A three-dimensional geological modeling method, device, medium and equipment

Abstract

The application provides a three-dimensional geological modeling method, device, medium and equipment, and relates to the field of oilfield development geology. The method constructs a high-resolution isochronous sequence stratigraphic framework and a sedimentary facies model corresponding to a target region based on multiple detection data; embeds the sedimentary facies model in the high-resolution isochronous sequence stratigraphic framework layer by layer to obtain a three-dimensional reservoir architecture model; establishes a lithology development probability volume corresponding to the target region based on the multiple detection data and target seismic attributes; the target seismic attribute refers to a seismic attribute sensitive to lithology; the three-dimensional reservoir architecture model is constrained by using the lithology development probability volume to establish a three-dimensional reservoir lithofacies model; an attribute model corresponding to the target region is constructed, and a three-dimensional geological model corresponding to the target region is obtained in combination of the attribute model and the three-dimensional reservoir lithofacies model. Thus, the complex lithologic stratum model under the double control of the geological model and the seismic data is established, and the spatial distribution of each lithofacies of the complex lithologic stratum is finely carved and carefully described.

Description

Technical Field

[0001] This application relates to the field of oilfield development geology, and in particular to a three-dimensional geological modeling method, apparatus, medium and equipment. Background Technology

[0002] With the rapid development of computer technology, three-dimensional geological visualization of the subsurface geological environment has become a research hotspot. In the subsurface three-dimensional environment, the content and achievements of disciplines such as sequence stratigraphy, sedimentary geology, seismic sedimentology, and petroleum geology are integrated and applied to geological research. By conducting detailed three-dimensional geological modeling, an integrated model centered on data and models is established, providing a more intuitive and clear representation of the subsurface geological environment and laying the foundation for subsequent fracturing models and numerical simulations.

[0003] However, traditional geostatistical modeling methods, such as those based on variograms or multipoint statistics, cannot characterize complex lithological strata. Furthermore, the study area mainly consists of vertical or directional well clusters with local horizontal well deployments. Using conventional modeling methods will introduce uncertainties and multiple solutions between wells in the geological model, increasing the difficulty of three-dimensional characterization of unknown areas between wells in complex lithological strata. Summary of the Invention

[0004] This application provides a three-dimensional geological modeling method, apparatus, medium, and equipment, which can finely sculpt and meticulously describe the spatial distribution of various lithofacies in complex lithological strata, thereby improving the accuracy of the geological model and ultimately establishing a three-dimensional reservoir geological model that better reflects the geological conditions of the study area.

[0005] The first aspect of this application provides a three-dimensional geological modeling method, the method comprising: Based on multiple detection data, a high-resolution isochronous sequence stratigraphic framework and sedimentary facies model corresponding to the target area were constructed. The sedimentary facies model is layered and embedded into the high-resolution isochronous sequence stratigraphic framework to obtain a three-dimensional reservoir configuration model; Based on the aforementioned multiple detection data and target seismic attributes, a lithological development probability body corresponding to the target area is established; the target seismic attributes refer to seismic attributes that are sensitive to lithology. The three-dimensional reservoir configuration model is constrained by the lithological development probability volume to establish a three-dimensional reservoir lithofacies model; An attribute model corresponding to the target area is constructed, and a three-dimensional geological model corresponding to the target area is obtained by combining the attribute model and the three-dimensional reservoir facies model.

[0006] Optionally, based on multiple detection data, a high-resolution isochronous sequence stratigraphic framework corresponding to the target area is constructed, including: Sequence stratigraphy is performed on the target area based on well logging data, and structural trend constraints are applied based on seismic data to control the undulation of the strata between wells, thus obtaining an initial stratigraphic structural model. The initial stratigraphic model was fine-tuned using measured trajectory data from horizontal wells to obtain the target stratigraphic model. The target stratigraphic model is subjected to isochronous surface constraints to obtain a high-resolution isochronous sequence stratigraphic framework corresponding to the target region.

[0007] Optionally, the various detection data include single-well data, field outcrop data, and river channel seismic identification results; Based on various detection data, a sedimentary facies model corresponding to the target area is constructed, including: Sedimentary facies are divided based on single-well data to obtain single-well sand body identification results; Combining field outcrop data, river channel seismic identification results, and single-well sand body identification results, a river facies configuration model corresponding to the target area is established by layer. Sand body simulation of the fluvial facies configuration model is performed based on a multi-point geostatistical algorithm to obtain the sedimentary facies model corresponding to the target area.

[0008] Optionally, the target seismic attribute is wave impedance. Based on the various detection data and the target seismic attribute, a lithological development probability body corresponding to the target area is established, including: Seismic impedance inversion is performed based on the aforementioned multiple detection data to obtain the wave impedance value; The wave impedance value is input into a preset lithology prediction model to obtain the lithology development probability volume corresponding to the target area; wherein, the preset lithology prediction model is used to characterize the functional relationship between wave impedance and lithology development probability.

[0009] Optionally, seismic impedance inversion is performed based on seismic data to obtain wave impedance values, including: Based on seismic data, the reflection coefficient is obtained by inverting the reflection coefficient through maximum likelihood deconvolution. Based on the reflection coefficient, the initial wave impedance model is obtained by inversion using a recursive algorithm; Based on the initial wave impedance model, the wave impedance value is obtained by solving the objective optimization function.

[0010] Optionally, the lithological development probability volume is used to constrain the three-dimensional reservoir configuration model to establish a three-dimensional reservoir lithofacies model, including: Based on the lithological development probability volume, a lithological distribution model corresponding to the target area is constructed using the variogram function; Based on the lithological distribution model and the three-dimensional reservoir configuration model, multi-source information fusion is performed to obtain the three-dimensional reservoir lithofacies model corresponding to the target area.

[0011] Optionally, the method further includes: Obtain the post-test well data corresponding to the target area; Based on the post-well verification data, the three-dimensional geological model corresponding to the target area was verified from both the horizontal section and the vertical section, and the verification results were obtained. The three-dimensional geological model was optimized and adjusted based on the verification results.

