based on the first waveform indication inversion results, seismic data, and well logging data; and acquiring distribution data of the target reservoir based on the second waveform indication inversion results, the set of seismic data, and the set of well logging data. This method improves the correlation between seismic clustering waveforms and well logging curves, enhances the accuracy and resolution of prediction results, effectively eliminates interference from non-target reservoir factors in the seismic data, and can accurately distinguish reservoirs with a thickness of approximately 3 meters in thin / extremely thin layer inversion.

Description

Technical Field

[0001] This disclosure relates to the field of oil and gas geophysics, and in particular to a reservoir identification method, apparatus, storage medium, and electronic equipment. Background Technology

[0002] As exploration in basins and oilfields progresses, some oil and gas fields with distinct structural features, thick reservoirs, and large resources have been discovered and developed. Currently, the development of unconventional shale oil and gas reservoirs and thin conventional oil and gas layers is gradually becoming an important area of ​​development. However, in the development of these reservoirs, due to their thinness, rapid lateral changes, and especially the presence of thin-layered sandstone / carbonate rocks in shale oil and gas, reservoir identification is difficult, and there is no particularly reliable technology for sweet spot identification.

[0003] Seismic inversion technology is an effective means of identifying reservoirs on a planar or regional scale, but due to resolution limitations, it is difficult to distinguish thin layers with a thickness of less than 10 meters. Currently, the most commonly used seismic inversion techniques for reservoir prediction mainly fall into four categories:

[0004] The first type is geophysical inversion based on convolution models. However, geophysical inversion based on convolution models cannot overcome the limitations of seismic resolution; its minimum resolvable thickness can only reach 10–20 meters.

[0005] The second category is nonlinear inversion techniques, which mainly include neural networks, support vector machines, Bayesian methods, pattern recognition, and genetic algorithms. Although the solutions obtained by nonlinear inversion are often not globally optimal, and the inversion results are often mathematical calculations, they are less accurate in reflecting the regularity of geological reservoir distribution. They tend to produce better results at well-constrained locations and worse results at locations without well constraints.

[0006] The third type is geostatistical inversion, which improves vertical resolution through stochastic simulation. Because most of the high-frequency components in geostatistical inversion come from stochastic simulation, it suffers from problems such as high randomness in the inversion results and low horizontal resolution.

[0007] The fourth type utilizes seismic waveform constraints to establish a high-precision inversion initial model, and then uses high-resolution inversion by model waveform imitation to achieve waveform-indicating reservoir prediction for thin reservoirs. However, currently, the waveform classification results of the original seismic data during the waveform indication process are still affected by the quality of the data itself, and a frequency division model cannot be established. Furthermore, the accuracy in establishing well-seismic relationships needs further improvement to more accurately predict the spatial properties of reservoirs. Summary of the Invention

[0008] To address the problem that the four existing reservoir inversion techniques are limited by the algorithms or methods themselves and have difficulty identifying reservoirs with a thickness of less than 10 meters, this disclosure proposes a reservoir identification method, device, storage medium, and electronic equipment. It improves the waveform indication inversion method, which can effectively identify reservoirs with a thickness of less than 10 meters, thereby improving the accuracy and operability of reservoir identification.

[0009] A first aspect of this disclosure provides a reservoir identification method, the method comprising:

[0010] Acquire the seismic and well logging datasets of the target reservoir;

[0011] Obtain the seismic data and well logging data of each well in the aforementioned seismic data set and well logging data set;

[0012] Based on the seismic data and logging data of each well, the first well seismic calibration result data of each well is obtained;

[0013] Based on the first well seismic calibration result data of each well, the seismic data and logging data of each well, the first waveform indication inversion result data of each well is obtained;

[0014] Based on the first waveform indication inversion result data of each well, the seismic data and logging data of each well, the second well seismic calibration result data of each well is obtained respectively;

[0015] Based on the second well seismic calibration results of each well, the seismic data and logging data of each well, the second waveform indication inversion results of each well are obtained respectively;

[0016] Based on the second waveform indication inversion results of each well, the seismic data set, and the well logging data set, the distribution data of the target reservoir is obtained.

[0017] In some embodiments, obtaining the first well seismic calibration result data for each well based on the seismic data and well logging data of each well includes:

[0018] Based on the earthquake data and well logging data, obtain the first frequency division data and the synthetic seismic record data;

[0019] Based on the first frequency division data and the synthetic seismic record data, the first well seismic calibration result data of each well is obtained.

[0020] In some embodiments, obtaining the first frequency-division data based on the seismic data and well logging data includes:

[0021] Based on the earthquake data, obtain the frequency division result data and earthquake velocity data;

[0022] Based on the logging data, obtain logging curve data and reservoir thickness and range characteristic data;

[0023] The well logging curve data are sequentially subjected to curve correction, curve standardization, and curve sensitivity analysis to obtain the processed well logging curve data.

[0024] The first frequency division data is obtained based on the processed well logging curve data, frequency division result data, reservoir thickness and range characteristic data, and seismic velocity data.

[0025] In some embodiments, the method for obtaining the frequency division result data includes:

[0026] Frequency-division attribute analysis is performed on the earthquake data to obtain the frequency-division result data.

[0027] In some embodiments, obtaining the first waveform indication inversion result data for each well based on the first well seismic calibration result data, the seismic data, and the well logging data for each well includes:

[0028] The first data is obtained by performing spectral simulation inversion on the first well seismic calibration results data;

[0029] Well logging curve data is obtained from the well logging data, and waveform indication inversion is performed on the well logging curve data to obtain the second data;

[0030] A low-frequency model was constructed from the earthquake data to obtain the third data.

[0031] The first data, the second data, and the third data are frequency-merged to obtain the first waveform indication inversion result data for each well.

[0032] In some embodiments, obtaining the second well seismic calibration result data for each well based on the first waveform indication inversion result data, the seismic data, and the well logging data for each well includes:

[0033] Based on the aforementioned seismic data and well logging data, synthetic seismic record data is obtained;

[0034] Based on the first waveform indication inversion result data, well logging data, and seismic data, the second frequency division data is obtained;

[0035] Based on the synthetic seismic record data and the second frequency division data, the second well seismic calibration result data of each well is obtained.

