Three-dimensional geological modeling method, device and equipment based on sparse data and medium

Abstract

The application provides a sparse data-based three-dimensional geological modeling method, device, equipment and medium, relates to the technical field of three-dimensional geological modeling, and obtains single-well logging data of a single well in a target region and seismic data corresponding to the target region; a correlation model between logging parameters and seismic attributes is established based on the single-well logging data and the seismic data; based on the correlation model, seismic attribute inversion is performed on the seismic data to obtain regional logging data of a preset position in the target region; a three-dimensional structure model of the target region is constructed based on the single-well logging data and the regional logging data; and a reservoir attribute three-dimensional geological model of the target region is constructed based on the three-dimensional structure model, the single-well logging data, the regional logging data and the seismic attributes, so that the interwell prediction accuracy of the three-dimensional geological model is improved.

Description

Technical Field

[0001] This invention relates to the field of three-dimensional geological modeling technology, and more specifically, to a three-dimensional geological modeling method, apparatus, equipment, and medium based on sparse data. Background Technology

[0002] Three-dimensional geological modeling is one of the core technologies for reserve assessment and geological engineering design, and its accuracy directly affects the reliability of subsequent development decisions. In the early stages of exploration or in complex areas such as the deep sea, polar regions, and mountains, drilling costs are extremely high, and often only one or a very small number of wells can be obtained (i.e., sparse data conditions).

[0003] Existing 3D geological modeling methods under sparse data conditions have the following main technical defects: low accuracy of inter-well prediction. Traditional geostatistical modeling methods (such as Kriging interpolation and sequential Gaussian simulation) are highly dependent on the density and spatial distribution of well points. When the number of wells is very small (e.g., only one well), key statistical parameters such as the variogram cannot be effectively obtained, resulting in great uncertainty in the prediction results of the inter-well area. They often only provide homogenized or simple trend models that deviate from the geological reality, which cannot meet the needs of fine exploration. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a three-dimensional geological modeling method, apparatus, equipment and medium based on sparse data, so as to improve the inter-well prediction accuracy of three-dimensional geological models.

[0005] Firstly, this application provides a three-dimensional geological modeling method based on sparse data, including: Acquire single-well logging data of individual wells within the target area and corresponding seismic data for the target area; Based on single-well logging data and seismic data, a correlation model between logging parameters and seismic attributes is established. Based on the correlation model, seismic attributes are inverted from the seismic data to obtain regional logging data for preset locations within the target area. Based on single-well logging data and regional logging data, a three-dimensional structural model of the target area is constructed. Based on the three-dimensional structural model, single-well logging data, regional logging data, and seismic attributes, a three-dimensional geological model of reservoir attributes in the target area is constructed.

[0006] Optionally, based on single-well logging data and seismic data, a correlation model between logging parameters and seismic attributes is established, including: Sequence data is obtained by dividing the logging data of a single well into sequences. Based on seismic profiles of sequence stratigraphy and seismic data, a combined well-seismic sequence stratigraphy framework is established. A correlation model is derived based on the quantitative relationship between sequence stratigraphy framework and seismic attributes in seismic data.

[0007] Optionally, based on a correlation model, seismic attribute inversion is performed on the seismic data to obtain regional well logging data at preset locations within the target area, including: Based on the seismic attributes of the correlation model and seismic data, the seismic attributes are inverted using neural networks or multiple regression algorithms to obtain well logging characteristic data that characterizes reservoir features. The well logging characteristic data includes at least lithology, porosity, and permeability curves.

[0008] Optionally, based on single-well logging data and regional logging data, a three-dimensional structural model of the target area is constructed, including: A three-dimensional fault model is established based on fault seismic interpretation data of seismic data. The time-depth transformation of the stratigraphic data is performed to obtain the layer level in the depth domain; Based on the three-dimensional fault model, spatial interpolation is performed using the strata to obtain the three-dimensional structural model.

[0009] Optionally, based on the three-dimensional structural model, single-well logging data, regional logging data, and seismic attributes, a three-dimensional geological model of the reservoir attributes of the target area is constructed, including: A lithofacies model was established based on single-well logging data and regional logging data. Based on lithofacies models and seismic properties, it was determined that the spatial distribution of lithofacies is consistent with the seismic response characteristics. Based on the lithofacies model and the three-dimensional structural model, a three-dimensional geological model of reservoir properties is obtained.

[0010] Optionally, based on the lithofacies model and the three-dimensional structural model, a three-dimensional geological model of reservoir properties is obtained, including: Based on the lithofacies model and the three-dimensional structural model, a three-dimensional geological model of reservoir properties is obtained by using sequential Gaussian simulation or sequential indicator simulation algorithms and using the inversion data of seismic attributes as secondary variables in the co-simulation.

