A method, device and equipment for constructing a porous medium CO2 migration model

Abstract

The application provides a method, device and equipment for constructing a porous medium CO2 migration model, relates to the technical field of carbon dioxide storage, and comprises the following steps: obtaining reference pore structure data and geological space parameters of a target area, and logging data of different depth points of each reservoir section of the target area; dividing the target area according to the geological space parameters and the logging data of the different depth points of each reservoir section of the target area, to obtain a plurality of reservoir units; constructing a mapping relationship model of micro parameters-macroscopic seepage response of each reservoir unit; obtaining seepage monitoring data of each reservoir section in the process of CO2 injection and migration; and correcting and fusing the mapping relationship model of micro parameters-macroscopic seepage response of each reservoir unit according to the seepage monitoring data and the geological space parameters, to construct a porous medium CO2 migration model of the target area. The application can construct a high-precision porous medium CO2 migration simulation model.

Description

Technical Field

[0001] This application relates to the field of carbon dioxide sequestration technology, and more specifically, to a method, apparatus, and equipment for constructing a porous medium CO2 transport model. Background Technology

[0002] Carbon dioxide (CO2) geological storage is one of the key negative emission technologies for achieving the "dual carbon" strategic goals. Its core lies in the safe and long-term injection of industrially captured CO2 into porous reservoirs such as deep saline aquifers or depleted oil and gas reservoirs. Accurately simulating the migration patterns of CO2 in complex heterogeneous reservoirs is a prerequisite for assessing the safety, capacity, and leakage risk of storage.

[0003] Currently, existing CO2 transport modeling methods are typically based on the assumption of a continuous medium, employing Darcy's law combined with two-phase flow models (such as TOUGH2 and ECLIPSE) for numerical simulation. Reservoir parameters (such as relative permeability and capillary pressure curves) are mostly determined through core experiments or assigned using empirical formulas, and the entire reservoir is often simplified into several homogeneous blocks, neglecting the spatial heterogeneity of the microstructure. Some methods attempt to introduce digital cores or machine learning to establish a mapping relationship between micro-parameters and macro-responses; however, these models are mostly static and offline, not linked to dynamic field monitoring data, and therefore fail to reflect the evolution and uncertainties of reservoir properties during actual injection. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus, and device for constructing a porous medium CO2 transport model, so as to solve the above-mentioned problems existing in the prior art and to construct a high-precision porous medium CO2 transport simulation model.

[0005] Firstly, a method for constructing a CO2 transport model in porous media is provided, which may include: Acquire baseline pore structure data and geological spatial parameters of the target area, as well as well logging data at various depths in different reservoir sections of the target area; Based on the geological spatial parameters and well logging data at various depths in different reservoir sections of the target area, the target area is divided into multiple reservoir units. Reservoir pore structure data for each reservoir unit are extracted from the reference pore structure data. Based on the geological spatial parameters and reservoir pore structure data of each reservoir unit, a mapping relationship model between the micro parameters and macroscopic seepage response of each reservoir unit is constructed. Acquire seepage monitoring data for each reservoir segment during CO2 injection and migration; Based on the seepage monitoring data and the geological spatial parameters, the mapping relationship model between the micro-parameters and macro-seepage response of each reservoir unit is modified and integrated to construct a porous media CO2 transport model for the target area.

[0006] In an optional implementation, the method for obtaining the reference pore structure data includes: A three-dimensional scan of the core sample in the target area was performed to obtain a three-dimensional grayscale image of the core sample; The three-dimensional grayscale image of the core sample is subjected to pore-matrix binarization segmentation to obtain a three-dimensional binarized model of the core pore space. The pore structure parameters of the three-dimensional binary model of the core pore space are calculated to obtain the reference pore structure data.

[0007] In an optional implementation, the geological spatial parameters include: planar geological distribution data and well location data corresponding to each depth point in different reservoir sections.

[0008] In an optional implementation, the target area is divided into multiple reservoir units based on the geological spatial parameters and well logging data at various depths in different reservoir sections of the target area, including: For any depth point in any reservoir segment, feature extraction is performed on the logging data at that depth point to obtain the logging response features at that depth point; Clustering was performed on the logging response characteristics at each depth point to obtain multiple clusters; Based on the planar geological distribution data and the well location data corresponding to each depth point in different reservoir segments, the depth points within each cluster are integrated into spatially continuous reservoir feature similarity sub-regions, resulting in multiple reservoir units.

