A method, device, medium and equipment for earthquake prediction of clastic rock sand body structure

Abstract

The embodiment of the present application discloses a seismic prediction method, device, medium and equipment for clastic rock sand body structure. Among them, the method includes: obtaining well seismic calibration results and the vertical distribution of sand body structure in the target area; obtaining multi-dimensional seismic waveform characteristics of the target area based on the well seismic calibration results; using a dimensionality reduction strategy based on the multi-dimensional seismic waveform characteristics to obtain a one-dimensional seismic waveform characteristic value; the dimensionality reduction strategy is a t-distributed random neighborhood embedding method; determining the planar distribution of the sand body structure in the target area based on the one-dimensional seismic waveform characteristic value; determining the sweet spot of the clastic shale oil reservoir based on the planar distribution of the sand body structure in the target area and the vertical distribution of the sand body structure in the target area. This technical solution can increase the discrimination of lithology in the skeleton well, which is conducive to predicting the vertical and planar distribution of the sand body structure in the area to be explored, and thus improving the recognition accuracy of the geological sweet spots of the shale oil reservoir.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of geophysical technology, and in particular to a method, device, medium, and equipment for earthquake prediction of clastic rock sand body structures. Background Art

[0002] Exploration and development practices in recent years have demonstrated that sand body structure is a primary factor controlling the enrichment of sweet spots in the Chang 7 shale formation and a key geological determinant of shale oil production. Conventional 3D seismic inversion techniques currently focus solely on predicting sand body thickness and lateral distribution, lacking the precise techniques and methods for characterizing the vertical stacking of sand bodies. Consequently, they cannot comprehensively characterize the planar distribution of favorable sand body structures across the entire prospective exploration area, and the accuracy of identifying sweet spots in clastic shale oil reservoirs is limited. Summary of the Invention

[0003] The present invention provides a method, apparatus, medium, and device for seismic prediction of clastic sandstone structures. These methods accurately distinguish sandstone from mudstone by constructing a pseudo-P-wave impedance expression. Furthermore, they accurately predict the vertical and horizontal distribution of sandstone structures through geostatistical inversion and seismic waveform feature dimensionality reduction, thereby improving the accuracy of identifying geological sweet spots in shale oil reservoirs.

[0004] In a first aspect, an embodiment of the present application provides a method for seismic prediction of clastic sand body structure, the method comprising:

[0005] Obtain well-seismic calibration results and vertical distribution of sand body structure in the target area;

[0006] According to the well-seismic calibration results, a multi-dimensional seismic waveform characteristic of the target area is obtained;

[0007] According to the multi-dimensional seismic waveform characteristics, a dimensionality reduction strategy is adopted to obtain a one-dimensional seismic waveform characteristic value; the dimensionality reduction strategy is a t-distributed random neighborhood embedding method;

[0008] Determine the planar distribution of the sand body structure in the target area based on the one-dimensional seismic waveform characteristic value; the sand body structure includes thick massive sand bodies, thick sand and thin mud interbeds, and thin sand and thick mud interbeds;

[0009] The sweet spots of the clastic shale oil reservoir are determined according to the planar distribution of the sand body structure in the target area and the vertical distribution of the sand body structure in the target area.

[0010] In a second aspect, an embodiment of the present application provides an earthquake prediction device for a clastic sand body structure, the device comprising:

[0011] Information acquisition module, used to obtain well-seismic calibration results and vertical distribution of sand body structure in the target area;

[0012] A seismic waveform feature generation module, configured to obtain multi-dimensional seismic waveform features of a target area based on the well-seismic calibration results;

[0013] A seismic waveform characteristic value generation module is used to obtain a one-dimensional seismic waveform characteristic value based on the multi-dimensional seismic waveform characteristics by adopting a dimensionality reduction strategy; the dimensionality reduction strategy is a t-distributed random neighborhood embedding method;

[0014] a sand body structure plane distribution determination module, configured to determine the sand body structure plane distribution of the target area according to the one-dimensional seismic waveform characteristic value; the sand body structure includes thick massive sand bodies, thick sand and thin mud interbeds, and thin sand and thick mud interbeds;

[0015] The clastic shale oil reservoir sweet spot determination module is used to determine the clastic shale oil reservoir sweet spot based on the planar distribution of the sand body structure in the target area and the vertical distribution of the sand body structure in the target area.

[0016] In a third aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the seismic prediction method for clastic rock sand body structure as described in the embodiment of the present application.

[0017] In a fourth aspect, an embodiment of the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the seismic prediction method for clastic rock sand body structure as described in the embodiment of the present application is implemented.

[0018] The technical solution provided in the embodiment of the present application obtains the well-seismic calibration results and the vertical distribution of the sand body structure in the target area, and obtains the multi-dimensional seismic waveform characteristics of the target area based on the well-seismic calibration results. Based on the multi-dimensional seismic waveform characteristics, a dimensionality reduction strategy is adopted to obtain a one-dimensional seismic waveform characteristic value. Based on the one-dimensional seismic waveform characteristic value, the planar distribution of the sand body structure in the target area is determined. Based on the planar distribution of the sand body structure in the target area and the vertical distribution of the sand body structure in the target area, the sweet spot of the clastic shale oil reservoir is determined. This solution can further accurately predict the vertical distribution and planar distribution of the sand body structure through geostatistical inversion and seismic waveform characteristic dimensionality reduction, thereby improving the identification accuracy of the geological sweet spot of the shale oil reservoir. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of the seismic prediction method for clastic rock sand body structure provided in Example 1 of the present application;

[0020] Figure 2A This is a flow chart of a method for seismic prediction of clastic rock sand body structure provided by the second embodiment of the present invention;

[0021] Figure 2B This is a schematic diagram of a shale determination method provided in the second embodiment of the present invention;