[0011] Optionally, after obtaining regional logging data at a preset location within the target area, the method further includes: using the regional logging data as virtual well data; obtaining a well point dataset based on the virtual well data and single-well logging data; and performing variation function analysis based on the well point dataset to obtain the spatial structural parameters of the reservoir parameters.

[0012] Secondly, this application provides a three-dimensional geological modeling device based on sparse data, comprising: The data acquisition module is used to acquire single-well logging data of a single well within the target area and the corresponding seismic data of the target area; The model building module is used to establish a correlation model between logging parameters and seismic attributes based on single-well logging data and seismic data; based on the correlation model, it performs seismic attribute inversion on the seismic data to obtain regional logging data for preset locations within the target area; based on single-well logging data and regional logging data, it constructs a three-dimensional structural model of the target area; and based on the three-dimensional structural model, single-well logging data, regional logging data, and seismic attributes, it constructs a three-dimensional geological model of reservoir attributes for the target area.

[0013] Thirdly, 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 executes the computer program to implement the three-dimensional geological modeling method based on sparse data as described above.

[0014] Fourthly, this application provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the three-dimensional geological modeling method based on sparse data as described above.

[0015] This invention provides a three-dimensional geological modeling method, apparatus, equipment, and medium based on sparse data. The method involves acquiring single-well logging data from a single well within a target area and corresponding seismic data for that area; establishing a correlation model between logging parameters and seismic attributes based on the single-well logging data and seismic data; performing seismic attribute inversion on the seismic data based on the correlation model to obtain regional logging data for a predetermined location within the target area; constructing a three-dimensional structural model of the target area based on the single-well logging data and regional logging data; and constructing a three-dimensional geological model of reservoir attributes for the target area based on the three-dimensional structural model, single-well logging data, regional logging data, and seismic attributes, thereby improving the inter-well prediction accuracy of the three-dimensional geological model.

[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 The flowchart illustrates a three-dimensional geological modeling method based on sparse data provided in an embodiment of the present invention. Figure 2This diagram illustrates the structure of a three-dimensional geological modeling method based on sparse data provided in an embodiment of the present invention. Figure 3 A schematic diagram of an electronic device structure provided by an embodiment of the present invention is shown. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0020] This application provides a three-dimensional geological modeling method based on sparse data. (See attached document.) Figure 1 As shown, the 3D geological modeling method based on sparse data provided in this application includes at least the following steps: Step 110: Obtain single-well logging data of a single well within the target area and the corresponding seismic data of the target area.

[0021] In this embodiment, the target area is typically an area in the early stages of exploration or with a very small number of wells, i.e., it meets the sparse data condition, for example, the number of known wells in the area is less than a preset threshold. Within this target area, the relevant data of an existing well is first acquired, i.e., single-well logging data. Single-well logging data consists of various geophysical parameters measured along the wellbore after drilling, including but not limited to natural gamma, resistivity, sonic transit time, and density, which can directly reflect the physical properties and lithological characteristics of the underground rock strata at the drilling point. Simultaneously, seismic data covering the entire target area is acquired, which can be obtained by artificially generating seismic waves and receiving the reflected waves. Seismic data can provide continuous information on the underground stratigraphic structure throughout the entire area, indirectly reflecting the response signals of stratigraphic interfaces and structural morphology.

[0022] In this embodiment, by acquiring high-precision single-well logging data at well locations and seismic data covering a large area without wells, the advantages of integrating single-well logging data and seismic data are combined, and well-constrained seismic inversion is performed. This lays the necessary data prerequisite for constructing a high-precision three-dimensional geological model under sparse well conditions. It solves the problem of having only a very small amount of direct rock physics information under sparse data conditions. By introducing seismic data to make up for the lack of spatial information, it provides a data foundation for using seismic attributes to infer and generate regional logging data.

[0023] Step 120: Based on single-well logging data and seismic data, establish a correlation model between logging parameters and seismic attributes; based on the correlation model, perform seismic attribute inversion on the seismic data to obtain regional logging data for preset locations within the target area; based on single-well logging data and regional logging data, construct a three-dimensional structural model of the target area; based on the three-dimensional structural model, single-well logging data, regional logging data, and seismic attributes, construct a three-dimensional geological model of reservoir attributes for the target area.

[0024] In this embodiment, sequence stratigraphy is performed on single-well logging data to obtain sequence data; a well-seismic combined sequence stratigraphic framework is established based on the sequence data and seismic profiles of seismic data; and a correlation model is obtained based on the quantitative relationship between the sequence stratigraphic framework and seismic attributes in the seismic data.

