Three-dimensional geologic body model construction method based on sedimentary simulation

Abstract

The invention discloses a three-dimensional geologic body model construction method based on sedimentary simulation, and the method comprises the following steps: S1, data collection: collecting various geological data of a target region, including but not limited to drilling data, seismic exploration data, a geological map, paleocurrent direction data and the like; the collected data are preprocessed, the data quality is checked, and wrong or abnormal data are removed; s2, feature parameter extraction: extracting feature parameters related to sedimentary facies from the preprocessed geological exploration data; the parameters can comprise a particle size median Md, a sorting coefficient SO, skewness SK, kurtosis Kg, a logging curve characteristic value, natural gamma GR and resistivity Rt; and S3, parameter mapping of the sedimentary facies. According to the preparation method, through parameter mapping and fine identification of sedimentary facies, the sedimentary characteristics of the geologic body are reflected more accurately, and the calculation amount is reduced and the modeling efficiency is improved while the precision of the model is ensured by the application of a self-adaptive grid division technology and a finite volume method.

Description

Technical Field

[0001] The invention relates to the technical field of waterproof coiled materials, and in particular to a method for constructing a three-dimensional geological body model based on sedimentation simulation. Background Art

[0002] The construction of three-dimensional geological models is crucial in many fields such as geological research, mineral exploration, and engineering construction. Traditional methods for constructing geological models often have some limitations. For example, some methods are only based on limited borehole data or two-dimensional geological maps for interpolation and inference, which makes it difficult to accurately reflect the real complex structure of the geological body. Especially when dealing with geological bodies with complex sedimentary environments, due to the interaction of multiple factors in the sedimentary process (such as water flow velocity, sediment source, terrain changes, etc.), traditional methods cannot simulate the formation process of the geological body well, resulting in a large deviation between the constructed three-dimensional model and the actual geological conditions.

[0003] However, some existing modeling methods are not perfect in considering the spatiotemporal evolution of geological bodies and cannot effectively integrate dynamic information during the sedimentation process, which affects the model's ability to predict changes in geological body properties and guide practical applications. Therefore, we propose a three-dimensional geological model construction method based on sedimentation simulation to solve the above problems. Summary of the invention

[0004] Based on the technical problems existing in the background technology, the present invention proposes a method for constructing a three-dimensional geological body model based on sedimentation simulation.

[0005] The present invention proposes a method for constructing a three-dimensional geological body model based on sedimentation simulation, comprising the following steps:

[0006] S1: Data collection: Collect various geological data of the target area, including but not limited to drilling data, seismic exploration data, geological maps, paleoflow direction data, etc.; pre-process the collected data, check the data quality, and eliminate erroneous or abnormal data;

[0007] S2: Feature parameter extraction: Extract the feature parameters related to the sedimentary phase from the preprocessed geological exploration data; these parameters may include the median particle size M d , sorting coefficient S O , skewness SK, kurtosis K g As well as the characteristic values ​​of logging curves, natural gamma GR, resistivity Rt;

[0008] S3: Parameter mapping of sedimentary phases: According to the sedimentary phases identified in S2, the corresponding sedimentary parameters are assigned to each phase area. Different sedimentary phases have different sedimentary parameter characteristics, such as sedimentation rate, sediment source supply rate, sediment grain size distribution, etc. For example, the sedimentation rate of the delta phase is usually higher than that of the deep-sea phase, and the grain size is relatively coarse; by establishing a mapping relationship table between sedimentary phases and parameters, accurate parameter input is provided for subsequent sedimentary simulation;

[0009] S4: Sedimentation process simulation: Based on the results of sedimentation environment analysis, a mathematical model of the sedimentation process is established. The model takes into account the physical processes of sediment transportation, deposition and compaction. For sediment transportation, appropriate fluid mechanics equations are used to describe the migration trajectory of sediments based on the relationship between water flow velocity and sediment particle size. For the sedimentation process, the accumulation position and thickness of sediments are determined based on the relationship between sedimentation rate and local environmental factors.

