Gas reservoir three-dimensional geological modeling method and system and medium
By using multi-source data fusion and static-dynamic collaborative constraints, the problem of insufficient data integration in existing geological modeling was solved, and a high-precision, low-uncertainty three-dimensional gas reservoir geological model was realized, which improved the model's predictive ability and development benefits.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-01-08
- Publication Date
- 2026-03-27
AI Technical Summary
Existing geological modeling methods fail to effectively integrate multiple data sources, resulting in insufficient inter-well prediction capabilities, an inability to accurately reflect complex geological heterogeneity, low modeling accuracy, high uncertainty, and a lack of static and dynamic verification mechanisms.
By comprehensively utilizing seismic, well logging, vertical seismic profile (VSP), core, and dynamic production data, and through multi-source data fusion and static-dynamic collaborative constraints, a high-precision three-dimensional gas reservoir geological model is constructed, including steps such as depth correction, multi-attribute fusion, lithofacies modeling, and fluid flow information feedback.
It significantly improved modeling accuracy, reduced uncertainty, formed a quality control closed loop, ensured that the model conformed to static geological understanding and accurately reproduced historical production dynamics, and enhanced the model's predictive ability and development benefits.
Smart Images
Figure CN121741841A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of oil and gas field exploration and development technology, specifically to a three-dimensional geological modeling method, system, and medium for gas reservoirs. Background Technology
[0002] As oil and gas exploration and development targets become increasingly complex, the limitations of traditional reservoir geological modeling methods are becoming more and more apparent. Firstly, data utilization is isolated: the modeling process typically relies solely on well logging curve interpolation or a single seismic inversion body as constraints, failing to effectively integrate multi-scale and multi-type data such as seismic attributes, VSP (Volume Spectroscopy), core samples, and production dynamics. This results in insufficient inter-well predictive capability and an inability to accurately reflect complex geological heterogeneity. Secondly, uncertainty is high and subjectivity is strong: in the absence of effective inter-well constraints, reservoir attribute modeling heavily relies on the manual setting of geostatistical variograms, leading to multiple solutions. Furthermore, the modeling process requires significant human intervention for post-processing adjustments, resulting in low efficiency and poor model repeatability and objectivity. Thirdly, there is a disconnect between "static" and "dynamic" data: traditional modeling processes stop at establishing static geological models. The accuracy of the model's prediction of fluid flow and production dynamics lacks an inherent verification and constraint mechanism, casting doubt on the model's reliability in development decision-making.
[0003] Although existing technologies have attempted to use seismic attributes as trend constraints in modeling, how to quantitatively integrate multiple complementary seismic information to construct a more reliable trend field, and how to deeply integrate high-precision VSP data and production dynamic data into the modeling process to form closed-loop quality control, remain technical challenges that urgently need to be addressed.
[0004] In view of the above, this application is hereby submitted. Summary of the Invention
[0005] The technical problem this invention aims to solve is that existing geological modeling methods cannot deeply integrate VSP data and production dynamic data into the modeling process, resulting in low modeling accuracy and model uncertainty. This invention provides a method, system, and medium for three-dimensional geological modeling of gas reservoirs. It comprehensively utilizes seismic, well logging, vertical seismic profile (VSP), core, and dynamic production data, based on multi-source data fusion and static / dynamic synergistic constraints, to construct a high-precision, low-uncertainty three-dimensional gas reservoir geological model. This invention is particularly suitable for the detailed reservoir description, reserve assessment, and development scheme optimization of conventional and unconventional gas reservoirs with complex structures and strong heterogeneity, and is of great significance for improving the accuracy of gas reservoir description and development efficiency.
[0006] This invention is achieved through the following technical solution:
[0007] In a first aspect, the present invention provides a three-dimensional geological modeling method for gas reservoirs, the method comprising:
[0008] Acquire multi-source data for the target work area, including seismic data, vertical seismic profile data, well logging data, seismic interpretation horizon and fault data, core analysis data, and gas reservoir dynamic production data;
[0009] By combining vertical seismic profile data and virtual well data, depth correction is performed on the time-depth converted layers to obtain the corrected layers; based on the corrected layers and fault data, a three-dimensional structural framework model is established.
[0010] Based on the seismic attribute volume, the seismic inversion volume, and the determined optimal weight coefficients of the two, a spatial development probability trend volume of sand bodies is generated by fusion.
[0011] Using a three-dimensional structural lattice model as the spatial framework and the spatial development probability trend of sand bodies as the lateral soft constraint, multiple equally likely lithofacies models were initially realized. By calculating the degree of agreement between each realization and well point data and the spatial correlation with seismic trends, a weighted average was performed to obtain the three-dimensional lithofacies model.