[0025] Specifically, based on single-well logging data and seismic data, a correlation model between logging parameters and seismic attributes is established, which includes the following steps: First, sequence logging of the acquired single-well data is performed to obtain sequence data. Sequence logging is based on the vertical variation of lithology and electrical properties in the formation, dividing and correlating the stratigraphic sequence revealed by drilling according to sedimentary cycles or sedimentary systems tracts, thereby identifying sequence boundaries at different levels and structural units within each sequence. Sequence data includes information such as the top and bottom depths, thickness, internal lithological assemblage, and logging response characteristics of each sequence unit. By performing sequence logging on single-well data, discrete or continuous logging curves can be transformed into geologically significant sequence units, providing a foundation for establishing an isochronous stratigraphic framework and avoiding time-crossing problems that may occur when directly correlating depth-domain data. Secondly, based on the seismic profiles in sequence stratigraphic data and seismic data, a well-seismic combined sequence stratigraphic framework is established. Specifically, the depths of each sequence interface and the lithological sequence within the sequence determined by single-well sequence division are compared and calibrated with the reflection characteristics of the in-phase axes identified on the seismic profile. By projecting the depth of the sequence interface at the single-well location onto the seismic profile, the location and reflection characteristics of each sequence interface on the seismic profile are determined using synthetic seismic records or direct correlation methods. Thus, under the constraint of well points, the in-phase seismic reflections of the entire region are interpreted as corresponding sequence interfaces, establishing an isochronous sequence stratigraphic framework covering the entire target area. The seismic profile reflects the reflection wave morphology of the subsurface stratigraphic interfaces, and the termination, overlap, and underlap of the in-phase axes correspond to the sequence interfaces. The framework is actually a three-dimensional spatial framework that defines the spatial distribution range, thickness variation trend, and contact relationship between different sequence units. In this embodiment of the application, by establishing a well-seismic combined sequence stratigraphic framework, the high-resolution sequence information of a single well is extended to the entire area covered by seismic data, realizing isochronous stratigraphic correlation from point to surface, providing a unified spatiotemporal stratigraphic framework for subsequent quantitative analysis of the relationship between seismic attributes and well logging parameters, and ensuring the geological rationality of correlation modeling. Finally, under the established sequence stratigraphic framework of well-seismic integration, seismic attributes corresponding to each sequence unit are extracted from the seismic data. The quantitative relationship between these seismic attributes and the obtained single-well logging parameters is analyzed. Through statistical regression, neural networks, or multivariate analysis, the mathematical relationship between logging parameters such as lithology, porosity, and permeability and various seismic attributes is quantitatively described, resulting in a correlation model. This correlation model reflects that, given a seismic attribute value at a certain location within the target area, the estimated logging parameter value at that location can be calculated. Seismic attributes are various parameters reflecting the characteristics of the subsurface medium, such as amplitude, frequency, phase, waveform, and coherence, obtained from seismic data through mathematical transformation or calculation. In this embodiment, by establishing a correlation model, the mathematical fusion of logging and seismic data is achieved, enabling the indirect acquisition of regional logging parameters for locations without drilling using comprehensive seismic data. This provides crucial seismic inversion basis for generating virtual well data and constructing refined three-dimensional geological models under sparse well conditions.

[0026] In this embodiment, a correlation model that can convert seismic attributes into well logging parameters is constructed by sequence stratigraphy, well-seismic combined sequence stratigraphic framework establishment, and quantitative relationship fitting between seismic attributes and well logging parameters. This effectively overcomes the difficulty of missing regional well logging information under sparse well data conditions, lays the technical foundation for obtaining regional well logging data by seismic attribute inversion, and improves the accuracy and reliability of inter-well prediction in the entire three-dimensional geological modeling process.

[0027] In this embodiment of the application, based on the correlation model and the seismic attributes of the seismic data, a neural network or multiple regression algorithm is used to perform seismic attribute inversion to obtain well logging characteristic data that characterizes reservoir features. The well logging characteristic data includes at least lithology, porosity and permeability curves.