[0009] In an optional implementation, reservoir pore structure data for each reservoir unit is extracted from the baseline pore structure data, including: For any reservoir unit, the average logging response characteristics of the reservoir unit are calculated based on the logging response characteristics at different depths within the reservoir unit. The reservoir pore structure data of the reservoir unit is obtained by performing correlation matching between the baseline pore structure data and the average logging response characteristics of the reservoir unit.

[0010] In an optional implementation, based on the geological spatial parameters and reservoir pore structure data of each reservoir unit, a mapping model of the micro-parameters-macro-permeability response of each reservoir unit is constructed, including: Based on the geological spatial parameters and reservoir pore structure data of each reservoir unit, a numerical simulation model of micropore fluid transport for each reservoir unit is constructed. Based on the configured multi-condition parameters, the numerical simulation model of micropore fluid transport in each reservoir unit is simulated under multiple conditions to obtain the micro-fluid transport characteristic data of each reservoir unit under different conditions. Multivariate statistical regression was used to perform correlation analysis on the reservoir pore structure data of each reservoir unit and the corresponding microscopic fluid transport characteristics data of the reservoir unit under different operating conditions, and to construct a mapping relationship model between the microscopic parameters and macroscopic seepage response of each reservoir unit.

[0011] In an optional implementation, based on the geological spatial parameters and reservoir pore structure data of each reservoir unit, a numerical simulation model of micropore fluid transport for each reservoir unit is constructed, including: For any reservoir unit, the geological spatial parameters and the reservoir pore structure data of the reservoir unit are coupled to generate an initial three-dimensional mesh of the reservoir unit. The initial three-dimensional mesh of the reservoir unit is binarized to obtain a three-dimensional pore space binarized model of the reservoir unit. Based on the configured scene parameters, the three-dimensional pore space binarization model of the reservoir unit is processed to obtain a geometric computational domain model including boundary conditions. The geometric computational domain model containing boundary conditions is filled with global physical properties to obtain a computational domain model containing physical properties. A mathematical description of the two-phase fluid transport dynamics process in the computational domain model containing physical properties is performed to obtain a numerical simulation model of the micropore fluid transport of the reservoir unit.

[0012] Secondly, an apparatus for constructing a porous media CO2 transport model is provided, the apparatus comprising: The first acquisition unit is used to acquire the benchmark pore structure data and geological spatial parameters of the target area, as well as the well logging data at various depths of different reservoir sections in the target area. The division unit is used to divide the target area into multiple reservoir units based on the geological spatial parameters and well logging data at various depths of different reservoir sections in the target area. The extraction unit is used to extract reservoir pore structure data of each reservoir unit from the reference pore structure data. The first construction unit is used to construct a mapping relationship model between the micro-parameters and macro-permeability response of each reservoir unit based on the geological spatial parameters and the reservoir pore structure data of each reservoir unit. The second acquisition unit is used to acquire seepage monitoring data of each reservoir segment during CO2 injection and migration. The second construction unit is used to modify and fuse the mapping relationship model of micro parameters-macro seepage response of each reservoir unit according to the seepage monitoring data and the geological spatial parameters, and construct a porous medium CO2 transport model of the target area.

[0013] Thirdly, an electronic device is provided, which includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in memory, it implements any of the steps described in the first aspect above.

[0014] Fourthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when executed by a processor, the computer program implements the steps of any of the methods described in the first aspect above.

[0015] This application divides the target area into multiple reservoir units, combines benchmark pore structure data, well logging data, and geological spatial parameters to construct an independent micro-parameter-macro-seepage response mapping model for each unit, and further utilizes actual seepage monitoring data collected during CO2 injection and migration to dynamically correct and fuse the model, significantly improving the accuracy, adaptability, and engineering practicality of CO2 migration simulation in porous media.

[0016] This application divides reservoir units based on well logging and geological parameters, and extracts unit-level pore features from benchmark pore structure data. This effectively preserves the microscopic heterogeneity of different sedimentary facies zones and diagenetic environments, and avoids misjudgments of CO2 migration paths and displacement efficiency by traditional homogenization modeling.

[0017] This application uses field seepage monitoring data (such as pressure, saturation, flow rate, etc.) to iteratively correct the mapping relationship model of each reservoir unit, so that micro parameters (such as porosity, permeability, and key points of relative permeability curves) can adaptively match the real seepage response, significantly improving the prediction accuracy and robustness of the model under dynamic injection conditions.