[0012] Using a three-dimensional lithofacies model as a constraint, geostatistical simulations are performed on facies zones to generate a three-dimensional distribution model of reservoir physical parameters;
[0013] The three-dimensional lithofacies model and the three-dimensional distribution model are imported into the numerical simulator. Historical data of gas reservoir dynamic production are used for fitting, and the fitting differences are analyzed. Based on the fitting differences, fluid flow information is fed back to the three-dimensional lithofacies model and the three-dimensional distribution model for iterative modeling to obtain a three-dimensional geological model.
[0014] Furthermore, by combining vertical seismic profile data and virtual well data, depth correction is performed on the time-depth converted layers to obtain the corrected layers, including:
[0015] Based on the aforementioned vertical seismic profile data, a combined well-seismic velocity field is established;
[0016] Based on the combined well-seismic velocity field, the seismic interpretation horizons are converted to time-depth to obtain the time-depth converted horizons.
[0017] The depth of the time-depth converted layers is corrected using drilling stratification data from the well logging data, and virtual well data generated by seismic attributes is introduced to control microstructures, thus obtaining the corrected layers.
[0018] Furthermore, the introduction of virtual well data generated guided by seismic attributes specifically includes:
[0019] In areas lacking actual drilling control, pseudo-well points are virtually set along structural ridges or river centerlines indicated by seismic attributes, based on the relative relationships of seismic attributes. These pseudo-well points are then assigned formation depth and thickness information derived from adjacent actual drilling data and seismic attribute trends to enhance control over local microstructures and thin reservoirs.
[0020] Furthermore, based on the seismic attribute volume, the seismic inversion volume, and the determined optimal weighting coefficients of both, a spatial development probability trend volume of sand bodies is generated by fusing them, including:
[0021] Extract at least two seismic attribute volumes sensitive to sand bodies from the seismic data and obtain pre-stack or post-stack seismic inversion volumes;
[0022] The optimal weighting coefficients for each seismic attribute volume and the inverted volume are determined by using a multivariate regression algorithm or a co-kriging algorithm based on wellpoint lithofacies calibration.
[0023] Based on the earthquake attribute volume, earthquake inversion volume, and optimal weighting coefficients, a sand body spatial development probability trend volume is generated by fusing them together.
[0024] The multiple regression algorithm includes: uniformly selecting drilled well points within the work area; performing multiple linear regression analysis on the data values of each attribute volume and seismic inversion volume at the well points and the lithofacies or sandstone ratio interpreted from the wellbore; and solving for an optimal set of weight coefficients with the objective of maximizing the coefficient of determination of the regression model. ,in .
[0025] Furthermore, using a three-dimensional structural lattice model as the spatial framework and the spatial development probability trend of sand bodies as the lateral soft constraint, multiple equally likely lithofacies models were initially realized. By calculating the degree of agreement between each realization and well point data, as well as its spatial correlation with seismic trends, a weighted average was performed to obtain the three-dimensional lithofacies model, including:
[0026] The wellpoint lithofacies interpretation data in the core analysis data are coarsened vertically at a meter scale to obtain coarsened wellpoint lithofacies interpretation data;
[0027] Using a three-dimensional structural lattice model as the spatial framework and the spatial development probability trend of sand bodies as the lateral soft constraint, a sequential indicator simulation algorithm is adopted. The trend volume is used to drive the spatial variation of the principal axis direction and range of the variation function to establish multiple equally likely lithofacies models for initial realization.
[0028] By calculating the degree of agreement between each implementation and the coarsened wellpoint lithofacies interpretation data, as well as the spatial correlation with seismic trends, a weighted average is performed to obtain a three-dimensional lithofacies model.
[0029] Furthermore, the use of trend volume to drive the spatial variation of the principal axis direction and range of the variation function refers to taking the gradient direction of the sand body spatial development probability trend volume as the principal axis direction of the geostatistical variation function, and relating the spatial variation rate of the sand body spatial development probability trend volume to the geologically understood phase transition distance, in order to dynamically adjust the range parameters of the variation function at various locations in three-dimensional space.
[0030] Furthermore, using a three-dimensional lithofacies model as a constraint, geostatistical simulations are performed on the facies zones to generate a three-dimensional distribution model of reservoir physical parameters, including:
[0031] The wellpoint porosity, permeability, and gas saturation data in the core analysis data are coarsened to obtain coarsened data;
[0032] Based on the coarsened data, and constrained by a three-dimensional lithofacies model, geostatistical simulations are performed on the facies zones to generate a three-dimensional distribution model of reservoir physical parameters.