[0022] Figure 2C 1 is a schematic diagram comparing longitudinal wave impedance and pseudo-longitudinal wave impedance data provided by the second embodiment of the present invention;

[0023] Figure 2D This is a schematic diagram of a method for determining sandstone and mudstone provided in the second embodiment of the present invention;

[0024] Figure 2E This is a schematic diagram of conventional sparse pulse inversion results provided by the second embodiment of the present invention;

[0025] Figure 2F is a schematic diagram of geostatistical inversion results provided by the second embodiment of the present invention;

[0026] Figure 2G This is a schematic diagram of the planar distribution of the sand body structure in the target area provided by the second embodiment of the present invention;

[0027] Figure 3 This is a schematic structural diagram of an earthquake prediction device for a clastic rock sand body structure provided by a third embodiment of the present invention;

[0028] Figure 4 This is a structural diagram of an electronic device provided in Example 5 of the present application. DETAILED DESCRIPTION

[0029] The present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are intended only to illustrate the present application and are not intended to limit the present application. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions of the present application, not all of the structures.

[0030] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the steps as sequential processes, many of the steps can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the steps can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the accompanying drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0031] Example 1

[0032] Figure 1This is a flowchart of the earthquake prediction method for clastic rock sand body structure provided in Example 1 of the present application. This embodiment can be applied to the earthquake prediction scenario of clastic rock sand body structure. The method can be executed by the earthquake prediction device for clastic rock sand body structure provided in the embodiment of the present application. The device can be implemented by software and / or hardware and can be integrated into electronic equipment.

[0033] like Figure 1 As shown, the seismic prediction method of the clastic rock sand body structure includes:

[0034] S101, obtaining well-seismic calibration results and vertical distribution of sand body structure in the target area.

[0035] This scheme can be executed by electronic devices such as computers. The target area can be an area to be explored. Exploration and development practices in recent years have shown that the sand body structure type is one of the main controlling factors for the enrichment of the Chang 7 shale oil sweet spot and is also a key geological factor that determines shale oil production.

[0036] To accurately locate shale oil sweet spots, a framework well is selected in the target area. Electronic equipment then performs forward modeling on the framework well using well logging and seismic data to obtain well-seismic calibration results. Furthermore, the electronic equipment must determine the vertical distribution of the sandstone structure in the target area to determine the shale oil exploration interval. This vertical distribution of the sandstone structure in the target area can be determined based on existing exploration data or by analyzing well logging and seismic data.

[0037] S102: Obtain multi-dimensional seismic waveform characteristics of the target area according to the well-seismic calibration results.

[0038] Based on the well-seismic calibration results obtained in S101, a computer or other electronic device can obtain multidimensional seismic wave characteristics of the target area. The multidimensional seismic wave characteristics may include characteristics such as the waveform, amplitude, and frequency of the seismic wave. Specifically, the seismic wave waveform may include parallel strong reflection waves, parallel medium-strong reflection waves, and parallel medium-weak reflection waves. The amplitude may include strong amplitude, medium-strong amplitude, and medium-weak amplitude. The frequency may include medium-high frequency, medium-low frequency, and low frequency.

[0039] S103, using a dimensionality reduction strategy based on the multi-dimensional seismic waveform characteristics to obtain a one-dimensional seismic waveform characteristic value; the dimensionality reduction strategy is a t-distributed random neighborhood embedding method.

[0040] Multidimensional seismic waveform characteristics are very complex to analyze, and it is not easy to determine the planar distribution of the sand body structure in the target area. Using a dimensionality reduction strategy, the multidimensional seismic waveform characteristics can be transformed into one-dimensional seismic wave characteristic values. The one-dimensional seismic wave characteristic values ​​can be used to relatively easily determine the planar distribution of the sand body structure in the target area. The dimensionality reduction strategy can be a t-distributed stochastic neighbor embedding (t-SNE) method. The t-distributed stochastic neighbor embedding method is a machine learning algorithm for nonlinear dimensionality reduction and is very suitable for reducing high-dimensional data to low-dimensional data. A large number of seismic waves are usually generated in the target area, and each seismic wave includes multidimensional features. Therefore, the use of a dimensionality reduction strategy can effectively simplify the seismic wave characteristics, so that the one-dimensional seismic waveform characteristic values ​​can highlight the differences in seismic waveforms while retaining the multidimensional seismic wave characteristics.

[0041] S104 , determining a planar distribution of a sand body structure in a target area based on the one-dimensional seismic waveform characteristic value; the sand body structure includes thick massive sand bodies, thick sand and thin mud interbeds, and thin sand and thick mud interbeds.

[0042] Based on the one-dimensional seismic waveform eigenvalues ​​in the target area, a computer or other electronic device performs statistical analysis, classifying similar one-dimensional seismic waveform eigenvalues ​​into a sand body structure type, thereby obtaining a planar distribution of the sand body structure in the target area. These sand bodies can include thick, massive sand, thick sand interbedded with thin mud, and thin sand interbedded with thick mud. The electronic device can set characteristic thresholds based on these three types of sand body structures and compare them with the one-dimensional seismic waveform eigenvalues ​​in the target area to obtain a planar distribution of the sand body structure.

[0043] S105 , determining a sweet spot of a clastic shale oil reservoir according to the planar distribution of the sand body structure in the target area and the vertical distribution of the sand body structure in the target area.

[0044] Based on the planar and vertical distribution of the sand body structure in the target area, explorers can accurately locate the sweet spots in the clastic shale oil reservoir, thereby improving work efficiency and providing important support for the optimization of favorable areas for shale oil exploration and development and the evaluation of horizontal well deployment.