[0010] S5: Construction of 3D geological model: 3D space discretization: The 3D space of the study area is discretized using adaptive meshing technology. In areas where the geological body changes dramatically, such as near faults and at the boundaries of sedimentary phases, denser grids are used; in areas where the geological body is relatively uniform, sparser grids are used, which can ensure the accuracy of the model and reduce the amount of calculation;

[0011] S6: Use the finite volume method to numerically simulate the deposition process; establish the control equations of the deposition process based on the laws of conservation of mass, momentum and energy;

[0012] Combined with the sedimentation parameters and boundary conditions, these equations are solved to simulate the accumulation process of sediments in three-dimensional space at different time steps;

[0013] S7: According to the simulation results of the sedimentary process, the sedimentary layer information at each time step is integrated into the three-dimensional space. The data is filled between the grid cells using the interpolation algorithm to obtain a continuous geological body model;

[0014] At the same time, the simulation results are compared with the actual geological data to calibrate and verify the model. By adjusting the sedimentary parameters and model structure, the error between the simulation results and the actual data is minimized.

[0015] Preferably, the data collected in S1 will provide basic information for subsequent deposition simulation and model building, and the collected data is preprocessed, including data cleaning, normalization and other operations, to improve data quality.

[0016] Preferably, the sedimentary phase identification formula is:

[0017]

[0018] Where X = (M d ,S O ,SK,K g ,GR,Rt) is a vector containing the above-mentioned extracted feature parameters;

[0019] P(Ci) is the sedimentary facies category C i The prior probability of

[0020] μ i and Category C i The characteristic parameters mean vector and covariance matrix of .

[0021] Preferably, the sedimentary phase fine identification includes texture feature analysis, extracting texture features from seismic data, including grayscale co-occurrence matrix features, such as contrast, correlation, energy and entropy, and wavelet transform texture features, which can reflect the subtle differences between different sedimentary phases in seismic images.

[0022] Preferably, the sedimentary phase fine identification also includes texture feature analysis and spectrum feature analysis: spectrum analysis is performed on seismic wave data to obtain spectrum feature parameters, such as main frequency, bandwidth, spectrum slope, etc. Strata in different sedimentary phases have different absorption and reflection characteristics for seismic waves, resulting in differences in spectrum features.

[0023] Preferably, in S6, the sediment mass conservation equation is:

[0024]

[0025] Where ρ is the sediment density, φ is the porosity, v is the velocity vector of the sediment particles, and Q is the source and sink term (including the input and output of sediment).

[0026] Preferably, the three-dimensional geological model grid unit attribute value calculation formula;

[0027] P ijk =f(r ijk ,d ijk ,env)

[0028] P ijk It is the attribute value of the grid cell located at the position in the three-dimensional grid. This attribute can be geological attributes such as lithology, porosity, and permeability;

[0029] r ijk is the deposition rate corresponding to the grid cell;

[0030] d ijk is the sediment grain size corresponding to the grid cell;

[0031] env is the type of depositional environment;

[0032] f is the property calculation function determined according to geological laws.

[0033] Preferably, the constructed three-dimensional geological body model is verified by using a plurality of verification methods, and the known geological profile data is compared with the profile generated by the model to calculate the similarity index between the two, such as mean square error, correlation coefficient, etc.;

[0034] The model is optimized based on the verification results. If the similarity index does not reach the preset threshold, the data processing or model building step is returned to adjust the parameters or improve the algorithm until the model meets the accuracy requirements.

[0035] Beneficial effects of the preparation of the present invention:

[0036] 1. It can comprehensively utilize multiple sources of geological data, improve the model's ability to reflect the true situation of the geological body, and improve data quality through comprehensive utilization and preprocessing of multiple geological data, providing a guarantee for accurate modeling.

[0037] 2. Through parameter mapping and fine identification of sedimentary phases, the sedimentary characteristics of the geological body are more accurately reflected.

[0038] 3. The application of adaptive meshing technology and finite volume method reduces the amount of calculation and improves modeling efficiency while ensuring model accuracy.