[0033] Furthermore, historical data from gas reservoir dynamic production are used for fitting, and the fitting differences are analyzed. Based on these differences, fluid flow information is fed back to the three-dimensional lithofacies model and the three-dimensional distribution model for iterative modeling, resulting in a three-dimensional geological model, including:
[0034] Historical data were fitted using dynamic production data from the gas reservoir to obtain fitted values;
[0035] Subtracting the fitted value from the measured value yields the fitting difference;
[0036] If the fitting difference exceeds a set threshold, fluid flow information (such as pressure connectivity and dominant seepage direction) is fed back to the three-dimensional lithofacies model and the three-dimensional distribution model. The modeling parameters or trend constraints are adjusted, and iterative modeling is performed until a three-dimensional geological model that matches the static geological features and dynamic production response is obtained.
[0037] Furthermore, fluid flow information is fed back to the three-dimensional lithofacies model and the three-dimensional distribution model, including: correcting the understanding of reservoir connectivity based on the inter-well interference presented in the production history fitting, and then adjusting the interlayer distribution in the three-dimensional lithofacies model or the spatial continuity model of the permeability field in the three-dimensional geological model.
[0038] Secondly, the present invention provides a three-dimensional geological modeling system for gas reservoirs, the system comprising:
[0039] The acquisition unit is used to acquire multi-source data of the target work area, including seismic data, vertical seismic profile data, well logging data, seismic interpretation horizon and fault data, core analysis data and gas reservoir dynamic production data;
[0040] A structural modeling unit is constructed to combine vertical seismic profile data and virtual well data to perform depth correction on the time-depth converted layers, thereby obtaining the corrected layers; based on the corrected layers and fault data, a three-dimensional structural framework model is established.
[0041] The trend volume generation unit is used to generate a sand body spatial development probability trend volume by fusing the seismic attribute volume, the seismic inversion volume, and the determined optimal weight coefficients of the two.
[0042] The three-dimensional lithofacies model establishment unit is used to establish multiple equally likely lithofacies models as initial realizations, with the three-dimensional structural lattice model as the spatial framework and the spatial development probability trend of sand bodies as the lateral soft constraint. By calculating the degree of agreement between each realization and the well point data and the spatial correlation with the seismic trend, a weighted average is performed to obtain the three-dimensional lithofacies model.
[0043] The three-dimensional distribution model generation unit is used to perform geostatistical simulation of facies zones based on a three-dimensional lithofacies model as a constraint, and generate a three-dimensional distribution model of reservoir physical parameters.
[0044] The static-dynamic coupling analysis and feedback optimization unit is used to import the three-dimensional lithofacies model and the three-dimensional distribution model into the numerical simulator, perform historical fitting using dynamic production data of the gas reservoir, and analyze the fitting differences; and based on the fitting differences, feed back fluid flow information to the three-dimensional lithofacies model and the three-dimensional distribution model for iterative modeling to obtain a three-dimensional geological model.
[0045] Thirdly, the present invention provides a computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the above-described method for three-dimensional geological modeling of a gas reservoir.
[0046] Compared with the prior art, the present invention has the following advantages and beneficial effects:
[0047] This invention discloses a method, system, and medium for three-dimensional geological modeling of gas reservoirs. It comprehensively utilizes seismic, well logging, vertical seismic profile (VSP), core, and dynamic production data, based on multi-source data fusion and static / dynamic synergistic constraints, to construct a high-precision, low-uncertainty three-dimensional gas reservoir geological model. This invention is particularly suitable for the detailed reservoir description, reserve assessment, and development scheme optimization of conventional and unconventional gas reservoirs with complex structures and strong heterogeneity, and is of great significance for improving the accuracy of gas reservoir description and development efficiency.
[0048] 1. Significantly improved modeling accuracy: Through the construction modeling constrained by both VSP data and hierarchical data, as well as the multi-attribute fusion trend constraint based on well calibration and quantifiable weights, the model's structural morphology and sand body distribution prediction accuracy are significantly improved compared to traditional methods.
[0049] 2. Significantly reduced model uncertainty: This invention uses the seismic trend volume to dynamically drive the variogram function, making the stochastic simulation process more consistent with geological laws. At the same time, it adopts a weighted fusion output of multiple implementations, which reduces the random noise of the final model and narrows the uncertainty range of key reservoir boundaries.
[0050] 3. A closed-loop quality control system has been established, enhancing the model's predictive capabilities: The unique static-dynamic collaborative verification and feedback mechanism ensures that the constructed geological model not only conforms to static geological understanding but also accurately reproduces historical production dynamics. This makes the model highly reliable for residual gas prediction and development scheme optimization, effectively improving the success rate of well placement guidance in practical applications.