[0018] Based on the independent calibration of each reservoir unit model, this application seamlessly integrates multiple refined sub-models into a unified reservoir global CO2 migration model through spatial alignment and parameter coupling strategies. This not only preserves the accuracy of local details but also ensures global physical consistency, providing a high-fidelity simulation foundation for storage capacity assessment, leading edge tracking, and leakage risk early warning. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A system architecture diagram for constructing a porous medium CO2 transport model is provided in the embodiments of this application; Figure 2 A flowchart illustrating a method for constructing a porous medium CO2 transport model, as provided in an embodiment of this application; Figure 3 A schematic diagram of a device for constructing a porous medium CO2 transport model provided in this application embodiment; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by those skilled in the art. The words "first," "second," and similar terms used in this application do not indicate any order, quantity, or importance, but are only used to distinguish different components. The words "comprising" or "including," etc., mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, but do not exclude other elements or objects. The words "connected," "coupled," or "connected," etc., are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up," "down," "left," "right," etc., are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0022] The method for constructing a porous media CO2 transport model provided in this application embodiment can be applied to... Figure 1 In the system architecture shown, such as Figure 1As shown, the system may include a server and multiple data acquisition devices. The server can be a physical server, a server cluster consisting of multiple physical servers, or a distributed system. It can also be a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. The data acquisition devices and the server can be directly or indirectly connected via wired or wireless communication methods; this application does not impose any limitations on this connection.

[0023] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0024] Figure 2 This is a schematic flowchart illustrating a method for constructing a CO2 transport model in porous media, as provided in an embodiment of this application. Figure 2 As shown, the method may include: Step S210: Obtain the baseline pore structure data and geological spatial parameters of the target area, as well as the well logging data at various depths of different reservoir sections in the target area.

[0025] The target area is the storage area for carbon dioxide to be sealed; the benchmark pore structure data may include: spatial distribution data of porosity and permeability corresponding to different core samples; well logging data at each depth point in different reservoir sections may include resistivity, sonic transit time and density; geological spatial parameters may include: planar geological distribution data and well location data corresponding to each depth point in different reservoir sections; the planar geological distribution data may include: the planar distribution range and vertical thickness of each reservoir section.

[0026] In practice, geophysical logging instruments (such as resistivity logging instruments, sonic time-of-flight logging instruments, and density logging instruments) are used to collect multi-parameter logging data of all production wells and abandoned mines in the target area to obtain logging data of the target area. A high-resolution industrial CT scanner was used to perform non-destructive three-dimensional scanning of the core samples remaining in abandoned mines or production wells within the target area, resulting in three-dimensional grayscale images of each core sample. For any core sample's three-dimensional grayscale image, an adaptive threshold segmentation algorithm is used to perform pore-matrix binarization segmentation on the core sample's three-dimensional grayscale image, obtaining a three-dimensional binarized model of the core pore space. Specifically, the grayscale value distribution characteristics of the core's three-dimensional grayscale image are analyzed, and the grayscale threshold ranges of pores and rock matrix are automatically identified. Pixels in the core's three-dimensional grayscale image whose grayscale values ​​are in the pore range are marked as pore phases, and other pixels are marked as matrix phases, thus obtaining a three-dimensional binarized model of the core pore space. A pore network extraction algorithm was used to calculate the pore structure parameters of a three-dimensional binary model of the core pore space, obtaining the baseline pore structure data for the target area. Specifically, based on the three-dimensional binary model, the geometric morphology parameters of the pores were extracted. Using a fluid flow equivalent simulation method, the spatial distribution data of porosity and permeability of the corresponding local reservoir segment of the core sample were calculated based on the extracted pore geometric morphology parameters. Among them, the geometric morphology parameters include: pore diameter, pore throat ratio, and pore connectivity. The spatial distribution data of porosity and permeability of the corresponding local reservoir segment of the core sample were used as the baseline pore structure data for the target area.

[0027] Step S220: Based on the geological spatial parameters and the logging data at various depths of different reservoir sections in the target area, the target area is divided into multiple reservoir units.

[0028] In practice, for any depth point in any reservoir section, feature extraction is performed on the logging data at that depth point to obtain the logging response characteristics at that depth point; The K-means unsupervised clustering algorithm is used to cluster the logging response features of each depth point to obtain multiple clusters. Specifically, based on the logging response features of each depth point, the Euclidean distance of the logging response features of different depth points is calculated iteratively, and depth points with high feature similarity are grouped into the same cluster. Based on planar geological distribution data and wellbore location data corresponding to each depth point in different reservoir segments, the depth points within each cluster are spatially integrated to obtain multiple reservoir units. Specifically, using the wellbore location data corresponding to each depth point and planar geological distribution data, the depth points of wellbore reservoir segments within the same cluster that are adjacent in planar location and have consistent geological units are integrated into spatially continuous reservoir feature similarity sub-regions to obtain multiple reservoir units of the target storage area. Among them, any reservoir unit contains multiple depth points with similar reservoir features.