[0045] The technical solution provided in the embodiment of the present application obtains the well-seismic calibration results and the vertical distribution of the sand body structure in the target area, and obtains the multi-dimensional seismic waveform characteristics of the target area based on the well-seismic calibration results. Based on the multi-dimensional seismic waveform characteristics, a dimensionality reduction strategy is adopted to obtain a one-dimensional seismic waveform characteristic value. Based on the one-dimensional seismic waveform characteristic value, the planar distribution of the sand body structure in the target area is determined. Based on the planar distribution of the sand body structure in the target area and the vertical distribution of the sand body structure in the target area, the sweet spot of the clastic shale oil reservoir is determined. This solution can further accurately predict the vertical distribution and planar distribution of the sand body structure through geostatistical inversion and seismic waveform characteristic dimensionality reduction, thereby improving the identification accuracy of the geological sweet spot of the shale oil reservoir.

[0046] Example 2

[0047] Figure 2A This is a flow chart of a seismic prediction method for clastic rock sand body structure provided by the second embodiment of the present invention. This embodiment is refined based on the above embodiment.

[0048] like Figure 2A As shown, the method of this embodiment specifically includes the following steps:

[0049] S201, acquiring well logging data of each skeleton well in the target area.

[0050] Based on exploration experience, multiple skeleton wells can be set up in the target area to facilitate pre-exploration of the target area's geological structure. The well logging data can be an indirect reflection of reservoir characteristics and can include conventional data, nuclear magnetic resonance (NMR) data, and rock mechanics data. Conventional data can include neutron curves, acoustic data, density data, gamma-ray data, shale content, porosity, and probe resistivity. NMR data can include bound fluid saturation, NMR porosity, and NMR-calculated permeability. Rock mechanics data can include bulk modulus, Young's modulus, P-wave transit time, and S-wave transit time.

[0051] S202: Determine the logging gamma data and the longitudinal wave impedance data of each skeleton well according to the logging data of each skeleton well.

[0052] It is understood that after a computer or other electronic device uses application software to collect the logging data of each skeleton well in the target area, the logging gamma data can be directly obtained from the logging data of each skeleton well. The longitudinal wave impedance data can be calculated based on the density data and acoustic wave data in the logging data.

[0053] S203: Determine the shale distribution of each skeleton well based on the well logging gamma data and the longitudinal wave impedance data.

[0054] The gamma-ray and P-wave impedance data can be used to identify shale formations within each skeleton well. Therefore, the electronic equipment can determine which layers in each skeleton well are shale formations based on the gamma-ray and P-wave impedance data.

[0055] Specifically, determining the shale distribution of each skeleton well based on the well logging gamma data and the longitudinal wave impedance data includes:

[0056] If the logging gamma value of the target layer of the skeleton well is greater than the logging gamma threshold of shale, and the P-wave impedance of the target layer is less than the P-wave impedance threshold of shale, then the target layer is determined to be shale; the logging gamma threshold and P-wave impedance threshold of shale are obtained based on statistical analysis of logging data.

[0057] Figure 2B This is a schematic diagram of a shale determination method provided by the second embodiment of the present invention. The electronic device can perform intersection analysis on the well logging gamma data and the longitudinal wave impedance data of the target skeleton well, such as Figure 2B As shown in the figure, the horizontal axis represents the logging gamma value, and the vertical axis represents the longitudinal wave impedance value. By statistically analyzing the logging data, the logging gamma threshold and longitudinal wave impedance threshold of shale can be obtained. Using the logging gamma threshold and longitudinal wave impedance threshold of shale, it is possible to distinguish whether each layer of the skeleton well is shale. Figure 2B For example, the logging gamma threshold of shale is 175API, and the longitudinal wave impedance threshold is 9500m / s*g / cm 3 If the target interval's logging gamma value in the skeleton well is greater than the shale logging gamma threshold, and the target interval's P-wave impedance is less than the shale P-wave impedance threshold, the interval can be determined to be shale. If the target interval's logging gamma value in the skeleton well is less than the shale logging gamma threshold, or the target interval's P-wave impedance is greater than the shale P-wave impedance threshold, the interval is not shale and may be sandstone or mudstone.

[0058] S204: constructing a pseudo-P-wave impedance expression based on the P-wave impedance data, the sand-to-formation ratio and the mud content in the well logging data.

[0059] Because the P-wave impedance ranges of sandstone and mudstone overlap, accurately distinguishing sandstone from mudstone using P-wave impedance data is difficult. Therefore, this solution constructs a pseudo-P-wave impedance expression using P-wave impedance, the sand-to-formation ratio, and shale content from well logging data. The pseudo-P-wave impedance value calculated using this expression can more easily distinguish sandstone from mudstone. This expression can amplify the lithologic differences between mudstone and sandstone. The sand-to-formation ratio, which is the ratio of the total sandstone thickness to the formation thickness, is a key parameter for reservoir evaluation. Mud refers to clastic material with a particle diameter less than 0.01 mm. The shale content can be expressed as the shale volume, which is the ratio of the shale volume to the total rock volume. Determining the shale content is crucial for the quantitative interpretation of shale sandstone reservoirs. When rocks contain shale, various well logging data are more or less affected by it. Therefore, when evaluating rock properties, only by knowing the shale content can the effects of shale be determined and corrected for.

[0060] In this solution, optionally, in the pseudo-P-wave impedance expression, the pseudo-P-wave impedance is positively correlated with the sand-to-ground ratio, positively correlated with the initial P-wave impedance, positively correlated with the minimum P-wave impedance of sandstone and mudstone, and negatively correlated with the mud content;

[0061] The minimum longitudinal wave impedance of sandstone and mudstone is obtained through statistical analysis of well logging data; and the initial longitudinal wave impedance is the longitudinal wave impedance of the target layer section of the skeleton well.