[0051] 4. High level of automation and intelligence: The system of this invention integrates the entire process from data to model and then to dynamic verification, reducing the links of manual intervention and subjectivity, and improving the efficiency and standardization of modeling work. Attached Figure Description
[0052] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:
[0053] Figure 1 This is a flowchart of a three-dimensional geological modeling method for gas reservoirs according to the present invention;
[0054] Figure 2 This is a detailed flowchart of a three-dimensional geological modeling method for gas reservoirs according to the present invention;
[0055] Figure 3 This is a schematic diagram of the multi-attribute fusion and weight determination method of the present invention;
[0056] Figure 4 This is a schematic diagram illustrating the dynamic collaborative verification and feedback optimization of the present invention;
[0057] Figure 5 This is a comparison diagram between the method of this invention and traditional modeling methods;
[0058] Figure 6 This is a structural block diagram of a three-dimensional geological modeling system for gas reservoirs according to the present invention. Detailed Implementation
[0059] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of this invention are only for explaining this invention and are not intended to limit this invention.
[0060] This invention comprehensively utilizes seismic, well logging, vertical seismic profile (VSP), core, and dynamic production data to construct a high-precision, low-uncertainty three-dimensional gas reservoir geological model through multi-source data fusion and static-dynamic synergistic constraints. This invention is particularly suitable for fine reservoir description, reserve assessment, and development scheme optimization of conventional and unconventional gas reservoirs with complex structures and strong heterogeneity.
[0061] Specifically, (1) a multi-attribute quantitative fusion and weight optimization method is proposed to construct a lateral trend constraint body that can more accurately reflect the spatial distribution law of sand bodies and reduce the uncertainty of lithofacies and attribute modeling.
[0062] (2) Innovative integration of VSP velocity and drilling stratification data to achieve accurate calibration and microstructure characterization of depth domain structural model.
[0063] (3) Establish a closed-loop workflow of "static modeling-dynamic verification-model update" and feed back the dynamic production data as a constraint to the static model optimization to ensure that the final three-dimensional geological model has both geological rationality and dynamic prediction capability.
[0064] (4) Provide a system for implementing the above method to improve the automation, standardization and reliability of the modeling process.
[0065] Example 1
[0066] like Figure 1 and Figure 2 As shown, the present invention provides a three-dimensional geological modeling method for gas reservoirs, the method comprising:
[0067] Step 1: Obtain multi-source data for the target work area, including seismic data, vertical seismic profile data, well logging data, seismic interpretation horizon and fault data, core analysis data, and gas reservoir dynamic production data;
[0068] Step 2: Combine vertical seismic profile data and virtual well data to perform depth correction on the time-depth converted strata to obtain the corrected strata; based on the corrected strata and fault data, establish a three-dimensional structural framework model.
[0069] In this embodiment, depth correction is performed on the time-depth converted layers by combining vertical seismic profile data and virtual well data to obtain the corrected layers, including:
[0070] Based on the aforementioned vertical seismic profile data (VSP data), a combined well-seismic velocity field is established;
[0071] Based on the combined well-seismic velocity field, the seismic interpretation horizons are converted to time-depth to obtain the time-depth converted horizons.
[0072] The depth of the time-depth converted layers is corrected using drilling stratification data from the well logging data, and virtual well data generated by seismic attributes is introduced to control microstructures, thus obtaining the corrected layers.
[0073] Specifically, virtual well data generated by seismic attributes is introduced. In areas lacking actual drilling control, pseudo-well points are virtually set up along structural ridges or river centerlines indicated by seismic attributes, based on the relative relationships of seismic attributes. These pseudo-well points are then assigned formation depth and thickness information derived from adjacent actual drilling data and seismic attribute trends to enhance control over local microstructures and thin reservoirs.
[0074] Step 3: Based on the seismic attribute volume, the seismic inversion volume, and the determined optimal weight coefficients of the two, a spatial development probability trend volume of sand bodies is generated by fusion.
[0075] In this embodiment, as Figure 3 As shown, Figure 3 This is a schematic diagram illustrating the multi-attribute fusion and weight determination method. Step 3 specifically includes:
[0076] Extract at least two seismic attribute volumes sensitive to sand bodies (e.g., seismic attribute volume A and seismic attribute volume B) from the seismic data, and obtain pre-stack or post-stack seismic inversion volumes (e.g., seismic inversion volume C).
[0077] The optimal weighting coefficients for each seismic attribute volume and the inverted volume are determined by using a multivariate regression algorithm or a co-kriging algorithm based on wellpoint lithofacies calibration.
[0078] Based on the earthquake attribute volume, earthquake inversion volume, and optimal weighting coefficients, a comprehensive sand body spatial development probability trend volume is generated by fusion.
[0079] The multiple regression algorithm includes: uniformly selecting drilled well points within the work area; performing multiple linear regression analysis on the data values of each attribute volume and seismic inversion volume at the well points and the lithofacies or sandstone ratio interpreted from the wellbore; and solving for an optimal set of weight coefficients with the objective of maximizing the coefficient of determination of the regression model. ,in .