[0028] Specifically, based on a correlation model, seismic attribute inversion is performed on the seismic data to obtain regional well logging data at preset locations within the target area. This process includes the following steps: First, acquire the established correlation model and the three-dimensional seismic data volume covering the entire target area; Then, under the constraints of the correlation model, a neural network algorithm or a multiple regression algorithm is used to invert seismic attributes on the entire seismic data volume. Specifically, the neural network algorithm constructs a multi-layer network structure containing input, hidden, and output layers, using multiple components of seismic attributes as input and corresponding well logging parameters as output. The neural network is trained using well logging data at known well points and seismic attribute samples, enabling it to learn the nonlinear mapping relationship between the two. The trained neural network is then applied to the seismic attributes of each seismic trace or grid cell across the entire region, thereby inverting and obtaining the estimated well logging parameters at each location. Furthermore, the multiple regression algorithm establishes a linear or polynomial regression equation between seismic attributes and well logging parameters, uses the least squares method to solve for the regression coefficients, and substitutes the seismic attribute values ​​into the regression equation to calculate the estimated well logging parameters. Seismic attribute inversion is performed using neural network algorithms or multiple regression algorithms, transforming the spatial distribution of seismic attributes into the spatial distribution of well logging parameters. This yields well logging characteristic data characterizing reservoir features. The well logging characteristic data is a three-dimensional spatially distributed attribute volume, and its data types include at least lithological data, porosity data, and permeability curve data. Lithological data represents the type of subsurface rock, such as sandstone or mudstone; porosity data represents the volume ratio of pore space in the rock; and permeability data represents the rock's ability to allow fluid passage. The well logging characteristic data includes not only actual measurements at known drilling locations but also predicted values ​​at other preset locations within the target area. Preset locations refer to locations within the target area that have not been actually drilled. The regional well logging data obtained through inversion can be used as virtual well data to participate in subsequent modeling processes. For example, multiple virtual well locations can be established within the target area according to a certain grid or key structural locations, and each virtual well location generates a complete lithological sequence, porosity curve, and permeability curve.

[0029] In this embodiment, by combining a correlation model with a neural network or multiple regression algorithm, the advantage of continuous lateral coverage of seismic data is fully utilized. Limited single-well logging information is extended to the entire target area, generating rich and spatially distributed regional logging data. This not only fills the gaps in logging information between wells under sparse drilling conditions, but also ensures that the attribute types directly correspond to key parameters for reservoir evaluation, providing reliable input data for subsequent construction of 3D structural models and reservoir attribute models. Simultaneously, the use of neural network algorithms effectively handles the complex nonlinear relationship between seismic attributes and logging parameters, improving the accuracy and reliability of the inversion results, and thus significantly enhancing the predictive capability of the 3D geological model between wells.

[0030] In this embodiment of the application, a three-dimensional fault model is established based on fault seismic interpretation data of seismic data; the sequence data is converted from time to depth to obtain the layer planes in the depth domain; based on the three-dimensional fault model, spatial interpolation is performed using the layer planes to obtain a three-dimensional structural model.

[0031] Specifically, based on single-well logging data and regional logging data, a three-dimensional structural model of the target area is constructed, including the following steps: First, a three-dimensional fault model is established based on fault seismic interpretation data from seismic data. After interpretation, the spatial distribution information of faults within the target area can be obtained, including geometric parameters such as fault strike, dip, dip angle, and displacement. Fault seismic interpretation data usually exists in the form of fault lines or fault planes. Using the fault interpretation data as input, the fault modeling function in 3D modeling software is used to construct a three-dimensional fault model describing the spatial morphology and intersecting relationships of faults. The three-dimensional fault model uses a set of three-dimensional surfaces to represent each fault, accurately depicting the location, extent, and intersecting relationships of faults in three-dimensional space. By establishing the three-dimensional fault model, the abstract fault geometry information obtained from seismic interpretation is transformed into a visualized three-dimensional geometric entity that can be used to constrain stratigraphic modeling. This lays the spatial framework foundation for the subsequent construction of stratigraphic planes under fault constraints, ensuring the spatial matching relationship between faults and strata in the structural model. Secondly, the previously obtained sequence data undergoes time-depth transformation to obtain the layer boundaries in the depth domain. Since sequence data is obtained in the time domain of seismic data—that is, sequence boundaries on seismic profiles are typically represented by two-way travel times—actual geological models need to be constructed in the depth domain to match drilling depth, rock properties, and other depth-domain data. Therefore, it is necessary to use velocity fields or time-depth transformation relationships to convert the time-domain sequence boundaries into layer boundaries in the depth domain. Specifically, the velocity field can be derived from the stacked velocity spectrum and the velocity model established from well-logging data during seismic processing, or from the time-depth relationship obtained through well-seismic calibration. The time value of each sequence boundary is multiplied by the corresponding velocity, or the spatial location of the sequence boundary in the depth domain is calculated using a time-depth transformation function. After time-depth transformation, a series of layer data in the depth domain are obtained, with each sequence boundary representing an isochronous sedimentary interface in the spatial surface of the depth domain. The time-depth conversion solved the problem of scale inconsistency between seismic time domain data and drilling depth domain data, enabling the structural model to accurately reflect the actual burial depth and thickness of underground strata, and ensuring the quantitative consistency between the model and the measured data. Finally, based on the established 3D fault model, spatial interpolation is performed using the depth-domain bedding planes obtained after time-depth transformation to obtain a 3D structural model. Specifically, the 3D fault model provides the geometric constraints of the fault planes, while the bedding planes provide control point data for the stratigraphic interfaces. Since drilling and seismic interpretation can only obtain a limited number of bedding plane control points, spatial interpolation algorithms are needed to infer the morphology of the entire stratigraphic interface. During the interpolation process, the fault model acts as a boundary condition or obstacle condition, causing the interpolated bedding planes to discontinuate or terminate at the fault, thus realistically reflecting the cutting and displacement effects of the fault on the strata. Commonly used interpolation algorithms include Kriging interpolation, inverse distance weighted interpolation, and discrete smooth interpolation. By fitting surfaces within each stratigraphic unit based on stratigraphic control points and under the constraints of a three-dimensional fault model, three-dimensional surfaces of the top and bottom surfaces of each stratum are generated. All stratigraphic surfaces are combined from top to bottom according to the stratigraphic sequence and embedded into the fault model to form a complete three-dimensional structural model. This model not only shows the spatial distribution, thickness variation, and undulation of the strata, but also accurately expresses the cutting relationship of the faults on the strata and the spatial contact relationship of the strata.