[0036] In some embodiments, obtaining the second frequency-division data based on the first waveform indication inversion result data, well logging data, and seismic data includes:

[0037] Based on the earthquake data, obtain the frequency division result data and earthquake velocity data;

[0038] Obtain well logging curve data based on the well logging data;

[0039] The well logging curve data are sequentially subjected to curve correction, curve standardization, and curve sensitivity analysis to obtain the processed well logging curve data.

[0040] Based on the inversion results data indicated by the first waveform, obtain reservoir thickness and range characteristic data;

[0041] The second frequency division data is obtained based on the processed well logging curve data, frequency division result data, reservoir thickness and range characteristic data, and seismic velocity data.

[0042] In some embodiments, obtaining the second waveform indication inversion result data for each well based on the second well seismic calibration result data, the seismic data, and the logging data of each well includes:

[0043] The second well seismic calibration results of each well are used to perform spectral simulation inversion to obtain the first data;

[0044] Well logging curve data is obtained from the well logging data of each well, and waveform indication inversion is performed on the well logging curve data to obtain the second data;

[0045] A low-frequency model was constructed from the seismic data of each well to obtain the third data.

[0046] The first, second, and third data are frequency-merged to obtain the second waveform indication inversion result data for each well.

[0047] In some embodiments, the method further includes:

[0048] The second well seismic calibration results data for each well are adjusted to the preset accuracy.

[0049] In some embodiments, obtaining the distribution data of the target reservoir based on the second waveform indication inversion result data of each well, the seismic data set, and the well logging data set includes:

[0050] Based on the earthquake data set and the well logging data set, obtain a data set with preset attributes;

[0051] The data set of the preset attributes is classified to obtain the classified attribute data set;

[0052] The classification of attribute data set and the second waveform indication inversion result data of each well are fused and analyzed to obtain the spatial distribution structure of the target reservoir.

[0053] The distribution spatial structure is processed in three-dimensional space to obtain the distribution data of the target reservoir.

[0054] A second aspect of this disclosure provides an apparatus comprising:

[0055] The first acquisition module is used to acquire the seismic data set and well logging data set of the target reservoir;

[0056] The second acquisition module is used to acquire the seismic data and well logging data of each well in the seismic data set and well logging data set;

[0057] The first well seismic calibration result acquisition module is used to acquire the first well seismic calibration result data of each well based on the seismic data and logging data of each well.

[0058] The first waveform indication inversion result acquisition module is used to acquire the first waveform indication inversion result data of each well based on the first well seismic calibration result data of each well, the seismic data and logging data of each well, respectively.

[0059] The second well seismic calibration result acquisition module is used to acquire the second well seismic calibration result data of each well based on the first waveform indication inversion result data of each well, the seismic data and logging data of each well, respectively.

[0060] The second waveform indication inversion result acquisition module is used to acquire the second waveform indication inversion result data of each well based on the second well seismic calibration result data of each well, the seismic data and logging data of each well, respectively.

[0061] The distribution data acquisition module is used to acquire the distribution data of the target reservoir based on the second waveform indication inversion result data of each well, the seismic data set, and the well logging data set.

[0062] A third aspect of this disclosure provides a storage medium storing a computer program that can be executed by one or more processors to implement the reservoir identification method described above.

[0063] A fourth aspect of this disclosure provides an electronic device including a memory and a processor, wherein a computer program is stored on the memory and the processor are communicatively connected to each other, and the computer program, when executed by the processor, implements the reservoir identification method as described above.

[0064] Compared with the prior art, the technical solution disclosed herein has the following advantages or beneficial effects:

[0065] This disclosed method, based on high-resolution waveform indication simulation, improves the correlation between seismic clustering waveforms and well logging curves by repeatedly iterating seismic calibration using frequency-division attribute analysis results instead of the original seismic data and the preliminary results of re-inversion. This, in turn, enhances the accuracy and resolution of prediction results. The resolution of reservoir inversion results is further improved, effectively eliminating interference from non-target reservoir factors in the seismic data. Attribute fusion effectively establishes a unified understanding between seismic and geological information, further improving inversion accuracy. In thin / extremely thin layer inversion, it can accurately distinguish reservoirs with a thickness of approximately 3 meters. Attached Figure Description

[0066] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0067] Figure 1 A flowchart of a reservoir identification method provided in this disclosure embodiment;

[0068] Figure 2 A schematic diagram illustrating a well-seismic calibration result generated based on dominant frequency division data and synthetic seismic record data from a single well, as provided in an embodiment of this disclosure;

[0069] Figure 3 A schematic diagram illustrating a well-seismic calibration result generated from waveform indication inversion results of multiple wells and synthetic seismic records, provided as an embodiment of this disclosure;

[0070] Figure 4 A fusion analysis result diagram provided in this embodiment of the disclosure;

[0071] Figure 5 A schematic diagram illustrating the prediction results of reservoir distribution provided in an embodiment of this disclosure;

[0072] Figure 6 This is a schematic diagram of the structure of a reservoir identification device provided in an embodiment of the present disclosure;

[0073] Figure 7 This is a connection block diagram of an electronic device provided in an embodiment of the present disclosure. Detailed Implementation

[0074] The following detailed description of the embodiments of this disclosure, in conjunction with the accompanying drawings, will provide a thorough understanding of how this disclosure uses technical means to solve technical problems and achieve corresponding technical effects, enabling its implementation. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and all resulting technical solutions are within the protection scope of this disclosure.

[0075] Example 1

[0076] This embodiment provides a reservoir identification method. Figure 1 A flowchart of a reservoir identification method provided in this disclosure embodiment is shown below. Figure 1 As shown, the method in this embodiment includes:

[0077] S110. Obtain the seismic data set and well logging data set of the target reservoir.

[0078] It should be noted that the data set here can refer to a collection of data from multiple wells at the target reservoir.

[0079] S120. Obtain the seismic data and well logging data of each well in the seismic data set and well logging data set.

[0080] In some embodiments, the well logging data includes:

[0081] Well logging curve data, drilling data, and drilling stratification data.