[0080] Step 4: Using the three-dimensional structural lattice model as the spatial framework and the spatial development probability trend of sand bodies as the lateral soft constraint, establish multiple equally likely lithofacies models as initial realizations; by calculating the degree of agreement between each realization and well point data and the spatial correlation with seismic trends, a weighted average is performed to obtain the three-dimensional lithofacies model.
[0081] In this embodiment, step 4 specifically includes:
[0082] The wellpoint lithofacies interpretation data in the core analysis data are coarsened vertically at a meter scale to obtain coarsened wellpoint lithofacies interpretation data;
[0083] Using a three-dimensional structural lattice model as the spatial framework and the spatial development probability trend of sand bodies as the lateral soft constraint, a sequential indicator simulation algorithm is adopted. The trend volume is used to drive the spatial variation of the principal axis direction and range of the variation function to establish multiple equally likely lithofacies models for initial realization.
[0084] By calculating the degree of agreement between each implementation and the coarsened wellpoint lithofacies interpretation data, as well as the spatial correlation with seismic trends, a weighted average is performed to obtain a three-dimensional lithofacies model.
[0085] Specifically, using the trend body to drive the spatial variation of the principal axis direction and range of the variation function means taking the gradient direction of the sand body spatial development probability trend body as the principal axis direction of the geostatistical variation function, and relating the spatial variation rate of the sand body spatial development probability trend body to the geologically understood phase transition distance, in order to dynamically adjust the range parameters of the variation function at various locations in three-dimensional space.
[0086] Step 5: Using the three-dimensional lithofacies model as a constraint, perform geostatistical simulation of the facies zones to generate a three-dimensional distribution model of reservoir physical parameters;
[0087] In this embodiment, step 5 specifically includes:
[0088] The wellpoint porosity, permeability, and gas saturation data in the core analysis data are coarsened to obtain coarsened data;
[0089] Based on the coarsened data, and constrained by a three-dimensional lithofacies model, geostatistical simulations are performed on the facies zones to generate a three-dimensional distribution model of reservoir physical parameters.
[0090] Step 6: Import the three-dimensional lithofacies model and the three-dimensional distribution model into the numerical simulator, use the dynamic production data of the gas reservoir to perform historical fitting, and analyze the fitting differences; and based on the fitting differences, feed the fluid flow information back to the three-dimensional lithofacies model and the three-dimensional distribution model for iterative modeling to obtain the three-dimensional geological model.
[0091] like Figure 4 As shown, Figure 4 This is a flowchart illustrating the dynamic collaborative verification and feedback optimization process in step 4.
[0092] In this embodiment, historical data of gas reservoir dynamic production is used for fitting, and the fitting differences are analyzed. Based on the fitting differences, fluid flow information is fed back to the three-dimensional lithofacies model and the three-dimensional distribution model for iterative modeling to obtain a three-dimensional geological model, including:
[0093] Historical data were fitted using dynamic production data from the gas reservoir to obtain fitted values;
[0094] Subtracting the fitted value from the measured value yields the fitting difference;
[0095] If the fitting difference exceeds a set threshold, the fluid flow information revealed by the dynamic data (such as pressure connectivity and dominant seepage direction) is fed back to the three-dimensional lithofacies model in step 4 and the three-dimensional distribution model in step 5. The modeling parameters or trend constraints are adjusted, and iterative modeling is performed until a high-precision three-dimensional geological model that matches the static geological features and dynamic production response is obtained.
[0096] Specifically, fluid flow information is fed back to the three-dimensional lithofacies model and the three-dimensional distribution model, including: correcting the understanding of reservoir connectivity based on the inter-well interference presented in the production history fitting, and then adjusting the interlayer distribution in the three-dimensional lithofacies model or the spatial continuity model of the permeability field in the three-dimensional geological model.
[0097] In specific implementation, the method of the present invention is carried out according to steps 1 to 6 above, as follows:
[0098] (1) Multi-source data collection: Collect multi-source data of the target work area. Multi-source data includes at least seismic data, attribute data, inversion data, vertical seismic profile (VSP) data, lithofacies data, porosity and permeability data, stratigraphic data, strata data, fault data, gas reservoir dynamic production data, etc.
[0099] (2) High-precision depth domain structural modeling: Using the velocity information of vertical seismic profile (VSP) data, a high-precision well-seismic joint velocity field for the work area is established after correcting the time-depth relationship. The seismically interpreted strata are converted to the depth domain, and depth correction is performed using actual drilling stratification data. At the same time, virtual wells are generated in the well-free areas of the work area using seismic attributes to obtain virtual well data, so as to constrain the micro-structural morphology, and finally a three-dimensional structural framework model is established.