[0062] Based on the density and acoustic data from the well logging data, a computer or other electronic device can calculate the P-wave impedance of each layer in each skeleton well. The pseudo-P-wave impedance of the target layer can be calculated based on the initial P-wave impedance of that layer. This initial P-wave impedance is the P-wave impedance of the target layer in the skeleton well. The initial P-wave impedance can be positively correlated with the pseudo-P-wave impedance.

[0063] By performing a statistical analysis on the longitudinal wave impedance of each layer of the skeleton well, the minimum longitudinal wave impedance of the sand and mudstone of each skeleton well can be obtained. It can be understood that the minimum longitudinal wave impedance of the sand and mudstone can be used to measure the longitudinal wave impedance of the sand and mudstone in the skeleton well and to determine the limit value of the longitudinal wave impedance of the sand and mudstone in the skeleton well. The minimum longitudinal wave impedance of the sand and mudstone should be positively correlated with the pseudo-longitudinal wave impedance. It is easy to understand that the sand-to-formation ratio is used to represent the ratio of the total thickness of the sandstone to the formation thickness. Therefore, the pseudo-longitudinal wave impedance can be positively correlated with the sand-to-formation ratio. Since mud has a certain influence on the logging data, it is necessary to correct the influence of mud. The pseudo-longitudinal wave impedance is negatively correlated with the mud content.

[0064] This approach qualitatively identifies the factors influencing pseudo-P-wave impedance and clarifies the relationships among sand-to-ground ratio, initial P-wave impedance, and the minimum P-wave impedance of sandstone and mudstone. This approach is beneficial for qualitatively distinguishing sandstone from mudstone.

[0065] Based on the above solution, optionally, the pseudo-longitudinal wave impedance expression is:

[0066] ;

[0067] in, is the pseudo-longitudinal wave impedance, is the minimum longitudinal wave impedance of sandstone and mudstone, is the sand-to-land ratio, is the initial longitudinal wave impedance, is the preset difference amplification factor between sandstone and mudstone, For the mud content.

[0068] The pseudo-P-wave impedance expression provided by this solution incorporates not only the sand-to-ground ratio, initial P-wave impedance, and the minimum P-wave impedance of sandstone and mudstone, but also the differential amplification factor between sandstone and mudstone to amplify the differences between these two types of rocks. This solution accurately determines the pseudo-P-wave impedance values ​​for each interval within a skeleton well, and thus produces a pseudo-P-wave impedance curve for the skeleton well, enabling quantitative differentiation between sandstone and mudstone.

[0069] Figure 2C This is a schematic diagram comparing the longitudinal wave impedance and pseudo-longitudinal wave impedance data provided by the second embodiment of the present invention. Figure 2C From the pseudo-P-wave impedance curve and P-wave impedance curve of the skeleton well, it can be clearly seen that the pseudo-P-wave impedance curve is more different from the P-wave impedance curve in mudstone and sandstone.

[0070] S205 , calculating the pseudo-P-wave impedance of the target layer of each skeleton well according to the pseudo-P-wave impedance expression, and determining the distribution of sandstone and mudstone in each skeleton well.

[0071] Computers and other electronic devices can batch calculate the pseudo-P-wave impedance of the target layer sections of each skeleton well based on the pseudo-P-wave impedance expression, and the pseudo-P-wave impedance data can highlight the difference between sandstone and mudstone. The pseudo-P-wave impedance expression can be used to determine which layers of each skeleton well are sandstone and which are mudstone, and thus the distribution of sandstone and mudstone in each skeleton well can be characterized. The way to divide sandstone and mudstone can be to set one or more reasonable threshold ranges based on the statistical data of pseudo-P-wave impedance, and distinguish sandstone and mudstone by the threshold. The threshold can be determined based on the mixed content of sandstone and mudstone, or it can be determined based on the statistical results of the pseudo-P-wave impedance of each sandstone and mudstone.

[0072] Optionally, in this solution, calculating the pseudo-P-wave impedance of the target layer of each skeleton well according to the pseudo-P-wave impedance expression and determining the distribution of sandstone and mudstone in each skeleton well includes:

[0073] If the pseudo-P-wave impedance of the target layer of the skeleton well is greater than the pseudo-P-wave impedance threshold of sandstone, the target layer is determined to be sandstone;

[0074] If the pseudo-P-wave impedance of the target layer of the skeleton well is less than the pseudo-P-wave impedance threshold of sandstone, the target layer is determined to be mudstone;

[0075] The pseudo-P wave impedance threshold of the sandstone is obtained based on the statistical analysis of the pseudo-P wave impedance of each layer in each skeleton well.

[0076] Specifically, because sandstone is a favorable target for geological exploration, computers and other electronic equipment can select the pseudo-P-wave impedance of sandstone in each layer of each skeleton well and perform statistical analysis to obtain the pseudo-P-wave impedance threshold of the sandstone. If the pseudo-P-wave impedance of the target layer of the skeleton well is greater than the pseudo-P-wave impedance threshold of the sandstone, the target layer can be determined to be sandstone. If the pseudo-P-wave impedance of the target layer of the skeleton well is less than the pseudo-P-wave impedance threshold of the sandstone, the target layer can be determined to be mudstone.

[0077] Figure 2D This is a schematic diagram of a method for determining sandstone and mudstone provided in the second embodiment of the present invention. Figure 2D The horizontal axis is the pseudo-P wave impedance value, and the vertical axis is the mud content. Each point in the figure is the pseudo-P wave impedance data of a skeleton well. The pseudo-P wave impedance threshold of sandstone is 11300 m / s*g / cm 3 The distribution of mudstone and sandstone in the skeleton well can be determined by the pseudo-P wave impedance threshold of sandstone.

[0078] S206, obtaining seismic data of the target area.