[0082] In some embodiments, the well logging data includes:

[0083] Acoustic transit time curve (AC), natural gamma ray curve (GR), density compensation curve (DEN), and well deviation curve (DEV).

[0084] S130. Based on the seismic data and logging data of each well, obtain the first well seismic calibration result data for each well.

[0085] In some embodiments, obtaining the first well seismic calibration result data for each well based on the seismic data and well logging data of each well includes:

[0086] Based on the earthquake data and well logging data, obtain the first frequency division data and the synthetic seismic record data;

[0087] Based on the first frequency division data and the synthetic seismic record data, the first well seismic calibration result data of each well is obtained.

[0088] In some embodiments, obtaining the first frequency-division data based on the seismic data and well logging data includes:

[0089] Based on the earthquake data, obtain the frequency division result data and earthquake velocity data;

[0090] Based on the logging data, obtain logging curve data and reservoir thickness and range characteristic data;

[0091] The well logging curve data are sequentially subjected to curve correction, curve standardization, and curve sensitivity analysis to obtain the processed well logging curve data.

[0092] The first frequency division data is obtained based on the processed well logging curve data, frequency division result data, reservoir thickness and range characteristic data, and seismic velocity data.

[0093] In some embodiments, the method for obtaining the frequency division result data includes:

[0094] Frequency-division attribute analysis is performed on the earthquake data to obtain the frequency-division result data.

[0095] In some embodiments, the acquisition method of synthetic seismic record data includes:

[0096] The drilling velocity data and density curve data are subjected to forward modeling to obtain the synthetic seismic record data.

[0097] Understandably, seismic data can be simply viewed as a composite of many frequency volumes, where different frequencies have varying resolving power for reservoirs. The relationship between seismic frequency and target reservoir thickness and seismic velocity falls under the category of tuning thickness or maximum resolving power of seismic data. By combining processed well logging data, frequency division results, reservoir thickness and extent characteristics, and seismic velocity data, superior frequency division data can be selected and used as the first frequency division data.

[0098] S140. Based on the first well seismic calibration result data of each well, the seismic data and logging data of each well, respectively, obtain the first waveform indication inversion result data of each well.

[0099] In some embodiments, obtaining the first waveform indication inversion result data for each well based on the first well seismic calibration result data, the seismic data, and the well logging data for each well includes:

[0100] The first data is obtained by performing spectral simulation inversion on the first well seismic calibration results data;

[0101] Well logging curve data is obtained from the well logging data, and waveform indication inversion is performed on the well logging curve data to obtain the second data;

[0102] A low-frequency model was constructed from the earthquake data to obtain the third data.

[0103] The first data, the second data, and the third data are frequency-merged to obtain the first waveform indication inversion result data for each well.

[0104] Optionally, relevant software (such as the thin-layer seismic inversion software SMI) can be used to perform spectral simulation inversion, waveform indication inversion, and low-frequency model construction. The obtained three parts of data are then frequency-merged to obtain preliminary waveform indication inversion results based on the selected seismic frequency division data.

[0105] S150. Based on the first waveform indication inversion result data of each well, the seismic data and logging data of each well, respectively, obtain the second well seismic calibration result data of each well.

[0106] In some embodiments, obtaining the second well seismic calibration result data for each well based on the first waveform indication inversion result data, the seismic data, and the well logging data for each well includes:

[0107] Based on the aforementioned seismic data and well logging data, synthetic seismic record data is obtained;

[0108] Based on the first waveform indication inversion result data, well logging data, and seismic data, the second frequency division data is obtained;

[0109] Based on the synthetic seismic record data and the second frequency division data, the second well seismic calibration result data of each well is obtained.

[0110] In some embodiments, obtaining the second frequency-division data based on the first waveform indication inversion result data, well logging data, and seismic data includes:

[0111] Based on the earthquake data, obtain the frequency division result data and earthquake velocity data;

[0112] Obtain well logging curve data based on the well logging data;

[0113] The well logging curve data are sequentially subjected to curve correction, curve standardization, and curve sensitivity analysis to obtain the processed well logging curve data.

[0114] Based on the inversion results data indicated by the first waveform, obtain reservoir thickness and range characteristic data;

[0115] The second frequency division data is obtained based on the processed well logging curve data, frequency division result data, reservoir thickness and range characteristic data, and seismic velocity data.

[0116] In some embodiments, the method further includes:

[0117] The second well seismic calibration results data for each well are adjusted to the preset accuracy.

[0118] Optionally, the preset accuracy may include enabling the same target reservoir location in all wells to be located on the same phase axis in the seismic data that has the same or similar reflection characteristics and spatial properties, and to be continuously tracked in the three-dimensional seismic data.

[0119] S160. Based on the second well seismic calibration result data of each well, the seismic data and logging data of each well, respectively, obtain the second waveform indication inversion result data of each well.

[0120] In some embodiments, obtaining the second waveform indication inversion result data for each well based on the second well seismic calibration result data, the seismic data, and the logging data of each well includes:

[0121] The second well seismic calibration results of each well are used to perform spectral simulation inversion to obtain the first data;

[0122] Well logging curve data is obtained from the well logging data of each well, and waveform indication inversion is performed on the well logging curve data to obtain the second data;

[0123] A low-frequency model was constructed from the seismic data of each well to obtain the third data.

[0124] The first, second, and third data are frequency-merged to obtain the second waveform indication inversion result data for each well.

[0125] S170. Based on the second waveform indication inversion result data of each well, the seismic data set, and the well logging data set, obtain the distribution data of the target reservoir.

[0126] It should be noted that the above operations need to be performed on all data in the acquired seismic and well logging datasets of the target reservoir, and finally, the second waveform indication inversion result data corresponding to each well should be generated. After all data in the seismic and well logging datasets have been processed, the following steps should be performed.

[0127] In some embodiments, obtaining the distribution data of the target reservoir based on the second waveform indication inversion result data of each well, the seismic data set, and the well logging data set includes:

[0128] Based on the earthquake data set and the well logging data set, obtain a data set with preset attributes;

[0129] The data set of the preset attributes is classified to obtain the classified attribute data set;

[0130] The classification of attribute data set and the second waveform indication inversion result data of each well are fused and analyzed to obtain the spatial distribution structure of the target reservoir.