[0039] 4. Through model calibration and verification, the simulation results are more consistent with the actual geological data, which improves the reliability of the model. DETAILED DESCRIPTION

[0040] The present invention will be further explained below in conjunction with specific embodiments.

[0041] In this embodiment, a method for constructing a three-dimensional geological body model based on sedimentation simulation is proposed, which includes the following steps:

[0042] S1: Data collection: Collect various geological data of the target area, including but not limited to drilling data, seismic exploration data, geological maps, paleocurrent direction data, etc.; pre-process the collected data, check the data quality, and remove erroneous or abnormal data; for example, for drilling data, check whether the depth value is reasonable and whether the lithology description is accurate; for seismic exploration data, perform pre-processing operations such as filtering to improve data quality;

[0043] More specifically, borehole data, including data such as borehole location, depth, core description, and seismic exploration profile data and interpretation results, were obtained from the geological exploration department. At the same time, geological maps of the area and related paleocurrent research reports were collected, and the borehole data were checked. It was found that there were obvious errors in the depth records of some boreholes, which were corrected by comparing with adjacent boreholes and analyzing geological maps. Spectral analysis was performed on the seismic exploration data to determine appropriate filtering parameters to remove high-frequency noise and low-frequency interference. All data were unified into the Gauss-Krüger projection coordinate system, and a weight of 0.6 was assigned to the borehole data and a weight of 0.4 was assigned to the seismic exploration data based on the data type and accuracy.

[0044] S2: Feature parameter extraction: Extract the feature parameters related to the sedimentary phase from the preprocessed geological exploration data; these parameters may include the median particle size M d , sorting coefficient S O , skewness SK, kurtosis K g As well as the characteristic values ​​of logging curves, natural gamma GR, resistivity Rt;

[0045] The sedimentary phase identification formula is:

[0046]

[0047] Where X = (M d ,S O ,SK,K g ,GR,Rt) is a vector containing the above-mentioned extracted feature parameters;

[0048] P(Ci) is the sedimentary facies category C i The prior probability of

[0049] μ i and Category C i The mean vector and covariance matrix of the characteristic parameters;

[0050] S3: Parameter mapping of sedimentary phases: According to the sedimentary phases identified in S2, the corresponding sedimentary parameters are assigned to each phase area. Different sedimentary phases have different sedimentary parameter characteristics, such as sedimentation rate, sediment source supply rate, sediment grain size distribution, etc. For example, the sedimentation rate of the delta phase is usually higher than that of the deep-sea phase, and the grain size is relatively coarse; by establishing a mapping relationship table between sedimentary phases and parameters, accurate parameter input is provided for subsequent sedimentary simulation;

[0051] S4: Sedimentation process simulation: Based on the results of sedimentation environment analysis, a mathematical model of the sedimentation process is established. The model takes into account the physical processes of sediment transportation, deposition and compaction. For sediment transportation, appropriate fluid mechanics equations are used to describe the migration trajectory of sediments based on the relationship between water flow velocity and sediment particle size. For the sedimentation process, the accumulation position and thickness of sediments are determined based on the relationship between sedimentation rate and local environmental factors.

[0052] S5: Construction of 3D geological model: 3D space discretization: The 3D space of the study area is discretized using adaptive meshing technology. In areas where the geological body changes dramatically, such as near faults and at the boundaries of sedimentary phases, denser grids are used; in areas where the geological body is relatively uniform, sparser grids are used, which can ensure the accuracy of the model and reduce the amount of calculation;

[0053] S6: The sedimentation process is numerically simulated using the finite volume method. The control equations of the sedimentation process are established based on the laws of conservation of mass, momentum and energy. The sediment mass conservation equation in S6 is:

[0054]

[0055] Where ρ is the sediment density, φ is the porosity, v is the velocity vector of the sediment particles, and Q is the source and sink term (including the input and output of sediment)

[0056] Combined with the sedimentation parameters and boundary conditions, these equations are solved to simulate the accumulation process of sediments in three-dimensional space at different time steps;

[0057] S7: Based on the simulation results of the sedimentary process, the sedimentary layer information at each time step is integrated into the three-dimensional space; the data is filled between the grid cells using the interpolation algorithm to obtain a continuous geological body model; at the same time, the simulation results are compared with the actual geological data to calibrate and verify the model.