[0012] Based on the same inventive concept, a second aspect of the present application provides a three-dimensional geological modeling apparatus, the apparatus comprising: The module is used to construct a high-resolution isochronous sequence stratigraphic framework and sedimentary facies model for the target area based on various detection data. An embedding module is used to embed the sedimentary facies model layer by layer into the high-resolution isochronous sequence stratigraphic framework to obtain a three-dimensional reservoir configuration model. The lithology probability prediction module is used to establish a lithology development probability body corresponding to the target area based on the various detection data and target seismic attributes; the target seismic attributes refer to seismic attributes that are sensitive to lithology. The constraint module is used to constrain the three-dimensional reservoir configuration model using the lithological development probability volume, and to establish a three-dimensional reservoir lithofacies model. The module is used to construct the attribute model corresponding to the target area, and by combining the attribute model and the three-dimensional reservoir facies model, a three-dimensional geological model corresponding to the target area is obtained.

[0013] Based on the same inventive concept, a third aspect of the present application provides a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the three-dimensional geological modeling method proposed in the first aspect of the present application.

[0014] Based on the same inventive concept, a fourth aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executed, implements the three-dimensional geological modeling method proposed in the first aspect of this application.

[0015] Compared with the prior art, this application has the following advantages: This application provides a three-dimensional geological modeling method that, based on multiple detection data, constructs a high-resolution isochronous sequence stratigraphic framework and sedimentary facies model corresponding to a target area; embeds the sedimentary facies model layer by layer into the high-resolution isochronous sequence stratigraphic framework to obtain a three-dimensional reservoir configuration model; establishes a lithological development probability volume corresponding to the target area based on multiple detection data and target seismic attributes; the target seismic attributes refer to seismic attributes sensitive to lithology; constrains the three-dimensional reservoir configuration model using the lithological development probability volume to establish a three-dimensional reservoir lithofacies model; constructs an attribute model corresponding to the target area, and combines the attribute model and the three-dimensional reservoir lithofacies model to obtain a three-dimensional geological model corresponding to the target area.

[0016] Therefore, by deeply mining the seismic data of complex lithological strata and applying it to the geological modeling process, a complex lithological strata model under the dual control of geological model and seismic data is established. The spatial distribution of each lithofacies in the complex lithological strata is finely sculpted and meticulously described, thereby improving the accuracy of the geological model, reducing the uncertainty of the geological model, and finally establishing a three-dimensional reservoir geological model that is more in line with the geological status of the study area. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a three-dimensional geological modeling method in one embodiment of this application; Figure 2 This is a schematic diagram of the structure of a high-resolution isochronous sequence stratigraphic framework in one embodiment of this application; Figure 3 This is a schematic diagram of a three-dimensional geological model constructed in one embodiment of this application; Figure 4 This is a schematic diagram of an ancient outcrop braided river in the wild, according to one embodiment of this application; Figure 5 This is a plan view of a modern braided river facies in one embodiment of this application; Figure 6 This is a distribution map of the width of a single river channel in one embodiment of this application; Figure 7 This is a schematic diagram of a single river channel model constructed in one embodiment of this application; Figure 8 This is a schematic diagram of a river channel model constructed in one embodiment of this application; Figure 9This is a schematic diagram of a river facies model constructed in one embodiment of this application; Figure 10 This is a schematic cross-sectional view of a river facies model in one embodiment of this application; Figure 11 This is a schematic diagram of a coal and rock development probability body in one embodiment of this application; Figure 12 This is a schematic diagram of a coal and rock distribution model in one embodiment of this application; Figure 13 This is a schematic diagram of the actual drilling of a blind well in one embodiment of this application; Figure 14 This is a schematic diagram of the functional modules of a three-dimensional geological modeling device according to one embodiment of this application; Figure 15 This is a schematic diagram of the structure of an electronic device according to one embodiment of this application. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] Geological modeling methods are divided into two categories: deterministic modeling and stochastic modeling. The modeling process is an integration of well logging, seismic, and other data to predict subsurface properties (such as lithology). Kriging is commonly used in deterministic modeling, but it struggles to characterize the spatial heterogeneity of the data. Stochastic modeling commonly employs methods such as sequential Gaussian simulation, sequential indicator simulation, and Markov random domains, but these methods cannot guarantee accuracy in unknown areas between wells. Therefore, how to incorporate geological results as constraints during modeling in complex lithological strata has been a hot research topic for scholars both domestically and internationally. Many researchers have conducted studies; some have extracted sensitive seismic attributes from seismic data to model fractures in the Bohai Bay Basin, but this model is highly dependent on seismic data. Others have applied phase control principles and seismic fracture development characteristics to fracture modeling, but current models for complex lithological strata are mostly controlled by a single factor, which does not reflect the characteristic of strata being controlled by multi-factor information.

[0021] Based on the above analysis, the main problems in the relevant technologies are: existing three-dimensional reservoir modeling technologies rely too much on known data, and the prediction of unknown areas between wells has great uncertainty. Furthermore, the seismic information of seismic data on complex lithological strata has not been thoroughly explored and applied to geological modeling.

[0022] In view of this, this application proposes a three-dimensional geological modeling method. By deeply mining the seismic data of complex lithological strata and applying it to the geological modeling process, a complex lithological strata model under the dual control of geological model and seismic data is established. The spatial distribution of each lithofacies in the complex lithological strata is finely sculpted and meticulously described, thereby improving the accuracy of the geological model, reducing the uncertainty of the geological model, and finally establishing a three-dimensional reservoir geological model that is more consistent with the geological status of the study area.

[0023] Please refer to Figure 1 , Figure 1 This is a flowchart of a three-dimensional geological modeling method proposed in one embodiment of this application. Figure 1 As shown, the method includes the following steps: S101: Based on multiple detection data, construct a high-resolution isochronous sequence stratigraphic framework and sedimentary facies model corresponding to the target area.