[0032] In this embodiment of the application, by constructing a three-dimensional structural model, the fracture system in the region is accurately characterized, providing a geometric basis for subsequent analysis of the impact of faults on fluid migration and storage. This allows lithological and physical property parameters to be assigned and simulated in the correct stratigraphic and fault space, thereby significantly improving the geological rationality and predictive reliability of the overall three-dimensional geological model.

[0033] In this embodiment, a lithofacies model is established based on single-well logging data and regional logging data; based on the lithofacies model and seismic attributes, the spatial distribution of lithofacies is determined to be consistent with the seismic response characteristics; based on the lithofacies model and the three-dimensional structural model, a three-dimensional geological model of reservoir attributes is obtained. Specifically, based on the lithofacies model and the three-dimensional structural model, a sequential Gaussian simulation or a sequential indicator simulation algorithm is used, and the inversion data of seismic attributes is used as a secondary variable in the collaborative simulation to obtain the three-dimensional geological model of reservoir attributes.

[0034] Specifically, based on the three-dimensional structural model, single-well logging data, regional logging data, and seismic attributes, a three-dimensional geological model of the reservoir attributes of the target area is constructed, which includes the following process: First, a lithofacies model is established based on single-well logging data and regional logging data. The single-well logging data and regional logging data are merged to form a lithofacies control point dataset consisting of sparsely distributed actual well points and densely distributed virtual well points within the target area. Then, using geostatistical methods or target-based simulation methods, lithofacies types are spatially assigned within the stratigraphic framework provided by the 3D structural model, thereby establishing a 3D lithofacies model that displays the distribution and patterns of different rock types, such as sandstone, mudstone, and siltstone facies, in 3D space. By establishing the lithofacies model, discrete well point lithofacies information is extended to the entire 3D space, revealing the spatial configuration of reservoirs and caprocks as well as the connectivity of sand bodies, providing important facies constraints for subsequent attribute modeling. Secondly, based on the established lithofacies model and seismic attributes in the seismic data, the consistency between the spatial distribution of lithofacies and seismic response characteristics is determined. Specifically, the lithofacies type at each spatial location in the lithofacies model is compared and analyzed with the seismic attribute values ​​at that location. Since seismic attributes respond to lithological changes—for example, different lithofacies typically correspond to different amplitudes, frequencies, or waveform characteristics—statistical or visualization methods can be used to verify whether the lithofacies model matches the spatial variation trend of seismic attributes. If inconsistencies are found, the lithofacies model is iteratively adjusted, for example, by modifying the lithofacies interpretation of virtual wells or adjusting simulation parameters, so that the spatial distribution of lithology reflected by the final lithofacies model and the geophysical response characteristics reflected by seismic attributes achieve consistency in both macroscopic trends and local details. By using high-resolution seismic information for quality control of the lithofacies model, the unreasonable lithofacies distribution that may result from relying solely on sparse well data is avoided, thus enhancing the geological realism of the model. Finally, based on the lithofacies model and the three-dimensional structural model, a three-dimensional geological model of reservoir properties is obtained. To reasonably assign seismic attributes in three-dimensional space, this embodiment employs a sequential Gaussian simulation algorithm or a sequential indicator simulation algorithm, using inverted seismic attribute data as secondary variables in the co-simulation. Sequential Gaussian simulation is suitable for simulating continuous variables such as porosity and permeability. This method simulates each grid cell sequentially along a random path, using the simulated neighboring point values ​​as conditional data and assuming the data follows a Gaussian distribution. Sequential indicator simulation is suitable for handling variables with clearly defined categories or threshold attributes, estimating the cumulative distribution function through indicator kriging. During the simulation, logging attribute data from actual well points and inverted attribute data from virtual well points are used as primary conditional data. Simultaneously, attribute volumes obtained from seismic attribute inversion, such as wave impedance inversion volumes, are used as secondary variables to participate in co-kriging or co-simulation calculations. These secondary variables guide the simulation results in the inter-well region along the trends indicated by the seismic attributes, thus ensuring that the final reservoir property model is both faithful to the measured well data and reflects the lateral predictive capability of seismic data. The algorithm described above is used to obtain a three-dimensional distribution model that includes properties such as porosity and permeability, which is a three-dimensional geological model of reservoir properties.