[0058] More specifically, the error between the simulation results and the actual data is minimized by adjusting the sedimentation parameters and the model structure; the formula for calculating the attribute values ​​of the grid cells in the three-dimensional geological body model;

[0059] P ijk =f(r ijk ,d ijk ,env)

[0060] P ijk It is the attribute value of the grid cell located at the position in the three-dimensional grid. This attribute can be geological attributes such as lithology, porosity, and permeability;

[0061] r ijk is the deposition rate corresponding to the grid cell;

[0062] d ijk is the sediment grain size corresponding to the grid cell;

[0063] env is the type of depositional environment;

[0064] f is the property calculation function determined according to geological laws.

[0065] A variety of verification methods are used to verify the constructed three-dimensional geological model. The known geological profile data is compared with the profile generated by the model, and the similarity indicators between the two, such as mean square error and correlation coefficient, are calculated. The model is optimized according to the verification results. If the similarity index does not reach the preset threshold, the data processing or model building steps are returned to adjust the parameters or improve the algorithm until the model meets the accuracy requirements.

[0066] In this embodiment, the data collected in S1 will provide basic information for subsequent deposition simulation and model building. The collected data is preprocessed, including data cleaning, normalization and other operations, to improve data quality.

[0067] In this embodiment, the fine identification of sedimentary phases includes texture feature analysis, which extracts texture features from seismic data, including grayscale co-occurrence matrix features such as contrast, correlation, energy and entropy, as well as wavelet transform texture features. These texture features can reflect the subtle differences between different sedimentary phases in seismic images.

[0068] In this embodiment, the fine identification of sedimentary phases also includes texture feature analysis and spectrum feature analysis: spectrum analysis is performed on seismic wave data to obtain spectrum feature parameters, such as main frequency, bandwidth, spectrum slope, etc. Strata in different sedimentary phases have different absorption and reflection characteristics for seismic waves, resulting in differences in spectrum features.

[0069] The present invention can comprehensively utilize multiple geological data sources, improve the model's ability to reflect the real situation of the geological body, and at the same time improve data quality through comprehensive utilization and preprocessing of multiple geological data, provide a guarantee for accurate modeling, consider parameter mapping and fine identification of sedimentary phases, and more accurately reflect the sedimentary characteristics of the geological body. The application of adaptive grid division technology and finite volume method reduces the amount of calculation while ensuring the accuracy of the model, improves the modeling efficiency, and makes the simulation results more consistent with the actual geological data through model calibration and verification, thereby improving the reliability of the model.

[0070] The above are only preferred specific implementation modes of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solutions and inventive concepts of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for constructing a three-dimensional geological model based on sedimentation simulation, characterized in that: The following steps are involved: S1: Data collection: Collect various geological data of the target area, including but not limited to drilling data, seismic exploration data, geological maps, paleoflow direction data, etc.; pre-process the collected data, check the data quality, and eliminate erroneous or abnormal data; S2: Feature parameter extraction: Extract the feature parameters related to the sedimentary phase from the preprocessed geological exploration data; these parameters may include the median particle size M d , sorting coefficient S O , skewness SK, kurtosis K g As well as the characteristic values ​​of logging curves, natural gamma GR, resistivity Rt; S3: Parameter mapping of sedimentary phases: According to the sedimentary phases identified in S2, corresponding sedimentary parameters are assigned to each phase area; different sedimentary phases have different sedimentary parameter characteristics, such as sedimentation rate, sediment source supply rate, sediment grain size distribution, etc.; by establishing a mapping relationship table between sedimentary phases and parameters, accurate parameter input is provided for subsequent sedimentary simulation; S4: Sedimentation process simulation: Based on the results of sedimentation environment analysis, a mathematical model of the sedimentation process is established. The model takes into account the physical processes of sediment transportation, deposition and compaction. For sediment transportation, appropriate fluid mechanics equations are used to describe the migration trajectory of sediments based on the relationship between water flow velocity and sediment particle size. For the sedimentation process, the accumulation position and thickness of sediments are determined based on the relationship between sedimentation rate and local environmental factors. S5: Construction of 3D geological model: 3D space discretization: The 3D space of the study area is discretized using adaptive meshing technology. In areas where the geological body changes dramatically, such as near faults and at the boundaries of sedimentary phases, denser grids are used; in areas where the geological body is relatively uniform, sparser grids are used, which can ensure the accuracy of the model and reduce the amount of calculation; S6: Use the finite volume method to numerically simulate the deposition process; establish the control equations of the deposition process based on the laws of conservation of mass, momentum and energy; Combined with the sedimentation parameters and boundary conditions, these equations are solved to simulate the accumulation process of sediments in three-dimensional space at different time steps; S7: According to the simulation results of the sedimentary process, the sedimentary layer information at each time step is integrated into the three-dimensional space. The data is filled between the grid cells using the interpolation algorithm to obtain a continuous geological body model; At the same time, the simulation results are compared with the actual geological data to calibrate and verify the model. By adjusting the sedimentary parameters and model structure, the error between the simulation results and the actual data is minimized.