[0024] In this embodiment, various detection data include, but are not limited to: well logging data, core data, stratigraphic data, physical property data, seismic data, dynamic data, geological research results, etc. Well logging data refers to various geological and engineering data obtained from a single well, including well logging curves for natural gamma, resistivity, sonic transit time, density, etc. Core data refers to core samples obtained through coring and their analysis results. Well logging data and core data can be used to analyze the formation characteristics, lithology, and physical properties around the wellbore. Stratigraphic data refers to the division of strata into different segments based on geological characteristics, which can be used to establish a stratigraphic framework. Physical property data refers to numbers describing the physical properties of rocks, including porosity, permeability, saturation, etc., which can be used to evaluate the reservoir's storage performance and fluid flow characteristics. Seismic data is seismic wave reflection data obtained through seismic exploration, including seismic profiles (cross-sectional images showing the reflection characteristics of underground strata) and seismic attributes (such as amplitude, frequency, phase, etc.), which can be used to identify the geological characteristics of underground strata, such as structure, lithology, and faults. Dynamic data refers to the dynamic changes in oil and gas wells during the production process, including production data, pressure data, and production test data (such as well test data and production logging data). This data can be used to analyze the development dynamics, production capacity changes, and development effects of oil and gas reservoirs. Geological research results refer to various conclusions and understandings obtained through geological research, including sedimentary facies studies (such as sedimentary environments, sedimentary facies types and their distribution), structural studies (such as faults, folds and other structural features and their evolutionary history), and reservoir studies (such as reservoir distribution, reservoir properties, and reservoir heterogeneity). These data are used to guide oil and gas exploration and development and optimize exploration and development plans.

[0025] The target area refers to the region where geological research will be conducted. After obtaining various detection data corresponding to the target area, a high-resolution isochronous sequence stratigraphic framework for the target area is first constructed.

[0026] For example, stratigraphic interfaces can be identified based on well logging curve characteristics (such as natural gamma and spontaneous potential), stratigraphic correlation and division can be performed in conjunction with seismic data, and the interpreted stratigraphic and fault results can be input into a specialized software module (such as the z-map module of the landmark software) to generate a stratigraphic framework map of the target area. Then, within the framework, the grid is cut according to the isochronous sequence interfaces to form sequence stratigraphic units (Sequence Zones), and the strata are divided into multiple levels according to the principles of sequence stratigraphy (such as sequence SQ1, lowstand systems tract, channel microfacies, and single sand layers). It should be noted that, considering the severe heterogeneity within the sand bodies in the geological model, the vertical grid setting of the stratigraphic framework should be as fine as possible to retain as much geological information as possible, thereby establishing a high-resolution isochronous sequence stratigraphic framework.

[0027] For example, the constructed high-resolution isochronous sequence stratigraphic framework is as follows: Figure 2 As shown in the figure. The X and Y planes represent geographic spatial locations (such as longitude, latitude, or local projected coordinates), reflecting the horizontal distribution characteristics of the strata; the Z direction represents the mixed dimension of the time-depth domain.

[0028] Simultaneously, a sedimentary facies model corresponding to the target area is constructed. Sedimentary facies is the sum of the characteristics of sediments and their formation environment, describing the characteristics of sediments and the geographical environment in which they formed during a specific geological period. By constructing a sedimentary facies model, the size, geometry, and three-dimensional spatial distribution of reservoir sand bodies in the target area can be described. For example, sequential indicator simulation (SIS) and multi-point geostatistical simulation (MPS) methods can be used to establish sedimentary facies models.

[0029] S102: The sedimentary facies model is embedded layer by layer into a high-resolution isochronous sequence stratigraphic framework to obtain a three-dimensional reservoir configuration model.

[0030] In this embodiment, layered embedding refers to the process of gradually integrating sedimentary facies models from the sequence level to the microfacies level into the high-resolution isochronous sequence stratigraphic framework based on the aforementioned hierarchical division.

[0031] During the embedding process, the high-resolution isochronous sequence stratigraphic framework provides a "container" for sedimentary facies, restricts the spatial boundaries of sedimentary facies models (such as stratigraphic top and bottom interfaces and fault closure), and constrains the vertical stratification of sedimentary facies distribution (such as different sequence units corresponding to different sedimentary environments), forcing sedimentary facies not to cross faults or sequence interfaces to ensure spatial rationality.

[0032] By using hierarchical embedding, the complexity of a single simulation can be reduced, ensuring the logical consistency between large-scale facies zones and small-scale microfacies, and enabling a gradual characterization from macro to micro, which is especially suitable for highly heterogeneous reservoirs (such as fluvial facies and turbidites).

[0033] Therefore, by embedding sedimentary facies models layer by layer into a high-resolution isochronous sequence stratigraphic framework, a three-dimensional reservoir configuration model is obtained. The three-dimensional reservoir configuration model can be regarded as a three-dimensional quantitative model that integrates the spatial configuration of sedimentary facies and reservoir parameters (porosity, permeability), reflecting the heterogeneity of the reservoir and fluid flow units.

[0034] In addition, to ensure the accuracy of the three-dimensional reservoir configuration model, adjustments can be made based on relevant detection data. For example, production data (such as water cut) can be inverted into the three-dimensional reservoir configuration model to adjust the connectivity of high-permeability facies zones (such as channel microfacies), thereby optimizing the three-dimensional reservoir configuration model.

[0035] S103: Based on multiple detection data and target seismic attributes, establish a lithological development probability body corresponding to the target area.

[0036] In this embodiment, the target seismic attribute refers to a lithology-sensitive seismic attribute, that is, a seismic-derived parameter sensitive to lithological changes, such as wave impedance, AVO gradient, and spectral decomposition energy. The target seismic attribute can indirectly reflect the lithological differences between sandstone, mudstone, carbonate rocks, etc.

[0037] The lithological development probability volume is a three-dimensional grid data volume. The value of each grid point (0~1) represents the probability of the existence of a specific lithology (such as sandstone). It serves as a bridge connecting seismic attributes and geological models.

[0038] In this implementation, the first step is to select the target seismic attribute needed for geological modeling of the target area by performing correlation analysis on various detection data, including plane seismic attributes and well logging interpretation results. For example, after time-depth conversion of seismic inversion data, statistical analysis of well lithology and seismic attributes is performed based on lithological interpretation results and corresponding seismic attributes. The analysis shows that P-wave impedance attributes have a better correlation with lithology, so P-wave impedance is selected as the target seismic attribute to establish the lithological development probability volume corresponding to the target area.