[0131] The distribution spatial structure is processed in three-dimensional space to obtain the distribution data of the target reservoir.

[0132] Optionally, the second waveform indication inversion result data of each well is stored in the inversion result data set.

[0133] Optionally, based on the second waveform indication inversion result data of each well, an inversion result data set is obtained, and then the classified attribute data set and the inversion result data set are fused and analyzed to obtain the distribution spatial structure of the target reservoir.

[0134] In some embodiments, the distributed data includes:

[0135] Distribution thickness data, distribution range data.

[0136] It should be noted that among the many seismic attributes, the specific seismic attributes that are sensitive to the reservoir response in a region generally vary from region to region. Therefore, users can filter the preset attributes according to their actual needs and / or the actual geological conditions of the current region to obtain a set of preset attributes. No specific restrictions are made here.

[0137] The method disclosed in this embodiment involves: acquiring a seismic data set and a well logging data set of the target reservoir; acquiring seismic data and well logging data for each well in the seismic data set and well logging data set; acquiring first well seismic calibration result data for each well based on the seismic data and well logging data for each well; acquiring first waveform indication inversion result data for each well based on the first well seismic calibration result data, the seismic data and well logging data for each well; acquiring second well seismic calibration result data for each well based on the first waveform indication inversion result data, the seismic data and well logging data for each well; acquiring second waveform indication inversion result data for each well based on the second well seismic calibration result data, the seismic data and well logging data for each well; and acquiring distribution data of the target reservoir based on the second waveform indication inversion result data of each well, the seismic data set, and the well logging data set. Based on high-resolution waveform indication simulation, the correlation between seismic clustering waveforms and well logging curves was improved by repeatedly iterating seismic calibration using frequency-division attribute analysis results instead of the original seismic data and the preliminary results of re-inversion. This improved the accuracy and resolution of the prediction results. The resolution of reservoir inversion results was further enhanced, effectively eliminating interference from non-target reservoir factors in the seismic data. Attribute fusion effectively established a unified understanding between seismic and geological information, further improving inversion accuracy. In thin / extremely thin layer inversion, reservoirs with a thickness of approximately 3 meters can be accurately distinguished.

[0138] Example 2

[0139] This embodiment is a specific example provided in this disclosure. In this embodiment, the thickness and planar distribution of the thin interbedded dense sandstone at the top of the F layer in the North American Powder River Basin are predicted.

[0140] The method in this embodiment includes the following steps:

[0141] The first step is to obtain the seismic data set and well logging data set of the target reservoir.

[0142] It should be noted that the data set here can refer to a collection of data from multiple wells at the target reservoir.

[0143] The second step is to obtain the seismic data and well logging data of each well in the seismic data set and well logging data set.

[0144] In some embodiments, the well logging data includes:

[0145] Well logging curve data, drilling data, and drilling stratification data.

[0146] In some embodiments, the well logging data includes:

[0147] Acoustic transit time curve (AC), natural gamma ray curve (GR), density compensation curve (DEN), and well deviation curve (DEV).

[0148] The third step is to obtain the first well seismic calibration result data for each well based on the seismic data and logging data of each well.

[0149] In some embodiments, obtaining the first well seismic calibration result data for each well based on the seismic data and well logging data of each well includes:

[0150] Based on the earthquake data and well logging data, obtain the first frequency division data and the synthetic seismic record data;

[0151] Based on the first frequency division data and the synthetic seismic record data, the first well seismic calibration result data of each well is obtained.

[0152] In some embodiments, obtaining the first frequency-division data based on the seismic data and well logging data includes:

[0153] Based on the earthquake data, obtain the frequency division result data and earthquake velocity data;

[0154] Based on the logging data, obtain logging curve data and reservoir thickness and range characteristic data;

[0155] The well logging curve data are sequentially subjected to curve correction, curve standardization, and curve sensitivity analysis to obtain the processed well logging curve data.

[0156] The first frequency division data is obtained based on the processed well logging curve data, frequency division result data, reservoir thickness and range characteristic data, and seismic velocity data.

[0157] In some embodiments, the method for obtaining the frequency division result data includes:

[0158] Frequency-division attribute analysis is performed on the earthquake data to obtain the frequency-division result data.

[0159] In some embodiments, the acquisition method of synthetic seismic record data includes:

[0160] The drilling velocity data and density curve data are subjected to forward modeling to obtain the synthetic seismic record data.

[0161] Understandably, seismic data can be simply viewed as a composite of many frequency volumes, where different frequencies have varying resolving power for reservoirs. The relationship between seismic frequency and target reservoir thickness and seismic velocity falls under the category of tuning thickness or maximum resolving power of seismic data. By combining processed well logging data, frequency division results, reservoir thickness and extent characteristics, and seismic velocity data, superior frequency division data can be selected and used as the first frequency division data.

[0162] Optionally, the data from the first well seismic calibration can be found in the attached diagram of the instruction manual. Figure 2 , Figure 2 This is a schematic diagram illustrating a well-seismic calibration result generated based on the dominant frequency division data of a single well and synthetic seismic record data, as provided in an embodiment of this disclosure.

[0163] The fourth step is to obtain the first waveform indication inversion result data for each well based on the first well seismic calibration result data, the seismic data and logging data of each well.

[0164] In some embodiments, obtaining the first waveform indication inversion result data for each well based on the first well seismic calibration result data, the seismic data, and the well logging data for each well includes:

[0165] The first data is obtained by performing spectral simulation inversion on the first well seismic calibration results data;

[0166] Well logging curve data is obtained from the well logging data, and waveform indication inversion is performed on the well logging curve data to obtain the second data;

[0167] A low-frequency model was constructed from the earthquake data to obtain the third data.

[0168] The first data, the second data, and the third data are frequency-merged to obtain the first waveform indication inversion result data for each well.

[0169] Optionally, relevant software (such as the thin-layer seismic inversion software SMI) can be used to perform spectral simulation inversion, waveform indication inversion, and low-frequency model construction. The obtained three parts of data are then frequency-merged to obtain preliminary waveform indication inversion results based on the selected seismic frequency division data.