[0079] After determining the distribution of mudstone and sandstone in each framework well, geological prospectors need to further determine the distribution of sandstone and mudstone in the entire target area. Therefore, they need to invert the seismic data of the target area to determine the distribution of sandstone and mudstone between the framework wells. This seismic data can be actual seismic observation data, which can be used to invert the seismic parameters to be inverted.

[0080] S207 , determining Ricker wavelets based on the seismic data, and performing forward simulation on each skeleton well in the target area to obtain a well-seismic calibration result for the target area.

[0081] Computers and other electronic equipment can select appropriate Ricker wavelets based on seismic data, and use rock physics models, propagation matrix simulation methods, and convolution models to perform forward simulations on each skeleton well in the target area, thereby obtaining well-seismic calibration results for the entire target area.

[0082] S208 , establishing an initial inversion model according to the well-seismic calibration result, the preset initial value of the pseudo-P-wave impedance to be inverted, and the seismic data.

[0083] Based on the well-seismic calibration results, the preset initial value of the pseudo-P-wave impedance to be inverted, and the seismic data, a computer or other electronic device can establish an initial inversion model and perform inversion on the pseudo-P-wave impedance to be inverted. The inversion model can be a conventional sparse pulse inversion method or a geostatistical inversion method. The geostatistical inversion method can fully utilize seismic data and well logging data to obtain high-resolution inversion results.

[0084] S209 , performing inversion according to the initial inversion model to obtain pseudo-P-wave impedance prediction data of regions associated with each skeleton well in the target area.

[0085] Computers and other electronic devices can invert the pseudo-P-wave impedance to be inverted based on the initial inversion model to obtain pseudo-P-wave impedance prediction data of the associated areas of each skeleton well in the target area. Figure 2E is a schematic diagram of conventional sparse pulse inversion results provided by the second embodiment of the present invention, Figure 2F This is a schematic diagram of the geostatistical inversion results provided by the second embodiment of the present invention, compared with Figure 2E and Figure 2F It can be found that the geostatistical inversion results are more accurate than the conventional sparse pulse inversion results, and the lithofacies of the target area are more delicate.

[0086] S210, determining pseudo-P-wave impedance data of the target area according to the pseudo-P-wave impedance prediction data of the areas associated with each skeleton well in the target area and the pseudo-P-wave impedance data of each skeleton well; the target area includes the skeleton wells and the areas associated with the skeleton wells.

[0087] The target area may include each skeleton well and its associated areas. To determine the lithofacies distribution of the entire target area, a computer or other electronic device can use the pseudo-P-wave impedance prediction data from the associated areas of each skeleton well as pseudo-P-wave impedance data, and combine it with the pseudo-P-wave impedance data from each skeleton well to obtain pseudo-P-wave impedance data for the entire target area. It should be noted that the P-wave impedance value of shale in the target area can be treated as the pseudo-P-wave impedance value. Using this pseudo-P-wave impedance data, the lithofacies distribution of the entire target area can be determined.

[0088] S211 , determining the lithofacies distribution of the target area according to the pseudo-P-wave impedance data of the target area; the lithofacies includes shale, sandstone, and mudstone.

[0089] As will be appreciated, computers and other electronic devices can determine the lithofacies distribution of the target area based on the pseudo-P-wave impedance data of the target area. These lithofacies include shale, sandstone, and mudstone. It should be noted that shale is easier to distinguish than sandstone and mudstone. Therefore, this solution can directly distinguish shale from sandstone and mudstone using the inverted pseudo-impedance data. Furthermore, mudstone and sandstone can be distinguished using the same method as in S205.

[0090] S212, determining the vertical distribution of the sand body structure in the target area according to the lithofacies distribution of the target area;

[0091] Based on the lithofacies distribution of the target area, computers and other electronic equipment can determine the vertical distribution of the sand body structure in the target area. The sand body structure includes thick massive sand bodies, thick sand interbeds with thin mud, and thin sand interbeds with thick mud. The sandstone content in these layers decreases in descending order.

[0092] This solution can determine the vertical distribution of sand body structures in the target area, that is, the distribution of underground sand body structures in the target area. This helps explorers determine the exploration depth and improve exploration accuracy and work efficiency.

[0093] S213, obtaining multi-dimensional seismic waveform characteristics of the target area according to the well-seismic calibration results.

[0094] S214, using a dimensionality reduction strategy based on the multi-dimensional seismic waveform characteristics to obtain a one-dimensional seismic waveform characteristic value; the dimensionality reduction strategy is a t-distributed random neighborhood embedding method.

[0095] S215 , determining the planar distribution of the sand body structure in the target area according to the one-dimensional seismic waveform characteristic value.

[0096] S216 , determining a sweet spot of a clastic shale oil reservoir according to the planar distribution of the sand body structure in the target area and the vertical distribution of the sand body structure in the target area.

[0097] Figure 2G This is a schematic diagram of the planar distribution of the sand body structure in the target area provided in Example 2 of the present invention. In the figure, the area with the darkest grayscale is thin sand and thick mud interlayers, the area with the lightest grayscale is thick sand and thin mud interlayers, and the area with intermediate grayscale is thick layered massive sand bodies. Among them, the thick layered massive sand bodies and the thick sand and thin mud interlayered sand body structures can be considered as clastic shale oil reservoir sweet spots.

[0098] The technical solution provided in the embodiment of the present application can determine the logging gamma data and longitudinal wave impedance data of each skeleton well through the logging data of each skeleton well in the target area, and then determine the shale distribution of each skeleton well. By using the longitudinal wave impedance data, the sand-to-ground ratio and mud content in the logging data, a pseudo-longitudinal wave impedance expression is constructed, and the pseudo-longitudinal wave impedance of the target layer of each skeleton well is calculated based on the pseudo-longitudinal wave impedance expression, thereby determining the sandstone and mudstone distribution of each skeleton well, and realizing the accurate distinction between sandstone and mudstone. At the same time, on the basis of the fine distinction between sandstone and mudstone, this solution can further accurately predict the vertical distribution and planar distribution of sand body structure through geostatistical inversion and seismic waveform feature dimensionality reduction, thereby improving the identification accuracy of geological sweet spots in shale oil reservoirs.