[0039] In establishing the lithological development probability volume corresponding to the target area, a nonlinear relationship between lithological development probability and P-wave impedance can first be established using random forest (RF) or support vector machine (SVM). Then, based on various detection data of the target area, the actual P-wave impedance is determined, and the lithological development probability volume corresponding to the target area is determined according to the existing nonlinear relationship between lithological development probability and P-wave impedance.

[0040] S104: Using the lithological development probability volume to constrain the three-dimensional reservoir configuration model, a three-dimensional reservoir lithofacies model is established.

[0041] For example, under the control of a three-dimensional reservoir configuration model, different variation functions can be set for each configuration unit, and under the trend constraint of the corresponding lithofacies development probability volume, a sequential indicator simulation method can be used to perform three-dimensional prediction (simulation or interpolation) between wells, thereby establishing a three-dimensional reservoir lithofacies model for the target area.

[0042] In this embodiment, by adding lithological development probability volumes to the lithofacies modeling of each single sand layer in the three-dimensional reservoir configuration model for collaborative constraints, the soft constraints of seismic data on the data-free areas between wells in the three-dimensional geological model are finally obtained. The lithological development probability volumes obtained from seismic data make up for the lack of data between wells, reduce the subjectivity of modeling, and thus establish a higher quality three-dimensional reservoir lithofacies model.

[0043] S105: Construct the attribute model corresponding to the target area, and combine the attribute model with the three-dimensional reservoir facies model to obtain the three-dimensional geological model corresponding to the target area.

[0044] In this embodiment, the attribute models corresponding to the target area include porosity models, permeability models, and oil (or gas) saturation models. Taking the porosity model as an example, based on the results of well logging data analysis (mainly referring to the probability density function characteristic values ​​of well logging data analysis), under the control of the three-dimensional reservoir facies model, combined with variogram analysis, and using the sequential indicator simulation method, a stochastic porosity model corresponding to the target area is established.

[0045] Then, by embedding the attribute model into the three-dimensional reservoir lithofacies model, a three-dimensional geological model corresponding to the target area is obtained, such as... Figure 3 As shown. Specifically, a hierarchical nesting approach is adopted, using the 3D reservoir lithofacies model as a "container" for the attribute model. The attribute model is only valid within the corresponding lithofacies grid (e.g., porosity is only assigned to the sandstone grid). Simultaneously, sedimentary facies boundaries control lithofacies distribution, and lithofacies further constrain attribute distribution (e.g., channel facies, sandstone, high-porosity zones). Furthermore, in reservoir numerical simulation, the 3D reservoir lithofacies model defines the boundaries of flow units, the attribute model provides physical property parameters, and ultimately, a 3D geological model corresponding to the target area is established.

[0046] This embodiment deeply mines seismic data on complex lithological strata and applies it to the geological modeling process. It establishes a complex lithological strata model under the dual control of geological model and seismic data, and finely sculpts and describes the spatial distribution of each lithofacies in the complex lithological strata, thereby improving the accuracy of the geological model, reducing its uncertainty, and ultimately establishing a three-dimensional reservoir geological model that better reflects the geological conditions of the study area. It can solve the problems of efficient and automatic reconstruction of complex geological body structure models with subsurface anisotropy, the integrated modeling and expression of geological framework models and fine attribute models, and whether the established model can support geological quantitative analysis, evaluation, and visualization decision-making.

[0047] Optionally, based on multiple detection data, a high-resolution isochronous sequence stratigraphic framework corresponding to the target area is constructed, including: S201: Sequence stratigraphy is performed on the target area based on well logging data, and structural trend constraints are applied based on seismic data to control the undulation of the strata between wells, thus obtaining an initial stratigraphic structural model.

[0048] In this embodiment, during the initial stratigraphic model construction, on the one hand, well logging curves can be used to delineate key strata such as sandstone-shale interfaces and unconformities. Then, well logging depths are used as hard data for sequence stratigraphy, accurately calibrating the top and bottom depths of the strata to ensure sufficient accuracy of the stratigraphic interfaces near the wellbore during modeling. On the other hand, seismic data provides laterally continuous structural information (resolution approximately 10-20m), compensating for the lack of inter-well coverage in well logging data. Therefore, a top-surface structural map can be extracted based on seismic reflection phase axes (e.g., converted from the time domain to the depth domain), and the structural trends interpreted by seismic analysis are used as soft constraints to control the undulations of the strata between wells. Thus, by combining depth-based stratigraphic data and lateral structural surface interpretation data, an initial stratigraphic model is obtained.

[0049] For example, if seismic data shows that a certain stratum is a westward-dipping monocline structure, then when constructing the initial stratigraphic structure model and performing well-to-well interpolation, this trend must be followed to avoid local structural anomalies and distortions.

[0050] S202: The initial stratigraphic structural model is fine-tuned using the measured trajectory data of horizontal wells to obtain the target stratigraphic structural model.

[0051] In this embodiment, the measured trajectory data of the horizontal well obtained when drilling along the formation (such as logging while drilling, LWD) can provide structural feedback with meter-level accuracy, which can be used to correct errors in seismic interpretation.

[0052] For example, when the deviation between the actual drilling trajectory and the predicted depth of a horizontal well is greater than 1m, the structural surface is adjusted in the opposite direction (such as local uplift or depression), and the formation contact relationship is identified using drilling gamma or resistivity data, and the initial formation structure model is updated in real time to achieve fine-tuning of the initial formation structure model.

[0053] S203: Apply isochronous surface constraints to the target stratigraphic model to obtain the high-resolution isochronous sequence stratigraphic framework corresponding to the target area.

[0054] In this embodiment, key isochronous interfaces such as the maximum flooding surface and unconformity can be identified through well logging sequence division and seismic sequence analysis. Then, under the constraint of the isochronous surface, layer-by-layer modeling is carried out using the layer flattening technique to ensure that each sequence unit is from the same depositional period.

[0055] Based on this, the vertical grid can be further dynamically divided. For example, a 1-5m vertical grid can be used for reservoir sections, and a 10-20m grid can be used for non-reservoir sections, thereby balancing model accuracy and computational efficiency, and finally obtaining a high-resolution isochronous sequence stratigraphic framework corresponding to the target area.