[0170] Step 5: Based on the first waveform indication inversion result data of each well, the seismic data and logging data of each well, obtain the second well seismic calibration result data of each well.

[0171] In some embodiments, obtaining the second well seismic calibration result data for each well based on the first waveform indication inversion result data, the seismic data, and the well logging data for each well includes:

[0172] Based on the aforementioned seismic data and well logging data, synthetic seismic record data is obtained;

[0173] Based on the first waveform indication inversion result data, well logging data, and seismic data, the second frequency division data is obtained;

[0174] Based on the synthetic seismic record data and the second frequency division data, the second well seismic calibration result data of each well is obtained.

[0175] In some embodiments, obtaining the second frequency-division data based on the first waveform indication inversion result data, well logging data, and seismic data includes:

[0176] Based on the earthquake data, obtain the frequency division result data and earthquake velocity data;

[0177] Obtain well logging curve data based on the well logging data;

[0178] The well logging curve data are sequentially subjected to curve correction, curve standardization, and curve sensitivity analysis to obtain the processed well logging curve data.

[0179] Based on the inversion results data indicated by the first waveform, obtain reservoir thickness and range characteristic data;

[0180] The second frequency division data is obtained based on the processed well logging curve data, frequency division result data, reservoir thickness and range characteristic data, and seismic velocity data.

[0181] In some embodiments, the method further includes:

[0182] The second well seismic calibration results data for each well are adjusted to the preset accuracy.

[0183] Optionally, the preset accuracy may include enabling the same target reservoir location in all wells to be located on the same phase axis in the seismic data that has the same or similar reflection characteristics and spatial properties, and to be continuously tracked in the three-dimensional seismic data.

[0184] Optionally, the second well seismic calibration results can be found in the attached diagram of the instruction manual. Figure 3 , Figure 3 This is a schematic diagram illustrating a well-seismic calibration result generated from waveform indication inversion results of multiple wells and synthetic seismic records, provided as an embodiment of this disclosure.

[0185] Step 6: Based on the second well seismic calibration results of each well, the seismic data and logging data of each well, obtain the second waveform indication inversion results of each well.

[0186] In some embodiments, obtaining the second waveform indication inversion result data for each well based on the second well seismic calibration result data, the seismic data, and the logging data of each well includes:

[0187] The second well seismic calibration results of each well are used to perform spectral simulation inversion to obtain the first data;

[0188] Well logging curve data is obtained from the well logging data of each well, and waveform indication inversion is performed on the well logging curve data to obtain the second data;

[0189] A low-frequency model was constructed from the seismic data of each well to obtain the third data.

[0190] The first, second, and third data are frequency-merged to obtain the second waveform indication inversion result data for each well.

[0191] Optionally, during this process, the correlation coefficient between the number of well sampling points and the seismic data can be analyzed repeatedly to obtain the cutoff number of sampling points and the optimal cutoff frequency. Through the inversion results in this step and the iterative calibration operation of the well composite record, as well as the iterative adjustment of parameters, the relative relationship between the reservoir location and characteristics reflected in the well and seismic data can be made more accurate.

[0192] Step 7: Based on the second waveform indication inversion results of each well, the seismic data set, and the well logging data set, obtain the distribution data of the target reservoir.

[0193] It should be noted that the above operations need to be performed on all data in the acquired seismic and well logging datasets of the target reservoir, and finally, the second waveform indication inversion result data corresponding to each well should be generated. After all data in the seismic and well logging datasets have been processed, the following steps should be performed.

[0194] In some embodiments, obtaining the distribution data of the target reservoir based on the second waveform indication inversion result data of each well, the seismic data set, and the well logging data set includes:

[0195] Based on the earthquake data set and the well logging data set, obtain a data set with preset attributes;

[0196] The data set of the preset attributes is classified to obtain the classified attribute data set;

[0197] The classification of attribute data set and the second waveform indication inversion result data of each well are fused and analyzed to obtain the spatial distribution structure of the target reservoir.

[0198] The distribution spatial structure is processed in three-dimensional space to obtain the distribution data of the target reservoir.

[0199] Optionally, the second waveform indication inversion result data of each well is stored in the inversion result data set.

[0200] Optionally, based on the second waveform indication inversion result data of each well, an inversion result data set is obtained, and then the classified attribute data set and the inversion result data set are fused and analyzed to obtain the distribution spatial structure of the target reservoir.

[0201] In some embodiments, the distributed data includes:

[0202] Distribution thickness data, distribution range data.

[0203] It should be noted that among the many seismic attributes, the specific seismic attributes that are sensitive to the reservoir response in a region generally vary from region to region. Therefore, users can filter the preset attributes according to their actual needs and / or the actual geological conditions of the current region to obtain a set of preset attributes. No specific restrictions are made here.

[0204] Optionally, key seismic attributes closely related to the target reservoir distribution can be identified by combining seismic and drilling data, and these attributes can be classified. Then, they can be fused with waveform indicator inversion results. The fusion analysis results can be found in the accompanying figures of the instruction manual. Figure 4 , Figure 4 The fusion analysis result diagram provided in this embodiment of the disclosure can clearly show the spatial structure of the target reservoir distribution. It can be calculated and measured in three-dimensional space using relevant software toolkits, thereby obtaining high-precision thickness and range of the target reservoir distribution, as well as other effective data related to reservoir characteristics. The reservoir distribution prediction results can be referred to in the accompanying drawings of the specification. Figure 5 , Figure 5 This is a schematic diagram illustrating the prediction results of reservoir distribution provided in an embodiment of this disclosure.

[0205] The method disclosed in this embodiment involves: acquiring a seismic data set and a well logging data set of a target reservoir; acquiring seismic data and well logging data for each well in the seismic data set and well logging data set; acquiring first well seismic calibration result data for each well based on the seismic data and well logging data for each well; acquiring first waveform indication inversion result data for each well based on the first well seismic calibration result data, the seismic data and well logging data for each well; acquiring second well seismic calibration result data for each well based on the first waveform indication inversion result data, the seismic data and well logging data for each well; acquiring second waveform indication inversion result data for each well based on the second well seismic calibration result data, the seismic data and well logging data for each well; and acquiring distribution data of the target reservoir based on the second waveform indication inversion result data of each well, the seismic data set, and the well logging data set. Based on high-resolution waveform indication simulation, the correlation between seismic clustering waveforms and well logging curves was improved by repeatedly iterating seismic calibration using frequency-division attribute analysis results instead of the original seismic data and the preliminary results of re-inversion. This improved the accuracy and resolution of the prediction results. The resolution of reservoir inversion results was further enhanced, effectively eliminating interference from non-target reservoir factors in the seismic data. Attribute fusion effectively established a unified understanding between seismic and geological information, further improving inversion accuracy. In thin / extremely thin layer inversion, reservoirs with a thickness of approximately 3 meters can be accurately distinguished.