[0099] Example 3

[0100] Figure 3 This is a structural schematic diagram of an earthquake prediction device for a clastic rock sand body structure provided in Example 3 of the present invention. The device can execute the earthquake prediction method for a clastic rock sand body structure provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0101] like Figure 3 As shown, the device may include:

[0102] Information acquisition module 301, used to obtain well-seismic calibration results and vertical distribution of sand body structure in the target area;

[0103] A seismic waveform feature generation module 302 is configured to obtain multi-dimensional seismic waveform features of a target area based on the well-seismic calibration results;

[0104] Seismic waveform characteristic value generation module 303, for obtaining one-dimensional seismic waveform characteristic values ​​according to the multi-dimensional seismic waveform characteristics by adopting a dimensionality reduction strategy; the dimensionality reduction strategy is a t-distributed random neighborhood embedding method;

[0105] The sand body structure plane distribution determination module 304 is used to determine the sand body structure plane distribution of the target area based on the one-dimensional seismic waveform characteristic value; the sand body structure includes thick massive sand, thick sand and thin mud interbeds, and thin sand and thick mud interbeds;

[0106] The clastic shale oil reservoir sweet spot determination module 305 is used to determine the clastic shale oil reservoir sweet spot based on the planar distribution of the sand body structure in the target area and the vertical distribution of the sand body structure in the target area.

[0107] In this solution, optionally, the device further includes:

[0108] Well logging data acquisition module, used to obtain well logging data of each skeleton well in the target area;

[0109] A data determination module, configured to determine the well logging gamma data and longitudinal wave impedance data of each skeleton well based on the well logging data of each skeleton well;

[0110] A shale distribution determination module, configured to determine the shale distribution of each skeleton well based on the well logging gamma data and the longitudinal wave impedance data;

[0111] A pseudo-P-wave impedance expression construction module is used to construct a pseudo-P-wave impedance expression based on the P-wave impedance data, the sand-to-ground ratio and the mud content in the well logging data;

[0112] The sandstone and mudstone distribution determination module is used to calculate the pseudo-P-wave impedance of the target layer of each skeleton well according to the pseudo-P-wave impedance expression, and determine the sandstone and mudstone distribution of each skeleton well.

[0113] In a feasible embodiment, optionally, the shale distribution determination module is specifically configured to:

[0114] If the logging gamma value of the target layer of the skeleton well is greater than the logging gamma threshold of shale, and the P-wave impedance of the target layer is less than the P-wave impedance threshold of shale, then the target layer is determined to be shale; the logging gamma threshold and P-wave impedance threshold of shale are obtained based on statistical analysis of logging data.

[0115] In this solution, optionally, in the pseudo-P-wave impedance expression, the pseudo-P-wave impedance is positively correlated with the sand-to-ground ratio, positively correlated with the initial P-wave impedance, positively correlated with the minimum P-wave impedance of sandstone and mudstone, and negatively correlated with the mud content;

[0116] The minimum longitudinal wave impedance of sandstone and mudstone is obtained through statistical analysis of well logging data; and the initial longitudinal wave impedance is the longitudinal wave impedance of the target layer section of the skeleton well.

[0117] Based on the above solution, optionally, the pseudo-longitudinal wave impedance expression is:

[0118] ;

[0119] in, is the pseudo-longitudinal wave impedance, is the minimum longitudinal wave impedance of sandstone and mudstone, is the sand-to-land ratio, is the initial longitudinal wave impedance, is the preset difference amplification factor between sandstone and mudstone, For the mud content.

[0120] In another feasible solution, optionally, the sandstone and mudstone distribution determination module is specifically configured to:

[0121] If the pseudo-P-wave impedance of the target layer of the skeleton well is greater than the pseudo-P-wave impedance threshold of sandstone, the target layer is determined to be sandstone;

[0122] If the pseudo-P-wave impedance of the target layer of the skeleton well is less than the pseudo-P-wave impedance threshold of sandstone, the target layer is determined to be mudstone;

[0123] The pseudo-P wave impedance threshold of the sandstone is obtained based on the statistical analysis of the pseudo-P wave impedance of each layer in each skeleton well.

[0124] In a preferred embodiment, optionally, the information acquisition module 301 is specifically configured to:

[0125] Acquire seismic data of the target area;

[0126] Determine the Ricker wavelet based on the seismic data, and perform forward simulation on each skeleton well in the target area to obtain the well-seismic calibration result of the target area;

[0127] Establishing an initial inversion model based on the well-seismic calibration results, the preset initial value of the pseudo-P-wave impedance to be inverted, and the seismic data;

[0128] Perform inversion according to the initial inversion model to obtain pseudo-P-wave impedance prediction data of the associated areas of each skeleton well in the target area;

[0129] Determining the pseudo-P-wave impedance data of the target area based on the pseudo-P-wave impedance prediction data of the areas associated with each skeleton well in the target area and the pseudo-P-wave impedance data of each skeleton well; the target area includes the skeleton wells and the areas associated with the skeleton wells;

[0130] Determining the lithofacies distribution of the target area based on the pseudo-P-wave impedance data of the target area; the lithofacies include shale, sandstone and mudstone;

[0131] According to the lithofacies distribution of the target area, the vertical distribution of the sand body structure in the target area is determined.

[0132] The above-mentioned product can execute the seismic prediction method of clastic rock sand body structure provided in the embodiment of the present application, and has the corresponding functional modules and beneficial effects of the execution method.