[0056] This embodiment uses well logging data to provide absolute depth control (skeleton), seismic data to constrain macroscopic structural trends (veins), and horizontal well trajectories to achieve local fine-tuning (blood vessels). Thus, well point depth is mainly controlled by well logging layer data, inter-layer structural trends are controlled by seismic interpretation and overlying strata structure, and the structure is fine-tuned by horizontal well drilling trajectories. Finally, a stratigraphic structural map is compiled by integrating multiple data applications, and a high-resolution isochronous sequence stratigraphic framework is established.

[0057] Optionally, a variety of detection data can be used, including single-well data, field outcrop data, and river channel seismic identification results.

[0058] In this embodiment, single-well data refers to vertically continuous geological information obtained through drilling, including well logging curves, core descriptions, and oil testing data. This constitutes the most direct "hard data" in 3D modeling. Outcrop data consists of exposed rock profiles or modern sedimentary bodies on the surface. Information such as sedimentary configuration and lithofacies distribution, obtained through field measurements and photogrammetry, can provide training images for multi-point geostatistics (MPS) and address uncertainties in inter-well predictions. Channel seismic identification results are information on the spatial distribution of paleochannels identified through seismic attribute extraction and interpretation techniques. This includes channel boundaries, migration paths, and sand body thickness. This is considered "soft data" and can fill gaps in inter-well scale information.

[0059] The above-mentioned sedimentary facies model for the target region is constructed based on various detection data, including: S301: Sedimentary facies are divided based on single-well data to obtain single-well sand body identification results.

[0060] In this embodiment, by identifying the vertical sedimentary facies sequence based on single-well data (including well logging data and core data), a sedimentary environment calibration at the well point scale is established, and the sedimentary facies type at each depth is marked to obtain the single-well sand body identification results.

[0061] For example, vertical stacking patterns can be calibrated (such as channel sand body thickness and mudstone interlayer frequency). This allows for a quantitative description of the geometry (length, width, and thickness) and spatial configuration of individual sand bodies based on sedimentary facies classification.

[0062] S302: Combining field outcrop data, river channel seismic identification results, and single-well sand body identification results, a river facies configuration model corresponding to the target area is established by layer.

[0063] In this embodiment, by combining field outcrop data, river channel seismic identification results, and single-well sand body identification results, a river facies configuration pattern of the target area is established by layer, including length, width, and curvature, etc., and this is used as hard data for river facies modeling to establish a river facies model.

[0064] For example, please refer to Figures 4-6 ,in, Figure 4 This is a schematic diagram of ancient braided river outcrops in the target area. Figure 5 This is a planar distribution map of modern braided river facies. Figure 6 This is a map showing the distribution of single river channel widths. In this embodiment, by combining field survey data of outcrops and modern braided rivers, the geometric morphology, scale parameters, and correlations of different types of river channels are statistically analyzed (e.g., Figure 6 As shown in the figure, this serves as a reference standard for predicting the size of a single river channel sand body in the target area.

[0065] Specifically, for each layer (such as SQ1 and SQ2), the following configuration parameters are quantified: 1. Geometric morphology, including length (single-stage channel extension distance), width (single channel width and total channel width determined based on field outcrop data or channel seismic identification results), and sinuosity (the ratio of channel length to straight-line distance); 2. Vertical stacking, including width-to-thickness ratio (the ratio of channel width to sand body thickness calculated based on field outcrop data) and connectivity (the lateral contact relationship of sand bodies based on well-to-well correlation, such as cut-and-stack or isolated types).

[0066] Then, the parameters measured by the field outcrop data (such as a single channel width of 200m) are used as the prototype model and converted to the geological age of the target area according to the sedimentation rate for outcrop calibration. The channel direction is extracted by the seismic horizontal slices in the channel seismic identification results and fused with the outcrop parameters to generate a planar trend field to achieve seismic constraints. The original channel depth is inferred by the thickness of the sand body in a single well (such as 5m) (based on a width-to-thickness ratio of 100:1, the original width is estimated to be about 500m) to achieve well control correction.

[0067] Ultimately, based on these parameters, a conceptual river network model, namely a river facies configuration model, is generated as input for multi-point geostatistical simulations.

[0068] S303: Sand body simulation of fluvial facies configuration model based on multi-point geostatistical algorithm to obtain sedimentary facies model corresponding to target area.

[0069] In this embodiment, a sedimentary facies distribution model that conforms to geological laws is constructed by extrapolating single-well and configuration data to three-dimensional space.

[0070] It should be noted that, considering the highest oil and gas reserves in fluvial reservoirs among sedimentary facies, this embodiment mainly focuses on constructing a fluvial sedimentary facies model, i.e., a fluvial facies model. For example, based on single-well interpretation, the parameters (length and width, etc.) and distribution direction of channels at different strata are used as constraints in the simulation process in the form of variograms, and a multi-point geostatistical algorithm is used to stochastically simulate the fluvial sand bodies to obtain the fluvial facies model corresponding to the target area.

[0071] For example, please refer to Figures 7-10 ,in, Figure 7 This is a schematic diagram of a single river channel model. Figure 8 This is a schematic diagram of the constructed river channel model. Figure 9 This is a schematic diagram of the constructed river facies model. Figure 10 This is a schematic cross-sectional view of a river facies model.

[0072] Specifically, the channel direction and width in the river facies configuration model are directly converted into a 3D wireframe model (such as the Polygon in Petrel), forcing the channel distribution in subsequent simulations to follow this framework. Within the channel zone, the sand body thickness distribution is constrained according to the width-to-thickness ratio (e.g., a 300m wide channel corresponds to a 3-4m thick sand body), thus achieving hard constraints on river facies modeling. Then, the main range is adjusted according to the tortuosity parameter (e.g., high-torsional-torsional-to-anisotropic model, with a range of 2000m along the channel direction and 500m vertically), and the overlay pattern in the river facies configuration model (e.g., tangential overlay) is converted into a 3D training image to guide the multi-point geostatistical algorithm in simulating sand body connectivity, thereby achieving soft constraints in stochastic simulation, and finally obtaining the sedimentary facies model corresponding to the target area.