[0206] Example 3

[0207] This embodiment provides an apparatus that can be used to execute the method embodiments of this disclosure. For details not disclosed in this apparatus embodiment, please refer to the method embodiments of this disclosure. Figure 6 This is a schematic diagram of the structure of a reservoir identification device provided in an embodiment of the present disclosure, as shown below. Figure 6 As shown, the device 600 provided in this embodiment includes:

[0208] The first acquisition module 601 is used to acquire the seismic data set and well logging data set of the target reservoir;

[0209] The second acquisition module 602 is used to acquire the seismic data and well logging data of each well in the seismic data set and well logging data set;

[0210] The first well seismic calibration result acquisition module 603 is used to acquire the first well seismic calibration result data of each well based on the seismic data and logging data of each well.

[0211] The first waveform indication inversion result acquisition module 604 is used to acquire the first waveform indication inversion result data of each well based on the first well seismic calibration result data of each well, the seismic data and logging data of each well, respectively.

[0212] The second well seismic calibration result acquisition module 605 is used to acquire the second well seismic calibration result data of each well based on the first waveform indication inversion result data of each well, the seismic data and logging data of each well, respectively.

[0213] The second waveform indication inversion result acquisition module 606 is used to acquire the second waveform indication inversion result data of each well based on the second well seismic calibration result data of each well, the seismic data and logging data of each well, respectively.

[0214] The distribution data acquisition module 607 is used to acquire the distribution data of the target reservoir based on the second waveform indication inversion result data of each well, the seismic data set, and the well logging data set.

[0215] In some embodiments, obtaining the first well seismic calibration result data for each well based on the seismic data and well logging data of each well includes:

[0216] Based on the earthquake data and well logging data, obtain the first frequency division data and the synthetic seismic record data;

[0217] Based on the first frequency division data and the synthetic seismic record data, the first well seismic calibration result data of each well is obtained.

[0218] In some embodiments, obtaining the first frequency-division data based on the seismic data and well logging data includes:

[0219] Based on the earthquake data, obtain the frequency division result data and earthquake velocity data;

[0220] Based on the logging data, obtain logging curve data and reservoir thickness and range characteristic data;

[0221] The well logging curve data are sequentially subjected to curve correction, curve standardization, and curve sensitivity analysis to obtain the processed well logging curve data.

[0222] The first frequency division data is obtained based on the processed well logging curve data, frequency division result data, reservoir thickness and range characteristic data, and seismic velocity data.

[0223] In some embodiments, the method for obtaining the frequency division result data includes:

[0224] Frequency-division attribute analysis is performed on the earthquake data to obtain the frequency-division result data.

[0225] In some embodiments, obtaining the first waveform indication inversion result data for each well based on the first well seismic calibration result data, the seismic data, and the well logging data for each well includes:

[0226] The first data is obtained by performing spectral simulation inversion on the first well seismic calibration results data;

[0227] Well logging curve data is obtained from the well logging data, and waveform indication inversion is performed on the well logging curve data to obtain the second data;

[0228] A low-frequency model was constructed from the earthquake data to obtain the third data.

[0229] The first data, the second data, and the third data are frequency-merged to obtain the first waveform indication inversion result data for each well.

[0230] In some embodiments, obtaining the second well seismic calibration result data for each well based on the first waveform indication inversion result data, the seismic data, and the well logging data for each well includes:

[0231] Based on the aforementioned seismic data and well logging data, synthetic seismic record data is obtained;

[0232] Based on the first waveform indication inversion result data, well logging data, and seismic data, the second frequency division data is obtained;

[0233] Based on the synthetic seismic record data and the second frequency division data, the second well seismic calibration result data of each well is obtained.

[0234] In some embodiments, obtaining the second frequency-division data based on the first waveform indication inversion result data, well logging data, and seismic data includes:

[0235] Based on the earthquake data, obtain the frequency division result data and earthquake velocity data;

[0236] Obtain well logging curve data based on the well logging data;

[0237] The well logging curve data are sequentially subjected to curve correction, curve standardization, and curve sensitivity analysis to obtain the processed well logging curve data.

[0238] Based on the inversion results data indicated by the first waveform, obtain reservoir thickness and range characteristic data;

[0239] The second frequency division data is obtained based on the processed well logging curve data, frequency division result data, reservoir thickness and range characteristic data, and seismic velocity data.

[0240] In some embodiments, obtaining the second waveform indication inversion result data for each well based on the second well seismic calibration result data, the seismic data, and the logging data of each well includes:

[0241] The second well seismic calibration results of each well are used to perform spectral simulation inversion to obtain the first data;

[0242] Well logging curve data is obtained from the well logging data of each well, and waveform indication inversion is performed on the well logging curve data to obtain the second data;

[0243] A low-frequency model was constructed from the seismic data of each well to obtain the third data.

[0244] The first, second, and third data are frequency-merged to obtain the second waveform indication inversion result data for each well.

[0245] In some embodiments, the method further includes:

[0246] The second well seismic calibration results data for each well are adjusted to the preset accuracy.

[0247] In some embodiments, obtaining the distribution data of the target reservoir based on the second waveform indication inversion result data of each well, the seismic data set, and the well logging data set includes:

[0248] Based on the earthquake data set and the well logging data set, obtain a data set with preset attributes;

[0249] The data set of the preset attributes is classified to obtain the classified attribute data set;

[0250] The classification of attribute data set and the second waveform indication inversion result data of each well are fused and analyzed to obtain the spatial distribution structure of the target reservoir.