[0133] Example 4

[0134] A fourth embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for seismic prediction of clastic sand body structure provided in all the embodiments of the present application is implemented:

[0135] Obtain well-seismic calibration results and vertical distribution of sand body structure in the target area;

[0136] According to the well-seismic calibration results, a multi-dimensional seismic waveform characteristic of the target area is obtained;

[0137] According to the multi-dimensional seismic waveform characteristics, a dimensionality reduction strategy is adopted to obtain a one-dimensional seismic waveform characteristic value; the dimensionality reduction strategy is a t-distributed random neighborhood embedding method;

[0138] Determine the planar distribution of the sand body structure in the target area based on the one-dimensional seismic waveform characteristic value; the sand body structure includes thick massive sand bodies, thick sand and thin mud interbeds, and thin sand and thick mud interbeds;

[0139] The sweet spots of the clastic shale oil reservoir are determined according to the planar distribution of the sand body structure in the target area and the vertical distribution of the sand body structure in the target area.

[0140] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0141] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0142] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0143] Computer program code for performing the operations of the present invention may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0144] Example 5

[0145] Embodiment 5 of the present application provides an electronic device. Figure 4 This is a schematic diagram of the structure of an electronic device provided in Example 5 of this application. Figure 4 As shown, this embodiment provides an electronic device 400, which includes: one or more processors 402; a storage device 401 for storing one or more programs. When the one or more programs are executed by the one or more processors 402, the one or more processors 402 implement the seismic prediction method for clastic rock sand body structure provided in the embodiment of the present application. The method includes:

[0146] Obtain well-seismic calibration results and vertical distribution of sand body structure in the target area;

[0147] According to the well-seismic calibration results, a multi-dimensional seismic waveform characteristic of the target area is obtained;

[0148] According to the multi-dimensional seismic waveform characteristics, a dimensionality reduction strategy is adopted to obtain a one-dimensional seismic waveform characteristic value; the dimensionality reduction strategy is a t-distributed random neighborhood embedding method;

[0149] Determine the planar distribution of the sand body structure in the target area based on the one-dimensional seismic waveform characteristic value; the sand body structure includes thick massive sand bodies, thick sand and thin mud interbeds, and thin sand and thick mud interbeds;

[0150] The sweet spots of the clastic shale oil reservoir are determined according to the planar distribution of the sand body structure in the target area and the vertical distribution of the sand body structure in the target area.

[0151] Of course, those skilled in the art will appreciate that the processor 402 also implements the technical solution of the seismic prediction method for clastic rock sand body structure provided in any embodiment of the present application.

[0152] Figure 4 The electronic device 400 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0153] like Figure 4 As shown, the electronic device 400 includes a processor 402, a storage device 401, an input device 403 and an output device 404; the number of processors 402 in the electronic device can be one or more. Figure 4 In the figure, a processor 402 is used as an example; the processor 402, the storage device 401, the input device 403 and the output device 404 in the electronic device can be connected by a bus or other means. Figure 4 The connection via bus 405 is taken as an example.

[0154] The storage device 401 is a computer-readable storage medium that can be used to store software programs, computer executable programs, and module units, such as program instructions corresponding to the seismic prediction method for clastic rock sand body structure in the embodiment of the present application.

[0155] The storage device 401 may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function; the data storage area may store data created based on the use of the terminal, etc. Furthermore, the storage device 401 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state memory device. In some instances, the storage device 401 may further include a memory remotely located relative to the processor 402, and such remote memory may be connected via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0156] The input device 403 may be used to receive input numbers, character information or voice information, and generate key signal input related to user settings and function control of the electronic device. The output device 404 may include electronic devices such as a display screen and a speaker.

[0157] The electronic device provided in the embodiments of this application can accurately distinguish sandstone from mudstone by constructing a pseudo-P-wave impedance expression. Furthermore, through geostatistical inversion and seismic waveform feature dimensionality reduction, it can accurately predict the vertical and horizontal distribution of sandstone structures, thereby improving the accuracy of identifying geological sweet spots in shale oil reservoirs.

[0158] The seismic prediction devices, media, and equipment for clastic sandstone structures provided in the above embodiments can implement the seismic prediction method for clastic sandstone structures provided in any embodiment of the present application, and possess the corresponding functional modules and beneficial effects of executing the method. For technical details not fully described in the above embodiments, reference can be made to the seismic prediction method for clastic sandstone structures provided in any embodiment of the present application.

[0159] Note that the above are only preferred embodiments of the present invention and the technical principles employed. Those skilled in the art will appreciate that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments, and substitutions are possible for those skilled in the art without departing from the scope of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments and may include many other equivalent embodiments without departing from the scope of the present invention. The scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for earthquake prediction of clastic rock sand body structure, characterized in that: The method comprises: Obtain logging data for each skeleton well in the target area; Determining the logging gamma data and the longitudinal wave impedance data of each skeleton well according to the logging data of each skeleton well; determining the shale distribution of each skeleton well based on the well logging gamma data and the longitudinal wave impedance data; A pseudo-P-wave impedance expression is constructed based on the P-wave impedance data, the sand-to-formation ratio, and the mud content in the logging data; in the pseudo-P-wave impedance expression, the pseudo-P-wave impedance is positively correlated with the sand-to-formation ratio, positively correlated with the initial P-wave impedance, positively correlated with the minimum P-wave impedance of sandstone and mudstone, and negatively correlated with the mud content; wherein the minimum P-wave impedance of sandstone and mudstone is obtained based on statistical analysis of the logging data; and the initial P-wave impedance is the P-wave impedance of the target layer of the skeleton well. Calculating the pseudo-P-wave impedance of the target layer of each skeleton well according to the pseudo-P-wave impedance expression, and determining the distribution of sandstone and mudstone in each skeleton well; Obtain well-seismic calibration results and vertical distribution of sand body structure in the target area; According to the well-seismic calibration results, a multi-dimensional seismic waveform characteristic of the target area is obtained; According to the multi-dimensional seismic waveform characteristics, a dimensionality reduction strategy is adopted to obtain a one-dimensional seismic waveform characteristic value; the dimensionality reduction strategy is a t-distributed random neighborhood embedding method; Determine the planar distribution of the sand body structure in the target area based on the one-dimensional seismic waveform characteristic value; the sand body structure includes thick massive sand bodies, thick sand and thin mud interbeds, and thin sand and thick mud interbeds; The sweet spots of the clastic shale oil reservoir are determined according to the planar distribution of the sand body structure in the target area and the vertical distribution of the sand body structure in the target area.