[0035] In this embodiment, firstly, the establishment of the lithofacies model and the consistency verification of seismic response ensure the geological rationality of the reservoir heterogeneity description, enabling the attribute model to have differentiated statistical characteristics in different lithofacies bodies. Secondly, the use of sequential Gaussian simulation or sequential indicator simulation algorithms can reasonably quantify the uncertainty of inter-well attributes and provide multiple equally probable realizations. Seismic attribute inversion data is used as a secondary variable in the collaborative simulation, effectively utilizing the advantage of continuous lateral coverage of seismic data, compensating for the deficiency of insufficient inter-well attribute prediction information under sparse well point conditions, and significantly improving the spatial prediction accuracy of reservoir attributes in the inter-well region. Finally, the obtained three-dimensional geological model of reservoir attributes provides a reliable three-dimensional quantitative basis for subsequent reserve calculation, fluid flow simulation, and engineering decision-making.

[0036] In this embodiment of the application, after obtaining regional logging data at a preset location within the target area, the method further includes: using the regional logging data as virtual well data; obtaining a well point dataset based on the virtual well data and single-well logging data; and performing variation function analysis based on the well point dataset to obtain the spatial structural parameters of the reservoir parameters.

[0037] Specifically, after obtaining regional logging data at a predetermined location within the target area, the step of using this regional logging data as virtual well data is as follows: Since the predetermined location represents a location within the target area where no actual well has been drilled, the regional logging data obtained through seismic attribute inversion characterizes the simulated logging response at that location. Therefore, it is considered that a virtual well exists at that predetermined location, and the logging data of the virtual well is the inverted regional logging data. This expands the originally sparse data condition, which only contains a single actual well, into a dataset containing multiple virtual wells. Then, based on virtual well data and single-well logging data, a wellpoint dataset is obtained. Specifically, the single-well logging data from actual drilling is merged with the virtual well data at various preset locations to form a wellpoint dataset with a more uniform spatial distribution and higher density. Each wellpoint in the wellpoint dataset contains the same logging parameter type, such as lithology, porosity, and permeability, and each wellpoint has clear spatial coordinates and formation depth information. By utilizing virtual well data, the number of sample points and spatial coverage for subsequent geostatistical analysis are effectively increased, thus overcoming the limitation that a single wellpoint cannot be effectively analyzed spatially under sparse data conditions. Finally, variogram analysis was performed based on the wellpoint dataset to obtain the spatial structural parameters of the reservoir. By calculating the relationship between the difference in the same reservoir parameter, such as porosity, between any two wellpoints in the wellpoint dataset and the distance and direction between the two wellpoints, a variogram model was fitted. The variogram model typically includes parameters such as nugget value, sill value, and range. Among them, the range reflects the effective distance of the spatial correlation of reservoir parameters, the nugget value reflects the random variation at the microscale, and the sill value reflects the overall variation amplitude. These parameters, including nugget value, sill value, and range, serve as spatial structural parameters, quantitatively describing the spatial distribution and anisotropy characteristics of reservoir parameters within the target region.

[0038] In this embodiment, by converting regional logging data into virtual well data and merging it with single-well logging data to construct a wellpoint dataset, the problem of insufficient data points leading to the inability to perform reliable variogram analysis under sparse drilling conditions is solved. Secondly, by performing variogram analysis based on the expanded wellpoint dataset, the obtained spatial structural parameters can more accurately reflect the actual spatial variability of reservoir parameters in the inter-well region. This provides reasonable and reliable spatial structural input parameters for subsequent construction of reservoir attribute models using geostatistical methods such as Kriging interpolation or sequential simulation, further improving the inter-well prediction accuracy of the three-dimensional geological model under sparse data conditions.