2. A method for constructing a three-dimensional geological model based on sedimentation simulation according to claim 1, characterized in that: The data collected in S1 will provide basic information for subsequent sedimentation simulation and model building. The collected data is preprocessed, including data cleaning, normalization and other operations, to improve data quality.

3. The method for constructing a three-dimensional geological model based on sedimentation simulation according to claim 1, characterized in that: The sedimentary phase identification formula is: Where X = (M d ,S O ,SK,K g ,GR,Rt) is a vector containing the above-mentioned extracted feature parameters; P(Ci) is the sedimentary facies category C i The prior probability of μ i and Category C i The characteristic parameters mean vector and covariance matrix of .

4. The method for constructing a three-dimensional geological model based on sedimentation simulation according to claim 1, characterized in that: The sedimentary phase fine identification includes texture feature analysis, extracting texture features from seismic data, including gray-level co-occurrence matrix features such as contrast, correlation, energy and entropy, and wavelet transform texture features. These texture features can reflect the subtle differences between different sedimentary phases on seismic images.

5. The method for constructing a three-dimensional geological model based on sedimentation simulation according to claim 1, characterized in that: The sedimentary phase fine identification also includes texture feature analysis and spectrum feature analysis: spectrum analysis is performed on seismic wave data to obtain spectrum feature parameters, such as main frequency, bandwidth, spectrum slope, etc. Strata in different sedimentary phases have different absorption and reflection characteristics for seismic waves, resulting in differences in spectrum features.

6. The method for constructing a three-dimensional geological model based on sedimentation simulation according to claim 1, characterized in that: The sediment mass conservation equation in S6 is: Where ρ is the sediment density, φ is the porosity, v is the velocity vector of the sediment particles, and Q is the source and sink term (including the input and output of sediment).

7. The method for constructing a three-dimensional geological model based on sedimentation simulation according to claim 1, characterized in that: The three-dimensional geological model grid unit attribute value calculation formula; P ijk =f(r ijk ,d ijk ,env) P ijk It is the attribute value of the grid cell located at the position in the three-dimensional grid. This attribute can be geological attributes such as lithology, porosity, and permeability; r ijk is the deposition rate corresponding to the grid cell; d ijk is the sediment grain size corresponding to the grid cell; env is the type of depositional environment; f is the property calculation function determined according to geological laws.

8. The method for constructing a three-dimensional geological model based on sedimentation simulation according to claim 1, characterized in that: The three-dimensional geological model is verified by using a variety of verification methods, and the known geological profile data is compared with the profile generated by the model to calculate the similarity index between the two, such as mean square error, correlation coefficient, etc. The model is optimized based on the verification results. If the similarity index does not reach the preset threshold, the data processing or model building step is returned to adjust the parameters or improve the algorithm until the model meets the accuracy requirements.

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