[0029] Step S230: Extract reservoir pore structure data for each reservoir unit from the baseline pore structure data.

[0030] In practice, based on the logging response characteristics at different depths within each reservoir unit, the average logging response characteristics of each reservoir unit are calculated. A feature matching algorithm is used to perform correlation matching processing on the benchmark pore structure data and the average logging response characteristics of each reservoir unit, resulting in reservoir pore structure data for each reservoir unit. Specifically, the source depth of the core sample corresponding to the benchmark pore structure data is obtained. Logging data at the source depth of the core sample is extracted from logging data at different depths in different reservoir sections of the target area, resulting in core logging data. Feature extraction is performed on the core logging data to obtain core logging features. A correlation mapping function between porosity, permeability, and logging response characteristics (resistivity, sonic transit time, density) is constructed. The similarity between the logging features corresponding to the benchmark pore structure and the average logging features of each reservoir unit is calculated. The benchmark pore structure with the highest similarity is matched to the corresponding reservoir unit, ultimately obtaining initial representative pore structure data specific to each reservoir unit as the reservoir pore structure data for each reservoir unit.

[0031] In another embodiment of this application, when only one core sample exists, the similarity between the logging characteristics corresponding to the benchmark pore structure and the average logging characteristics of each reservoir unit is calculated to obtain the similarity coefficient corresponding to each reservoir unit; for any reservoir unit, the benchmark pore structure is corrected using the similarity coefficient of the reservoir unit to obtain the reservoir pore structure data of the reservoir unit.

[0032] Step S240: Based on the geological spatial parameters and reservoir pore structure data of each reservoir unit, construct a mapping relationship model between the micro parameters and macroscopic seepage response of each reservoir unit.

[0033] In practice, based on geological spatial parameters and reservoir pore structure data of each reservoir unit, a numerical simulation model of micropore fluid transport for each reservoir unit is constructed. Based on the configured multi-condition parameters, the numerical simulation model of micropore fluid transport in each reservoir unit is simulated under multiple conditions to obtain microfluidic transport characteristic data under different conditions. Multivariate statistical regression was used to perform correlation analysis on the microscopic fluid transport characteristics data under multiple operating conditions and the corresponding reservoir pore structure data of each reservoir unit, and a mapping relationship model between the microscopic parameters and macroscopic seepage response of each reservoir unit was constructed.