[0251] The distribution spatial structure is processed in three-dimensional space to obtain the distribution data of the target reservoir.

[0252] It should be noted that the above modules / units can be functional modules or program modules, and can be implemented in software or hardware. For modules implemented in hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination.

[0253] The apparatus provided in this embodiment includes: a first acquisition module 601, used to acquire a seismic data set and a well logging data set of the target reservoir; a second acquisition module 602, used to acquire seismic data and well logging data of each well in the seismic data set and well logging data set; a first well seismic calibration result acquisition module 603, used to acquire first well seismic calibration result data of each well based on the seismic data and well logging data of each well; and a first waveform indication inversion result acquisition module 604, used to acquire first waveform indication inversion result data of each well based on the first well seismic calibration result data of each well, the seismic data and well logging data of each well. The system includes a second well seismic calibration result acquisition module 605, used to acquire second well seismic calibration result data for each well based on the first waveform indication inversion result data, seismic data, and well logging data of each well; a second waveform indication inversion result acquisition module 606, used to acquire second waveform indication inversion result data for each well based on the second well seismic calibration result data, seismic data, and well logging data of each well; and a distribution data acquisition module 607, used to acquire distribution data of the target reservoir based on the second waveform indication inversion result data of each well, the seismic data set, and the well logging data set. Based on high-resolution waveform indication simulation, by using frequency-division attribute analysis results to replace the original seismic data and repeatedly iterating the seismic calibration in the preliminary results of the re-inversion, the correlation between seismic clustering waveforms and well logging curves is improved, thereby improving the accuracy and resolution of the prediction results. This further improves the resolution of reservoir inversion results and effectively eliminates interference from non-target reservoir factors in seismic data; attribute fusion effectively establishes a unified understanding between seismic and geological information, further improving inversion accuracy, and can accurately distinguish reservoirs with a thickness of about 3 meters in thin / ultra-thin inversion.

[0254] Example 4

[0255] This embodiment also provides a storage medium storing a computer program. When the computer program is executed by a processor, it can implement the method steps as described in Embodiment 1 or Embodiment 2. This embodiment will not repeat the details here.

[0256] The storage medium may individually include computer programs, data files, data structures, etc., or a combination thereof. The storage medium or computer program may be specifically designed and understood by those skilled in the art of computer software, or the storage medium may be known and available to those skilled in the art of computer software. Examples of storage media include: magnetic media, such as hard disks, floppy disks, and magnetic tapes; optical media, such as CD-ROMs and DVDs; magneto-optical media, such as optical discs; and hardware devices specifically configured to store and execute computer programs, such as read-only memory (ROM), random access memory (RAM), flash memory; or servers, app stores, etc. Examples of computer programs include machine code (e.g., code generated by a compiler) and files containing high-level code that can be executed by a computer using an interpreter. The described hardware devices may be configured to function as one or more software modules to perform the operations and methods described above, and vice versa. Furthermore, the storage medium may be distributed across a networked computer system, allowing for the decentralized storage and execution of program code or computer programs.

[0257] Example 5

[0258] Figure 7 A connection block diagram of an electronic device provided in an embodiment of this disclosure, such as... Figure 7 As shown, the electronic device 700 may include: a processor 701, a memory 702, a multimedia component 703, an input / output (I / O) interface 704, and a communication component 705.

[0259] The memory 702 is used to store various types of data, such as instructions for any application or method in the electronic device, as well as application-related data. The processor 701 is used to execute all or part of the steps of the method in Embodiment 1 or Embodiment 2, which will not be repeated here.

[0260] It should be noted that the processor 701 may be implemented as an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field-programmable gate array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and is used to execute the method described above.

[0261] The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0262] Multimedia component 703 may include a screen, which may be a touchscreen, and an audio component for outputting and / or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory or transmitted via a communication component. The audio component also includes at least one speaker for outputting audio signals.

[0263] I / O interface 704 provides an interface between processor 701 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical buttons.

[0264] The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wired communication includes communication via network ports, serial ports, etc.; wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, 5G, or one or more combinations thereof. Therefore, the corresponding communication component 705 may include a Wi-Fi module, a Bluetooth module, and an NFC module.

[0265] In summary, this disclosure provides a reservoir identification method, apparatus, storage medium, and electronic device. The method includes: acquiring a seismic data set and a well logging data set of a target reservoir; acquiring seismic data and well logging data for each well in the seismic data set and well logging data set; acquiring first well seismic calibration result data for each well based on the seismic data and well logging data for each well; acquiring first waveform indication inversion result data for each well based on the first well seismic calibration result data, the seismic data, and the well logging data for each well; acquiring second well seismic calibration result data for each well based on the first waveform indication inversion result data, the seismic data, and the well logging data for each well; acquiring second waveform indication inversion result data for each well based on the second well seismic calibration result data, the seismic data, and the well logging data for each well; and acquiring distribution data of the target reservoir based on the second waveform indication inversion result data of each well, the seismic data set, and the well logging data set. Based on high-resolution waveform indication simulation, the correlation between seismic clustering waveforms and well logging curves was improved by repeatedly iterating seismic calibration using frequency-division attribute analysis results instead of the original seismic data and the preliminary results of re-inversion. This improved the accuracy and resolution of the prediction results. The resolution of reservoir inversion results was further enhanced, effectively eliminating interference from non-target reservoir factors in the seismic data. Attribute fusion effectively established a unified understanding between seismic and geological information, further improving inversion accuracy. In thin / extremely thin layer inversion, reservoirs with a thickness of approximately 3 meters can be accurately distinguished.

[0266] It should also be understood that the methods or systems disclosed in the embodiments provided in this disclosure can also be implemented in other ways. The method or system embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functions, and operations of possible implementations of methods and apparatus according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, computer program segment, or part of a computer program, which includes one or more computer programs for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings, and may actually be executed substantially in parallel, or sometimes in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer programs.

[0267] In this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, apparatus, or apparatus that includes that element. The use of terms such as "first," "second," etc., is for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number or sequence of the indicated technical features. In the description of this disclosure, unless otherwise expressly defined, terms such as "logging curve," "wellbore calibration," "frequency division," and "inversion" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of these terms in this disclosure in conjunction with the specific content of the technical solution. Furthermore, in the description of this disclosure, unless otherwise stated, the terms "a plurality of" or "more" mean at least two.