[0073] This embodiment reduces the uncertainty of inter-well prediction during sedimentary facies modeling by calibrating outcrop-seismic-logging ternary data, and achieves "geological process-driven" reservoir prediction by constraining stochastic simulation with hard data, which helps improve the accuracy of subsequent 3D lithofacies modeling.

[0074] Optionally, the target seismic attribute is wave impedance. Based on multiple detection data and the target seismic attribute, the above establishes a lithological development probability body corresponding to the target area, including: S401: Seismic wave impedance inversion is performed based on multiple detection data to obtain wave impedance values.

[0075] In this embodiment, considering that changes in the lithology and physical properties of underground geological bodies often lead to variations in rock density and seismic wave propagation velocity, resulting in changes in wave impedance, the change in wave impedance at the lithology-physical property interface generates a reflection coefficient pulse. When a seismic wave propagates to the wave impedance interface, a reflected wave is generated at that interface. Therefore, this embodiment first verifies the sensitivity of wave impedance to lithology by analyzing the correlation between the P-wave and S-wave velocity ratios and wave impedance with different lithologies, in order to determine whether wave impedance should be used as a target seismic attribute for geological modeling.

[0076] Once wave impedance is determined as the target seismic attribute, seismic wave impedance inversion can be performed based on various detection data to obtain the wave impedance value.

[0077] Specifically, the reflection coefficient is first obtained by inverting the reflection coefficient based on seismic data through maximum likelihood deconvolution.

[0078] Maximum likelihood deconvolution assumes that the formation's reflection coefficient is a combination of reflections from larger reflective interfaces and smaller reflections with a Gaussian background. Therefore, a minimum objective function can be derived:

[0079] Where J represents the minimum objective function, R 2 N represents the mean square value of the reflection coefficient. 2 Let r(K) represent the mean square value of the noise, r(K) represent the reflection coefficient of the Kth sampling point, n(K) represent the noise at the Kth sampling point, M represent the number of reflection layers, and L represent the total number of sampling points. This represents the likelihood value of a given reflection coefficient.

[0080] Therefore, based on the above formula, the reflection coefficient R is obtained through multiple iterations.

[0081] Then, based on the reflection coefficient, the initial wave impedance model is obtained by recursion algorithm, and the wave impedance value is obtained by solving the objective optimization function based on the initial wave impedance model.

[0082] Specifically, the initial wave impedance model can be obtained by recursively inverting the following formula:

[0083] Where Z(i) is the wave impedance value of the i-th layer, and R(i) is the reflection coefficient of the i-th layer.

[0084] Finally, wave impedance inversion is performed based on the constraints of well logging data. The calculated initial wave impedance is adjusted according to the objective optimization function, including the adjustment of the reflection coefficient, and the wave impedance value is finally obtained.

[0085] The objective optimization function is:

[0086] Where r is the reflection coefficient sequence, ΔZ is the impedance trend difference sequence, d is the seismic trace sequence, s is the synthetic seismic trace sequence, λ is the residual weighting factor, a is the trend weighting factor, and p and q are L-mode factors. Specifically, the first term on the right reflects the sum of the absolute values ​​of the reflection coefficients, the second term reflects the difference between the synthetic acoustic record and the original seismic data, and the third term is the trend constraint term.

[0087] S402: Input the wave impedance value into the preset lithology prediction model to obtain the lithology development probability volume corresponding to the target area; wherein, the preset lithology prediction model is used to characterize the functional relationship between wave impedance and lithology development probability.

[0088] In this embodiment, the pre-set lithology prediction model is obtained by training a neural network model and represents the nonlinear mapping relationship between wave impedance and lithology development probability. Specifically, firstly, based on sample seismic data, wave impedance values ​​and corresponding lithology labels (e.g., 0 = mudstone, 1 = sandstone) are extracted from the seismic traces near the well. Then, the data is input into a neural network (e.g., an MLP) to enable the neural network to learn the probabilistic relationship between wave impedance and lithology, thereby training the network model and ultimately obtaining the lithology prediction model.

[0089] After calculating the wave impedance value for the target area, inputting it into the lithology prediction model will output the lithology probability of each grid point, thereby obtaining the lithology development probability volume corresponding to the target area.

[0090] Traditional linear regression or statistical methods (such as Bayesian classification) are difficult to capture the nonlinear relationship between wave impedance and complex lithology (such as interbedded sandstone and mudstone, fractured carbonate rocks). However, deep neural networks can automatically and efficiently learn the high-order feature mapping between wave impedance and lithology, which can improve the accuracy of lithology identification, thereby improving the accuracy of reservoir characterization and promoting the construction of high-precision three-dimensional geological models.

[0091] Optionally, the above-mentioned use of lithological development probability volumes to constrain the three-dimensional reservoir configuration model and establish a three-dimensional reservoir lithofacies model includes: S501: Based on the lithological development probability volume, a lithological distribution model corresponding to the target area is constructed using the variogram function.

[0092] In this embodiment, after obtaining the lithological development probability volume of the target area, the Sequential Indicator Simulation (SISIM) method can be used to transform the probability volume into a discrete lithological model based on the variation function. For example, by assigning a discrete lithological code (e.g., 1=sandstone, 2=mudstone) to each cell in the three-dimensional grid, a lithological distribution model can be obtained.

[0093] For example, suppose the probability volume of coal and rock development in the target area is as follows: Figure 11 As shown. Under the constraint of the coal and rock development probability volume, using well point interpretation results as conditional data, the multi-point simulation scan template is set to 15×15×3, the multi-grid level is set to 3, the maximum number of known neighboring nodes is set to 8, and the input coal and rock training image is used, combined with the boundary control of the sedimentary facies model, to obtain the coal and rock distribution model of the target area, as shown. Figure 12 As shown. The input coal and rock training images are a priori geological models (such as the geometry of river channels and sandbars) constructed through geostatistics to reflect the spatial distribution patterns of lithology, and can provide multi-point statistical regularities.

[0094] S502: Based on the lithological distribution model and the three-dimensional reservoir configuration model, multi-source information is fused to obtain the three-dimensional reservoir lithofacies model corresponding to the target area.