2. The method according to claim 1, characterized in that Determining the shale distribution of each skeleton well based on the well logging gamma data and the longitudinal wave impedance data includes: If the logging gamma value of the target layer of the skeleton well is greater than the logging gamma threshold of shale, and the P-wave impedance of the target layer is less than the P-wave impedance threshold of shale, then the target layer is determined to be shale; the logging gamma threshold and P-wave impedance threshold of shale are obtained based on statistical analysis of logging data.

3. The method according to claim 1, characterized in that The pseudo-longitudinal wave impedance expression is: ； in, is the pseudo-longitudinal wave impedance, is the minimum longitudinal wave impedance of sandstone and mudstone, is the sand-to-land ratio, is the initial longitudinal wave impedance, is the preset difference amplification factor between sandstone and mudstone, For the mud content.

4. The method according to claim 1, wherein The step of calculating the pseudo-P-wave impedance of the target layer of each skeleton well according to the pseudo-P-wave impedance expression and determining the distribution of sandstone and mudstone in each skeleton well includes: If the pseudo-P-wave impedance of the target layer of the skeleton well is greater than the pseudo-P-wave impedance threshold of sandstone, the target layer is determined to be sandstone; If the pseudo-P-wave impedance of the target layer of the skeleton well is less than the pseudo-P-wave impedance threshold of sandstone, the target layer is determined to be mudstone; The pseudo-P wave impedance threshold of the sandstone is obtained based on the statistical analysis of the pseudo-P wave impedance of each layer in each skeleton well.

5. The method according to claim 1, wherein The obtaining of well-seismic calibration results and vertical distribution of sand body structure in the target area includes: Acquire seismic data of the target area; Determine the Ricker wavelet based on the seismic data, and perform forward simulation on each skeleton well in the target area to obtain the well-seismic calibration result of the target area; Establishing an initial inversion model based on the well-seismic calibration results, the preset initial value of the pseudo-P-wave impedance to be inverted, and the seismic data; Perform inversion according to the initial inversion model to obtain pseudo-P-wave impedance prediction data of the associated areas of each skeleton well in the target area; Determining the pseudo-P-wave impedance data of the target area based on the pseudo-P-wave impedance prediction data of the areas associated with each skeleton well in the target area and the pseudo-P-wave impedance data of each skeleton well; the target area includes the skeleton wells and the areas associated with the skeleton wells; Determining the lithofacies distribution of the target area based on the pseudo-P-wave impedance data of the target area; the lithofacies include shale, sandstone and mudstone; According to the lithofacies distribution of the target area, the vertical distribution of the sand body structure in the target area is determined.

6. An earthquake prediction device for clastic rock sand body structure, characterized in that: The device comprises: Well logging data acquisition module, used to obtain well logging data of each skeleton well in the target area; A data determination module, configured to determine the well logging gamma data and longitudinal wave impedance data of each skeleton well based on the well logging data of each skeleton well; A shale distribution determination module, configured to determine the shale distribution of each skeleton well based on the well logging gamma data and the longitudinal wave impedance data; a pseudo-P-wave impedance expression construction module, configured to construct a pseudo-P-wave impedance expression based on the P-wave impedance data, the sand-to-formation ratio, and the shale content in the logging data; wherein the pseudo-P-wave impedance is positively correlated with the sand-to-formation ratio, positively correlated with the initial P-wave impedance, positively correlated with the minimum P-wave impedance of sandstone and mudstone, and negatively correlated with the shale content; wherein the minimum P-wave impedance of sandstone and mudstone is obtained through statistical analysis of the logging data; and the initial P-wave impedance is the P-wave impedance of the target layer of the skeleton well; a sandstone and mudstone distribution determination module, configured to calculate the pseudo-P-wave impedance of the target layer of each skeleton well according to the pseudo-P-wave impedance expression, and determine the sandstone and mudstone distribution of each skeleton well; Information acquisition module, used to obtain well-seismic calibration results and vertical distribution of sand body structure in the target area; A seismic waveform feature generation module, configured to obtain multi-dimensional seismic waveform features of a target area based on the well-seismic calibration results; A seismic waveform characteristic value generation module is used to obtain a one-dimensional seismic waveform characteristic value based on the multi-dimensional seismic waveform characteristics by adopting a dimensionality reduction strategy; the dimensionality reduction strategy is a t-distributed random neighborhood embedding method; a sand body structure plane distribution determination module, configured to determine the sand body structure plane distribution of the target area according to the one-dimensional seismic waveform characteristic value; the sand body structure includes thick massive sand bodies, thick sand and thin mud interbeds, and thin sand and thick mud interbeds; The clastic shale oil reservoir sweet spot determination module is used to determine the clastic shale oil reservoir sweet spot based on the planar distribution of the sand body structure in the target area and the vertical distribution of the sand body structure in the target area.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the seismic prediction method for clastic rock sand body structure according to any one of claims 1 to 5 is implemented.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein: When the processor executes the computer program, the seismic prediction method for clastic rock sand body structure according to any one of claims 1 to 5 is implemented.

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