[0100] (3) Construction of multi-attribute fusion trend body: extract the root mean square amplitude and heterogeneous attribute body of the earthquake, and combine it with the P-wave and S-wave velocity ratio body obtained by simultaneous inversion before stacking. Perform multivariate regression of the values of the three data bodies with the sand-soil ratio above ground to obtain the optimal weight, and fuse them to generate the sand body development probability trend body.
[0101] (4) Well-seismic co-modeling of lithofacies: The lithofacies model is established by sequential indicator simulation with the sand body development probability trend volume as a constraint. The principal axis direction of the variation function is set as the gradient direction of the trend volume. The 10 lithofacies models with the highest consistency with the well point are selected and weighted averaged to obtain the final three-dimensional lithofacies model.
[0102] (5) Phase-controlled property modeling: Based on this, the porosity and permeability fields of sandstone and mudstone facies zones are simulated. With the three-dimensional lithofacies model as a constraint, geostatistical simulation is performed on the facies zones to generate a three-dimensional distribution model of reservoir physical parameters.
[0103] (6) Static and dynamic data collaborative verification and model optimization: The three-dimensional lithofacies model and the three-dimensional distribution model are numerically simulated to fit the production history of the actual wells. The connectivity of sandstone and mudstone in the model is analyzed by the fitting effect of production or pressure. This dynamic information is fed back to adjust the corresponding simulation parameters in lithofacies modeling. After remodeling, a second history fitting is performed to improve the fitting accuracy of the whole area. The final model can be used to guide well location deployment and improve reservoir prediction accuracy.
[0104] (7) Combine dynamic production data to conduct comprehensive evaluation of gas reservoirs, combine production dynamic data (gas production, pressure, water production, etc.) to conduct comprehensive analysis of the target layer, and predict the distribution of remaining gas based on the production decline relationship of single wells.
[0105] This invention is based on traditional modeling methods ( Figure 5 (upper part) and the method of the present invention ( Figure 5 (Second half) Comparison of lithofacies model profiles established in the same work area shows that, in terms of sand body boundary clarity and continuity, traditional modeling methods are limited by single seismic constraints and artificial variogram settings, resulting in relatively blurred sand body boundaries and significant "noise" and false connectivity in the inter-well sand body continuity prediction. In contrast, the method of this invention integrates multi-attribute trend volumes and dynamically drives the variogram, making the spatial distribution of sand bodies more consistent with geological laws, with clear boundaries and more continuous and natural channel and sandbar morphologies. Regarding microstructure and thin reservoir characterization, traditional modeling methods have weak control over microstructures and thin reservoirs in well-free areas, exhibiting a smoothing trend in the model. The method of this invention, by introducing VSP correction and virtual well data, significantly improves the ability to identify microstructures, clearly presenting thin sand layers on the profile, and more closely reflecting actual drilling conditions. In terms of heterogeneity characterization, traditional modeling methods show a relatively uniform lithofacies distribution in the simulation results, with insufficient representation of heterogeneity. The method of this invention, through trend volume constraints and facies-controlled modeling, makes the spatial configuration of sandstone and mudstone more complex and reasonable, and the distribution of interlayers within the reservoir more consistent with dynamic response characteristics. In summary, the model built by the method of this invention is superior to traditional modeling methods in terms of sand body prediction accuracy, geological structure rationality, and heterogeneity characterization. It is especially suitable for the fine description and development decision support of complex heterogeneous gas reservoirs.
[0106] Example 2
[0107] like Figure 6 As shown, the difference between this embodiment and Embodiment 1 is that this embodiment provides a three-dimensional geological modeling system for gas reservoirs, which corresponds one-to-one with the three-dimensional geological modeling method for gas reservoirs in Embodiment 1; the system includes:
[0108] The acquisition unit is used to acquire multi-source data of the target work area, including seismic data, vertical seismic profile data, well logging data, seismic interpretation horizon and fault data, core analysis data and gas reservoir dynamic production data;
[0109] A structural modeling unit is constructed to combine vertical seismic profile data and virtual well data to perform depth correction on the time-depth converted layers, thereby obtaining the corrected layers; based on the corrected layers and fault data, a three-dimensional structural framework model is established.
[0110] The trend volume generation unit is used to generate a sand body spatial development probability trend volume by fusing the seismic attribute volume, the seismic inversion volume, and the determined optimal weight coefficients of the two.
[0111] The three-dimensional lithofacies model establishment unit is used to establish multiple equally likely lithofacies models as initial realizations, with the three-dimensional structural lattice model as the spatial framework and the spatial development probability trend of sand bodies as the lateral soft constraint. By calculating the degree of agreement between each realization and the well point data and the spatial correlation with the seismic trend, a weighted average is performed to obtain the three-dimensional lithofacies model.