[0039] This application provides a three-dimensional geological modeling device based on sparse data, see below. Figure 2 As shown, the 3D geological modeling device based on sparse data provided in this application includes: The data acquisition module 210 is used to acquire single-well logging data of a single well within the target area and seismic data corresponding to the target area; The model building module 220 is used to establish a correlation model between logging parameters and seismic attributes based on single-well logging data and seismic data; based on the correlation model, it performs seismic attribute inversion on the seismic data to obtain regional logging data at a preset location within the target area; based on single-well logging data and regional logging data, it constructs a three-dimensional structural model of the target area; and based on the three-dimensional structural model, single-well logging data, regional logging data, and seismic attributes, it constructs a three-dimensional geological model of reservoir attributes for the target area.

[0040] It should be noted that the principle of the 3D geological modeling device based on sparse data provided in this application embodiment to solve the technical problem is similar to the 3D geological modeling method based on sparse data provided in this application embodiment. Therefore, the implementation of the 3D geological modeling device based on sparse data provided in this application embodiment can refer to the implementation of the 3D geological modeling method based on sparse data provided in this application embodiment, and the repeated parts will not be described again.

[0041] After introducing the 3D geological modeling method and apparatus based on sparse data provided in the embodiments of this application, the electronic equipment provided in the embodiments of this application will be briefly introduced next.

[0042] See Figure 3 As shown, the electronic device 500 provided in this application embodiment includes at least a processor 501, a memory 502, and a computer program stored in the memory 502 and executable on the processor 501. When the processor 501 executes the computer program, it implements the three-dimensional geological modeling method based on sparse data provided in this application embodiment.

[0043] The electronic device 500 provided in this application embodiment may further include a bus 503 connecting different components (including processor 501 and memory 502). The bus 503 represents one or more types of bus structures, including memory bus, peripheral bus, local area bus, etc.

[0044] Memory 502 may include a readable storage medium in the form of volatile memory, such as random access memory (RAM) 5021 and / or cache memory 5022, and may further include read-only memory (ROM) 5023. Memory 502 may also include a program tool 5025 having a set (at least one) of program modules 5024, including but not limited to an operating subsystem, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0045] Processor 501 can be a single processing element or a collective term for multiple processing elements. For example, processor 501 can be a central processing unit (CPU) or one or more integrated circuits configured to implement the sparse data-based three-dimensional geological modeling method provided in the embodiments of this application. Specifically, processor 501 can be a general-purpose processor, including but not limited to CPUs, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0046] Electronic device 500 can communicate with one or more external devices 504 (e.g., keyboard, remote control, etc.), and also with one or more devices that enable a user to interact with electronic device 500 (e.g., mobile phone, computer, etc.), and / or with devices that enable electronic device 500 to communicate with one or more other electronic devices 500 (e.g., router, modem, etc.). This communication can be performed through input / output (I / O) interface 505. Furthermore, electronic device 500 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) through network adapter 506. Figure 3 As shown, network adapter 506 communicates with other modules of electronic device 500 via bus 503. It should be understood that, although... Figure 3As not shown, other hardware and / or software modules may be used in conjunction with the electronic device 500, including but not limited to microcode, device drivers, redundant processors, external disk drive arrays, Redundant Arrays of Independent Disks (RAID) subsystems, tape drives, and data backup storage subsystems.

[0047] It should be noted that, Figure 3 The electronic device 500 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0048] The following describes the computer-readable storage medium provided in the embodiments of this application. The computer-readable storage medium provided in the embodiments of this application stores computer instructions, which, when executed by a processor, implement the 3D geological modeling method based on sparse data provided in the embodiments of this application. Specifically, the computer instructions can be built into or installed in the processor, so that the processor can implement the 3D geological modeling method based on sparse data provided in the embodiments of this application by executing the built-in or installed computer instructions.

[0049] In addition, the 3D geological modeling method based on sparse data provided in this application embodiment can also be implemented as a computer program product. The computer program product includes program code, which implements the 3D geological modeling method based on sparse data provided in this application embodiment when running on a processor.

[0050] The computer program product provided in this application embodiment may employ one or more computer-readable storage media, which may be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination thereof. Specifically, more specific examples (a non-exhaustive list) of computer-readable storage media include electrical connections with one or more wires, portable disks, hard disks, RAM, ROM, erasable programmable read-only memory (EPROM), optical fibers, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0051] The computer program product provided in this application embodiment can be a CD-ROM and include program code, and can also run on electronic devices such as computers. However, the computer program product provided in this application embodiment is not limited thereto. In this application embodiment, the computer-readable storage medium can be any tangible medium that contains or stores program code, which can be used by or in conjunction with an instruction execution system, device, or apparatus.