[0268] Finally, it should be noted that in the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "a single example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0269] Although embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and are merely implementation methods adopted to facilitate understanding of the present disclosure, and are not intended to limit the present disclosure. Any person skilled in the art to which this disclosure pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope disclosed herein, but the scope of protection of this disclosure shall still be determined by the scope defined in the appended claims.

Claims

1. A reservoir identification method, characterized in that, The method includes: Acquire the seismic and well logging datasets of the target reservoir; Obtain the seismic data and well logging data of each well in the aforementioned seismic data set and well logging data set; Based on the seismic data and logging data of each well, the first well seismic calibration result data of each well is obtained; Based on the first well seismic calibration result data of each well, the seismic data and logging data of each well, the first waveform indication inversion result data of each well is obtained; Based on the first waveform indication inversion result data, the seismic data, and the well logging data of each well, the second well seismic calibration result data of each well is obtained, including: obtaining synthetic seismic record data based on the seismic data and the well logging data; obtaining second frequency division data based on the first waveform indication inversion result data, the well logging data, and the seismic data; and obtaining the second well seismic calibration result data of each well based on the synthetic seismic record data and the second frequency division data. Based on the second seismic calibration results, seismic data, and well logging data of each well, the second waveform indication inversion result data of each well is obtained, including: performing spectral simulation inversion on the second seismic calibration results of each well to obtain first data; obtaining well logging curve data from the well logging data of each well, and performing waveform indication inversion on the well logging curve data to obtain second data; constructing a low-frequency model on the seismic data of each well to obtain third data; and frequency merging of the first data, second data, and third data to obtain the second waveform indication inversion result data of each well. Based on the second waveform indication inversion results of each well, the seismic data set, and the well logging data set, the distribution data of the target reservoir is obtained; The step of obtaining the second frequency-division data based on the first waveform indication inversion result data, well logging data, and seismic data includes: Based on the earthquake data, obtain the frequency division result data and earthquake velocity data; Obtain well logging curve data based on the well logging data; The well logging curve data are sequentially subjected to curve correction, curve standardization, and curve sensitivity analysis to obtain the processed well logging curve data. Based on the inversion results data indicated by the first waveform, obtain reservoir thickness and range characteristic data; The second frequency division data is obtained based on the processed well logging curve data, frequency division result data, reservoir thickness and range characteristic data, and seismic velocity data.

2. The method according to claim 1, characterized in that, The process of obtaining the first well seismic calibration result data for each well based on the seismic data and logging data of each well includes: Based on the earthquake data and well logging data, obtain the first frequency division data and the synthetic seismic record data; Based on the first frequency division data and the synthetic seismic record data, the first well seismic calibration result data of each well is obtained.

3. The method according to claim 2, characterized in that, The step of obtaining the first frequency-division data based on the seismic data and well logging data includes: Based on the earthquake data, obtain the frequency division result data and earthquake velocity data; Based on the logging data, obtain logging curve data and reservoir thickness and range characteristic data; The well logging curve data are sequentially subjected to curve correction, curve standardization, and curve sensitivity analysis to obtain the processed well logging curve data. The first frequency division data is obtained based on the processed well logging curve data, frequency division result data, reservoir thickness and range characteristic data, and seismic velocity data.

4. The method according to claim 3, characterized in that, The methods for obtaining the frequency division result data include: Frequency-division attribute analysis is performed on the earthquake data to obtain the frequency-division result data.

5. The method according to claim 1, characterized in that, The step of obtaining the first waveform indication inversion result data for each well based on the first well seismic calibration result data, the seismic data, and the well logging data of each well includes: The first data is obtained by performing spectral simulation inversion on the first well seismic calibration results data; Well logging curve data is obtained from the well logging data, and waveform indication inversion is performed on the well logging curve data to obtain the second data; A low-frequency model was constructed from the earthquake data to obtain the third data. The first data, the second data, and the third data are frequency-merged to obtain the first waveform indication inversion result data for each well.

6. The method according to claim 1, characterized in that, The method further includes: The second well seismic calibration results data for each well are adjusted to the preset accuracy.

7. The method according to claim 1, characterized in that, The step of obtaining the distribution data of the target reservoir based on the second waveform indication inversion results of each well, the seismic data set, and the well logging data set includes: Based on the earthquake data set and the well logging data set, obtain a data set with preset attributes; The data set of the preset attributes is classified to obtain the classified attribute data set; The classification of attribute data set and the second waveform indication inversion result data of each well are fused and analyzed to obtain the spatial distribution structure of the target reservoir. The distribution spatial structure is processed in three-dimensional space to obtain the distribution data of the target reservoir.

8. A reservoir identification device applying the method as described in claim 1, characterized in that, include: The first acquisition module is used to acquire the seismic data set and well logging data set of the target reservoir; The second acquisition module is used to acquire the seismic data and well logging data of each well in the seismic data set and well logging data set; The first well seismic calibration result acquisition module is used to acquire the first well seismic calibration result data of each well based on the seismic data and logging data of each well. The first waveform indication inversion result acquisition module is used to acquire the first waveform indication inversion result data of each well based on the first well seismic calibration result data of each well, the seismic data and logging data of each well, respectively. The second well seismic calibration result acquisition module is used to acquire the second well seismic calibration result data of each well based on the first waveform indication inversion result data of each well, the seismic data and logging data of each well, respectively. The second waveform indication inversion result acquisition module is used to acquire the second waveform indication inversion result data of each well based on the second well seismic calibration result data of each well, the seismic data and logging data of each well, respectively. The distribution data acquisition module is used to acquire the distribution data of the target reservoir based on the second waveform indication inversion result data of each well, the seismic data set, and the well logging data set.

9. A storage medium, characterized in that, The computer program stored in the storage medium can be executed by one or more processors to implement the reservoir identification method as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, The system includes a memory and a processor, wherein a computer program is stored in the memory, and the memory and the processor are communicatively connected to each other. When the computer program is executed by the processor, it performs the reservoir identification method as described in any one of claims 1 to 7.

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