[0112] The three-dimensional distribution model generation unit is used to perform geostatistical simulation of facies zones based on a three-dimensional lithofacies model as a constraint, and generate a three-dimensional distribution model of reservoir physical parameters.
[0113] The static-dynamic coupling analysis and feedback optimization unit is used to import the three-dimensional lithofacies model and the three-dimensional distribution model into the numerical simulator, perform historical fitting using dynamic production data of the gas reservoir, and analyze the fitting differences; and based on the fitting differences, feed back fluid flow information to the three-dimensional lithofacies model and the three-dimensional distribution model for iterative modeling to obtain a three-dimensional geological model.
[0114] The execution process of each unit can be carried out according to the steps of the gas reservoir three-dimensional geological modeling method in Example 1, and will not be described in detail in this example.
[0115] Meanwhile, the present invention also provides a computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the above-mentioned method for three-dimensional geological modeling of gas reservoirs.
[0116] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied 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.
[0117] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will 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 apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0118] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function 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.
[0119] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable 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.
[0120] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A method for three-dimensional geological modeling of gas reservoirs, characterized in that, The method includes: Acquire multi-source data for the target work area, including seismic data, vertical seismic profile data, well logging data, seismic interpretation horizon and fault data, core analysis data, and gas reservoir dynamic production data; By combining vertical seismic profile data and virtual well data, depth correction is performed on the time-depth converted layers to obtain the corrected layers; based on the corrected layers and fault data, a three-dimensional structural framework model is established. Based on the seismic attribute volume, the seismic inversion volume, and the determined optimal weight coefficients of the two, a spatial development probability trend volume of sand bodies is generated by fusion. Using a three-dimensional structural lattice model as the spatial framework and the spatial development probability trend of sand bodies as the lateral soft constraint, multiple equally likely lithofacies models were initially realized. By calculating the degree of agreement between each realization and well point data and the spatial correlation with seismic trends, a weighted average was performed to obtain the three-dimensional lithofacies model. Using a three-dimensional lithofacies model as a constraint, geostatistical simulations are performed on the facies zones to generate a three-dimensional distribution model of reservoir physical parameters; The three-dimensional lithofacies model and the three-dimensional distribution model are imported into the numerical simulator. Historical data of gas reservoir dynamic production are used for fitting, and the fitting differences are analyzed. Based on the fitting differences, fluid flow information is fed back to the three-dimensional lithofacies model and the three-dimensional distribution model for iterative modeling to obtain a three-dimensional geological model.
2. The method for three-dimensional geological modeling of gas reservoirs according to claim 1, characterized in that, By combining vertical seismic profile data and virtual well data, depth correction is performed on the time-depth converted layers to obtain the corrected layers, including: Based on the aforementioned vertical seismic profile data, a combined well-seismic velocity field is established; Based on the combined well-seismic velocity field, the seismic interpretation horizons are converted to time-depth to obtain the time-depth converted horizons. The depth of the time-depth converted layers is corrected using drilling stratification data from the well logging data, and virtual well data generated by seismic attributes is introduced to control microstructures, thus obtaining the corrected layers.
3. The method for three-dimensional geological modeling of gas reservoirs according to claim 2, characterized in that, The introduction of virtual well data generated guided by seismic attributes specifically includes: In areas lacking actual drilling control, pseudo-well points are virtually set along structural ridges or river centerlines indicated by seismic attributes, based on the relative relationships of seismic attributes. These pseudo-well points are then assigned formation depth and thickness information derived from adjacent actual drilling data and seismic attribute trends to enhance control over local microstructures and thin reservoirs.
4. The method for three-dimensional geological modeling of gas reservoirs according to claim 1, characterized in that, Based on the seismic attribute volume, the seismic inversion volume, and the determined optimal weighting coefficients of both, a spatial development probability trend volume of sand bodies is generated by fusing them, including: Extract at least two seismic attribute volumes sensitive to sand bodies from the seismic data and obtain pre-stack or post-stack seismic inversion volumes; The optimal weighting coefficients for each seismic attribute volume and the inverted volume are determined by using a multivariate regression algorithm or a co-kriging algorithm based on wellpoint lithofacies calibration. Based on the earthquake attribute volume, earthquake inversion volume, and optimal weighting coefficients, a sand body spatial development probability trend volume is generated by fusing them together. The multiple regression algorithm includes: uniformly selecting drilled well points within the work area; performing multiple linear regression analysis on the data values of each attribute volume and seismic inversion volume at the well points and the lithofacies or sandstone ratio interpreted from the wellbore; and solving for an optimal set of weight coefficients with the objective of maximizing the coefficient of determination of the regression model. ,in .