[0095] In this embodiment, based on the lithological distribution model and the three-dimensional reservoir configuration model, multi-source information is fused under the condition of conforming to the geological model and statistical regularity, and finally a three-dimensional reservoir lithofacies model under the synergistic control of geological model and seismic constraints is established.

[0096] Specifically, the probability fusion method can use permanence ratios (PR). The PR model calculates the cumulative product of multiple probabilities, and its formula is as follows:

[0097] Where L represents lithology, G represents the geostatistical model, and Z represents the inverted wave impedance. The probability of fusion of lithological development under the influence of multiple factors. The prior probability of lithological development. The probability of lithological development under geostatistical conditions. This represents the probability of lithological development under impedance inversion conditions.

[0098] Optionally, the method further includes: acquiring post-well data corresponding to the target area; verifying the three-dimensional geological model corresponding to the target area based on the post-well data, from both the horizontal section and the vertical section, and obtaining the verification results; and optimizing and adjusting the three-dimensional geological model based on the verification results.

[0099] In this embodiment, post-well verification data refers to geological characteristic data such as actual formation thickness, lithology, porosity, permeability, and oil saturation obtained through actual drilling of blind wells.

[0100] After constructing the 3D geological model, the geological modeling effect is analyzed by comparing the post-well verification data with the relevant data predicted by the 3D geological model, and the errors between the model and the actual data are evaluated, including errors in stratigraphic morphology, lithological distribution, and fluid properties. Then, based on the verification results obtained from the comparative analysis, the accuracy and precision of the 3D geological model are judged. If the error is large, the parameters of the 3D geological geometry model need to be adjusted according to the verification results, such as stratigraphic thickness and lithological distribution. If the error is mainly concentrated in certain areas, the mesh in these areas can be refined to improve the model's precision.

[0101] For example, please refer to Figure 13 , Figure 13 This is a schematic diagram of the actual drilling of a blind well according to one embodiment of this application. Figure 13 As shown, the actual drilled blind well is divided into two parts: a horizontal section and a vertical section, and each is verified separately. For the horizontal section, the actual length is assumed to be 1350m, the coal seam encountered is 1260m, and the coal seam encounter rate is 93.3%. However, the logging curves derived from the 3D geological model indicate that the predicted coal seam length is 1219m, and the predicted coal seam encounter rate is 90.3%. Therefore, through comparative analysis, the error in the coal seam encounter rate is found to be less than 3%, indicating that the prediction results are consistent with reality, and the constructed 3D geological model has high accuracy, meeting the expected requirements. However, if the error is large after comparison, such as 10%, then the 3D geological model needs to be optimized and adjusted.

[0102] Please refer to Figure 14 Based on the same inventive concept, a second aspect of this application provides a three-dimensional geological modeling device, the three-dimensional geological modeling device 14 comprising: Module 141 is used to construct a high-resolution isochronous sequence stratigraphic framework and sedimentary facies model corresponding to the target area based on multiple detection data. Embedding module 142 is used to embed the sedimentary facies model into a high-resolution isochronous sequence stratigraphic framework to obtain a three-dimensional reservoir configuration model. The lithology probability prediction module 143 is used to establish a lithology development probability body corresponding to the target area based on multiple detection data and target seismic attributes; the target seismic attributes refer to seismic attributes that are sensitive to lithology. Constraint module 144 is used to constrain the three-dimensional reservoir configuration model using the lithological development probability volume and establish a three-dimensional reservoir lithofacies model; Module 145 is used to construct the attribute model corresponding to the target area, and by combining the attribute model and the three-dimensional reservoir facies model, a three-dimensional geological model corresponding to the target area is obtained.

[0103] Optionally, building module 141 includes: The stratigraphic architecture construction submodule is used to perform sequence stratigraphic division of the target area based on well logging data and to perform structural trend constraints based on seismic data to control the undulation morphology of the strata between wells and obtain the initial stratigraphic structure model. The stratigraphic structure fine-tuning submodule is used to fine-tune the initial stratigraphic structure model using measured trajectory data from horizontal wells to obtain the target stratigraphic structure model. The high-resolution stratigraphic framework generation submodule is used to apply isochronous surface constraints to the target stratigraphic structural model to obtain the high-resolution isochronous sequence stratigraphic framework corresponding to the target region.

[0104] Optionally, various detection data include single-well data, field outcrop data, and river channel seismic identification results; Module 141 also includes: The single-well segmentation submodule is used to segment sedimentary facies based on single-well data and obtain single-well sand body identification results. The river facies model construction submodule is used to combine field outcrop data, river channel seismic identification results and single-well sand body identification results to establish a river facies configuration model corresponding to the target area in layers. The sedimentary facies model construction submodule is used to simulate sand bodies based on multi-point geostatistical algorithms to obtain the sedimentary facies model corresponding to the target area.

[0105] Optionally, the target seismic attribute is wave impedance, and the lithology probability prediction module 143 includes: The wave impedance inversion submodule is used to perform seismic wave impedance inversion based on various detection data to obtain wave impedance values. The lithology distribution prediction submodule is used to input the wave impedance value into a preset lithology prediction model to obtain the lithology development probability volume corresponding to the target area; wherein, the preset lithology prediction model is used to characterize the functional relationship between wave impedance and lithology development probability.

[0106] Optionally, the wave impedance inversion submodule includes: The reflection unit is used to invert the reflection coefficient based on seismic data through maximum likelihood deconvolution to obtain the reflection coefficient. The recursive unit is used to invert the initial wave impedance model based on the reflection coefficient using a recursive algorithm; The objective optimization unit is used to obtain the wave impedance value by solving the objective optimization function based on the initial wave impedance model.

[0107] Optionally, the constraint module 144 includes: The lithology distribution model construction submodule is used to construct the lithology distribution model corresponding to the target area based on the lithology development probability volume and using the variation function. The multi-element fusion submodule is used to fuse multi-element information based on the lithology distribution model and the three-dimensional reservoir configuration model to obtain the three-dimensional reservoir lithofacies model corresponding to the target area.