[0052] It should be noted that although several units or sub-units of the device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units.

[0053] Furthermore, although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0054] Although preferred embodiments of this 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 this application.

[0055] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. A three-dimensional geological modeling method based on sparse data, characterized in that, include: Acquire single-well logging data of a single well within the target area and the corresponding seismic data of the target area; Based on the single-well logging data and the seismic data, a correlation model between logging parameters and seismic attributes is established; based on the correlation model, seismic attributes are inverted on the seismic data to obtain regional logging data at a preset location within the target area; Based on the single-well logging data and the regional logging data, a three-dimensional structural model of the target area is constructed; based on the three-dimensional structural model, the single-well logging data, the regional logging data, and the seismic attributes, a three-dimensional geological model of the reservoir attributes of the target area is constructed.

2. The three-dimensional geological modeling method based on sparse data according to claim 1, characterized in that, Based on the single-well logging data and the seismic data, a correlation model between logging parameters and seismic attributes is established, including: Sequence logging data from the single well is performed to obtain sequence data. Based on the seismic profiles of the sequence data and the seismic data, a well-seismic combined sequence stratigraphic framework is established. The correlation model is obtained based on the quantitative relationship between the sequence stratigraphic framework and the seismic attributes in the seismic data.

3. The three-dimensional geological modeling method based on sparse data according to claim 1, characterized in that, Based on the aforementioned correlation model, seismic attribute inversion is performed on the seismic data to obtain regional well logging data at a preset location within the target area, including: Based on the correlation model and the seismic attributes of the seismic data, a neural network or multiple regression algorithm is used to perform seismic attribute inversion to obtain well logging characteristic data that characterizes reservoir features. The well logging characteristic data includes at least lithology, porosity, and permeability curves.

4. The three-dimensional geological modeling method based on sparse data according to claim 2, characterized in that, Based on the single-well logging data and the regional logging data, a three-dimensional structural model of the target area is constructed, including: Based on the fault seismic interpretation data of the aforementioned seismic data, a three-dimensional fault model is established; The layer sequence data is converted to depth over time to obtain the layer level in the depth domain; Based on the three-dimensional fault model, spatial interpolation is performed using the layer planes to obtain the three-dimensional structural model.

5. The three-dimensional geological modeling method based on sparse data according to claim 1, characterized in that, Based on the aforementioned three-dimensional structural model, the aforementioned single-well logging data, the aforementioned regional logging data, and the aforementioned seismic attributes, a three-dimensional geological model of the reservoir attributes of the target area is constructed, including: Based on the single-well logging data and the regional logging data, a lithofacies model is established; Based on the lithofacies model and the seismic properties, it is determined that the spatial distribution of lithofacies is consistent with the seismic response characteristics; Based on the lithofacies model and the three-dimensional structural model, the three-dimensional geological model of the reservoir properties is obtained.

6. The three-dimensional geological modeling method based on sparse data according to claim 5, characterized in that, Based on the lithofacies model and the three-dimensional structural model, the three-dimensional geological model of the reservoir properties is obtained, including: Based on the lithofacies model and the three-dimensional structural model, a three-dimensional geological model of the reservoir properties is obtained by using sequential Gaussian simulation or sequential indicator simulation algorithm, and by using the inversion data of the seismic attributes as secondary variables in the collaborative simulation.

7. The three-dimensional geological modeling method based on sparse data according to claim 1, characterized in that, After obtaining regional logging data at a preset location within the target area, the process further includes: The regional logging data is used as virtual well data; Based on the virtual well data and the single-well logging data, a well point dataset is obtained; Based on the well point dataset, variational function analysis was performed to obtain the spatial structural parameters of the reservoir.

8. A three-dimensional geological modeling device based on sparse data, characterized in that, include: The data acquisition module is used to acquire single-well logging data of a single well within the target area and seismic data corresponding to the target area; The model building module is used to establish a correlation model between logging parameters and seismic attributes based on the single-well logging data and the seismic data; and to perform seismic attribute inversion on the seismic data based on the correlation model to obtain regional logging data at a preset location within the target area. Based on the single-well logging data and the regional logging data, a three-dimensional structural model of the target area is constructed; based on the three-dimensional structural model, the single-well logging data, the regional logging data, and the seismic attributes, a three-dimensional geological model of the reservoir attributes of the target area is constructed.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the sparse data-based three-dimensional geological modeling method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the sparse data-based three-dimensional geological modeling method as described in any one of claims 1 to 7.

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