5. The method for three-dimensional geological modeling of gas reservoirs according to claim 1, characterized in that, Using a three-dimensional structural lattice model as the spatial framework and the spatial development probability trend of sand bodies as the lateral soft constraint, multiple equally likely lithofacies models were initially established. By calculating the degree of agreement between each model and well point data, as well as its spatial correlation with seismic trends, a weighted average was performed to obtain the three-dimensional lithofacies model, including: The wellpoint lithofacies interpretation data in the core analysis data are coarsened vertically at a meter scale to obtain coarsened wellpoint lithofacies interpretation data; Using a three-dimensional structural lattice model as the spatial framework and the spatial development probability trend of sand bodies as the lateral soft constraint, a sequential indicator simulation algorithm is adopted. The trend volume is used to drive the spatial variation of the principal axis direction and range of the variation function to establish multiple equally likely lithofacies models for initial realization. By calculating the degree of agreement between each implementation and the coarsened wellpoint lithofacies interpretation data, as well as the spatial correlation with seismic trends, a weighted average is performed to obtain a three-dimensional lithofacies model.
6. The method for three-dimensional geological modeling of gas reservoirs according to claim 5, characterized in that, The aforementioned use of trend volume to drive the spatial variation of the principal axis direction and range of the variation function refers to taking the gradient direction of the sand body spatial development probability trend volume as the principal axis direction of the geostatistical variation function, and correlating the spatial variation rate of the sand body spatial development probability trend volume with the geologically understood phase transition distance, in order to dynamically adjust the range parameters of the variation function at various locations in three-dimensional space.
7. The method for three-dimensional geological modeling of gas reservoirs according to claim 1, characterized in that, Using a three-dimensional lithofacies model as a constraint, geostatistical simulations are performed on facies zones to generate a three-dimensional distribution model of reservoir physical parameters, including: The wellpoint porosity, permeability, and gas saturation data in the core analysis data are coarsened to obtain coarsened data; Based on the coarsened data, and constrained by a three-dimensional lithofacies model, geostatistical simulations are performed on the facies zones to generate a three-dimensional distribution model of reservoir physical parameters.
8. The method for three-dimensional geological modeling of gas reservoirs according to claim 1, characterized in that, Historical data were fitted using dynamic production data from gas reservoirs, and the differences in the fitting were analyzed. Based on the fitting differences, the fluid flow information is fed back to the 3D lithofacies model and the 3D distribution model for iterative modeling, resulting in a 3D geological model, including: Historical data were fitted using dynamic production data from the gas reservoir to obtain fitted values; Subtracting the fitted value from the measured value yields the fitting difference; If the fitting difference exceeds a set threshold, the fluid flow information is fed back to the three-dimensional lithofacies model and the three-dimensional distribution model. The modeling parameters or trend constraints are adjusted to perform iterative modeling until a three-dimensional geological model that matches the static geological features and dynamic production response is obtained.
9. A three-dimensional geological modeling system for gas reservoirs, characterized in that, The system includes: The acquisition unit is used to acquire multi-source data of the target work area, including seismic data, vertical seismic profile data, well logging data, seismic interpretation horizon and fault data, core analysis data and gas reservoir dynamic production data; A structural modeling unit is constructed to combine vertical seismic profile data and virtual well data to perform depth correction on the time-depth converted layers, thereby obtaining the corrected layers; based on the corrected layers and fault data, a three-dimensional structural framework model is established. The trend volume generation unit is used to generate a sand body spatial development probability trend volume by fusing the seismic attribute volume, the seismic inversion volume, and the determined optimal weight coefficients of the two. The three-dimensional lithofacies model establishment unit is used to establish multiple equally likely lithofacies models as initial realizations, with the three-dimensional structural lattice model as the spatial framework and the spatial development probability trend of sand bodies as the lateral soft constraint. By calculating the degree of agreement between each realization and the well point data and the spatial correlation with the seismic trend, a weighted average is performed to obtain the three-dimensional lithofacies model. The three-dimensional distribution model generation unit is used to perform geostatistical simulation of facies zones based on a three-dimensional lithofacies model as a constraint, and generate a three-dimensional distribution model of reservoir physical parameters. The static-dynamic coupling analysis and feedback optimization unit is used to import the three-dimensional lithofacies model and the three-dimensional distribution model into the numerical simulator, perform historical fitting using dynamic production data of the gas reservoir, and analyze the fitting differences; and based on the fitting differences, feed back fluid flow information to the three-dimensional lithofacies model and the three-dimensional distribution model for iterative modeling to obtain a three-dimensional geological model.
10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements a three-dimensional geological modeling method for gas reservoirs as described in any one of claims 1 to 8.