[0108] Optionally, the device further includes: The effect verification module is used to acquire the post-test well data corresponding to the target area; based on the post-test well data, the three-dimensional geological model corresponding to the target area is verified from two parts: the horizontal section and the vertical section, and the verification results are obtained; based on the verification results, the three-dimensional geological model is optimized and adjusted.

[0109] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0110] Thirdly, based on the same inventive concept, embodiments of this application provide a readable storage medium on which a computer program is stored, which, when executed by a processor, implements the three-dimensional geological modeling method proposed in the first aspect of this application.

[0111] It should be noted that the specific implementation of the storage medium in this application embodiment refers to the specific implementation of the three-dimensional geological modeling method proposed in the first aspect of the above-mentioned application embodiment, and will not be repeated here.

[0112] Fourthly, based on the same inventive concept, referring to Figure 15 This application provides an electronic device 15, including a processor 151 and a memory 152; the memory 152 stores machine-executable instructions that can be executed by the processor 151, and the processor 151 is used to execute the machine-executable instructions to implement the three-dimensional geological modeling method proposed in the first aspect of this application.

[0113] It should be noted that the specific implementation of the electronic device 15 in this application embodiment refers to the specific implementation of the three-dimensional geological modeling method proposed in the first aspect of the above-mentioned application embodiment, and will not be repeated here.

[0114] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0115] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, or computer program products. Therefore, embodiments of this application can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of this application can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0116] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0117] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0118] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0119] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.

[0120] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device 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 terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0121] The above provides a detailed description of a three-dimensional geological modeling method, apparatus, medium, and equipment provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A three-dimensional geological modeling method, characterized in that, The method includes: Based on multiple detection data, a high-resolution isochronous sequence stratigraphic framework and sedimentary facies model corresponding to the target area were constructed. The sedimentary facies model is layered and embedded into the high-resolution isochronous sequence stratigraphic framework to obtain a three-dimensional reservoir configuration model; Based on the aforementioned multiple detection data and target seismic attributes, a lithological development probability body corresponding to the target area is established; the target seismic attributes refer to seismic attributes that are sensitive to lithology. The three-dimensional reservoir configuration model is constrained by the lithological development probability volume to establish a three-dimensional reservoir lithofacies model; An attribute model corresponding to the target area is constructed, and a three-dimensional geological model corresponding to the target area is obtained by combining the attribute model and the three-dimensional reservoir facies model.

2. The method according to claim 1, characterized in that, Based on multiple detection data, a high-resolution isochronous sequence stratigraphic framework corresponding to the target area was constructed, including: Sequence stratigraphy is performed on the target area based on well logging data, and structural trend constraints are applied based on seismic data to control the undulation of the strata between wells, thus obtaining an initial stratigraphic structural model. The initial stratigraphic model was fine-tuned using measured trajectory data from horizontal wells to obtain the target stratigraphic model. The target stratigraphic model is subjected to isochronous surface constraints to obtain a high-resolution isochronous sequence stratigraphic framework corresponding to the target region.

3. The method according to claim 1, characterized in that, The various detection data include single-well data, field outcrop data, and river channel seismic identification results; Based on various detection data, a sedimentary facies model corresponding to the target area is constructed, including: Sedimentary facies are divided based on single-well data to obtain single-well sand body identification results; Combining field outcrop data, river channel seismic identification results, and single-well sand body identification results, a river facies configuration model corresponding to the target area is established by layer. Sand body simulation of the fluvial facies configuration model is performed based on a multi-point geostatistical algorithm to obtain the sedimentary facies model corresponding to the target area.

4. The method according to claim 1, characterized in that, The target seismic attribute is wave impedance. Based on the various detection data and the target seismic attribute, a lithological development probability body corresponding to the target area is established, including: Seismic impedance inversion is performed based on the aforementioned multiple detection data to obtain the wave impedance value; The wave impedance value is input into a preset lithology prediction model to obtain the lithology development probability volume corresponding to the target area; wherein, the preset lithology prediction model is used to characterize the functional relationship between wave impedance and lithology development probability.

5. The method according to claim 4, characterized in that, Seismic impedance inversion is performed based on seismic data to obtain wave impedance values, including: Based on seismic data, the reflection coefficient is obtained by inverting the reflection coefficient through maximum likelihood deconvolution. Based on the reflection coefficient, the initial wave impedance model is obtained by inversion using a recursive algorithm; Based on the initial wave impedance model, the wave impedance value is obtained by solving the objective optimization function.

6. The method according to claim 1, characterized in that, The three-dimensional reservoir configuration model is constrained by the lithological development probability volume to establish a three-dimensional reservoir lithofacies model, including: Based on the lithological development probability volume, a lithological distribution model corresponding to the target area is constructed using the variogram function; Based on the lithological distribution model and the three-dimensional reservoir configuration model, multi-source information fusion is performed to obtain the three-dimensional reservoir lithofacies model corresponding to the target area.

7. The method according to claim 1, characterized in that, The method further includes: Obtain the post-test well data corresponding to the target area; Based on the post-well verification data, the three-dimensional geological model corresponding to the target area was verified from both the horizontal section and the vertical section, and the verification results were obtained. The three-dimensional geological model was optimized and adjusted based on the verification results.

8. A three-dimensional geological modeling device, characterized in that, The device includes: The module is used to construct a high-resolution isochronous sequence stratigraphic framework and sedimentary facies model for the target area based on various detection data. An embedding module is used to embed the sedimentary facies model layer by layer into the high-resolution isochronous sequence stratigraphic framework to obtain a three-dimensional reservoir configuration model. The lithology probability prediction module is used to establish a lithology development probability body corresponding to the target area based on the various detection data and target seismic attributes; the target seismic attributes refer to seismic attributes that are sensitive to lithology. The constraint module is used to constrain the three-dimensional reservoir configuration model using the lithological development probability volume, and to establish a three-dimensional reservoir lithofacies model. The module is used to construct the attribute model corresponding to the target area, and by combining the attribute model and the three-dimensional reservoir facies model, a three-dimensional geological model corresponding to the target area is obtained.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the three-dimensional geological modeling method as described in any one of claims 1 to 7.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the three-dimensional geological modeling method as described in any one of claims 1 to 7.

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