[0034] In one embodiment of this application, a numerical simulation model of micropore fluid transport for each reservoir unit is constructed based on geological spatial parameters and reservoir pore structure data of each reservoir unit, which may include: For any reservoir unit, a spatial coordinate mapping algorithm is used to couple the geological spatial parameters and the reservoir pore structure data of that reservoir unit to generate an initial three-dimensional mesh for that reservoir unit. Specifically, the planar distribution range and vertical thickness of the reservoir unit are read from the geological spatial parameters to determine the three-dimensional spatial range of the reservoir unit. The resolution of the three-dimensional cubic mesh is determined based on the minimum permeability value in the permeability spatial distribution data of the reservoir unit. The three-dimensional cubic mesh is divided according to the three-dimensional spatial range and the three-dimensional cubic mesh resolution, with each mesh unit corresponding to a small spatial region within the reservoir. The spatial coordinates of each small spatial region are recorded to form a three-dimensional blank mesh skeleton with spatial coordinates. The porosity spatial distribution data and permeability spatial distribution data in the pore structure data of the reservoir unit are assigned to each unit of the blank mesh skeleton according to the spatial coordinates, clarifying the porosity and permeability values ​​of each small region, and obtaining a three-dimensional mesh dataset with attribute labels, which is the initial three-dimensional mesh of the reservoir unit. An equivalent pore network modeling algorithm is used to binarize the initial 3D mesh of the reservoir unit, resulting in a 3D pore space binarized model of the reservoir unit. Specifically, if the porosity of any mesh cell in the initial 3D mesh is not less than the configured critical porosity threshold, then the mesh cell is identified as a pore candidate cell; otherwise, it is directly marked as a matrix phase. For each pore candidate cell, the equivalent pore throat radius is calculated in reverse using the Kozeny-Carman classical equation. The core of the equation relates permeability to porosity, tortuosity, and specific surface area. If the equivalent pore throat radius of any pore candidate cell is not less than the configured critical throat radius... If a threshold is set, the candidate pore unit is considered a porous phase; the critical throat radius threshold can be 0.1 μm, used to characterize the minimum throat size for fluid flow; otherwise, the candidate pore unit is determined to be an isolated pore and is still labeled as a matrix phase. The porous phase grid connectivity paths are optimized based on the permeability spatial gradient: for regions with high permeability, the number of connected porous phase grids and the path width are increased; for regions with low permeability, the connectivity paths are reduced, and the porous phase grid dispersion is increased, resulting in optimized grid units. The optimized grid units are then globally labeled with porous phase as 1 and matrix phase as 0, resulting in a three-dimensional pore space binary model of the reservoir unit. A computational domain extraction algorithm was used to process the three-dimensional pore space binarized model of the reservoir unit, resulting in a geometric computational domain model with boundary conditions. Specifically, the three-dimensional physical computational domain of the model was delineated using the spatial extrema of the three-dimensional pore space binarized model of the reservoir unit as the boundary, and the length, width, and height dimensions were defined. The six outer boundaries of the computational domain were functionally labeled to match the parameters of the CO2 injection-formation water displacement engineering scenario: one face was selected as the CO2 injection boundary and set as a constant pressure boundary (the conventional injection pressure in engineering is 10-20 MPa); its opposite face was the formation water production boundary and set as a constant pressure boundary (the conventional original formation pressure in engineering is 5-10 MPa); the remaining four faces were non-flux closed boundaries to simulate the barrier effect of the reservoir interlayer. All matrix phase (0 value) mesh elements in the binarized model were defined as rigid, impermeable, and non-slip boundaries, i.e., the fluid velocity on the matrix surface is 0, thus obtaining a geometric computational domain model with boundary conditions. A parameter mapping method is used to fill the geometric computational domain model with boundary conditions with global physical properties, resulting in a computational domain model with physical properties. Specifically, the simulation system is set as a two-phase fluid of CO2 and formation water, and the input parameters are the standard physical parameters for industry engineering: formation water viscosity 0.5-1 mPa. s, density 1000kg / m³; CO2 viscosity 0.01-0.05mPa The density is calculated based on the conventional temperature and pressure conditions of the reservoir; and the initial values ​​of the configured contact angle and interfacial tension are assigned to the global porous phase grid of the computational domain to obtain a computational domain model with physical properties. The Lattice Boltzmann Method (LBM) is employed to mathematically describe the two-phase fluid transport dynamics process in a computational domain model with physical properties, resulting in a numerical simulation model of microporous fluid transport in the reservoir unit. Specifically, based on the BGK collision model, the core governing equations for mass conservation and momentum transport of the two-phase fluid are established. The particle distribution function evolution equation is used to describe the fluid transport law, clarifying key elements such as the particle distribution function, discrete velocity vector, time step, and collision operator. The governing equations are discretized into two-dimensional iterative calculation formulas in space and time, and the matching relationship between the time step and the spatial step is set to satisfy the Courant-Friedrichs-Lewy (CFL) stability condition to ensure the convergence of the calculation process. A grid-by-grid iterative solution method is adopted, and the convergence criterion is set that the fluid velocity change rate between adjacent iteration steps is less than 10. -6 The mathematical solution framework was completed, and a numerical simulation model of micropore fluid transport specific to this reservoir unit was obtained. A macroscopic parameter inverse algorithm was used to verify the constructed numerical simulation model of microporous fluid transport and determine its effectiveness. Specifically, initial empirical values ​​of contact angle and interfacial tension were input, and the model was run for steady-state simulation to obtain data on the spatial distribution of fluid velocity and saturation within the pores. The volume averaging method was used to back-calculate the macroscopic porosity and permeability of the reservoir unit based on the simulation results. The back-calculated results were compared with the original input reservoir pore structure data (spatial distribution of porosity and permeability). If the deviation was ≤10% of the engineering allowable threshold, the model was deemed effective. If the deviation exceeded the threshold, the model was returned to fine-tune the pore phase grid distribution ratio and reconstructed into a binary model until the deviation met the requirements.

[0035] In one embodiment of this application, performing multi-condition simulations on the micropore fluid transport numerical simulation model of each reservoir unit according to configured multi-condition parameters may include: In practice, a gradient design is used to combine operating conditions, with contact angles covering 0°-180° (each 30° represents a gradient) and interfacial tensions covering 0.01-0.1 N / m (each 0.01 N / m represents a gradient). This ensures coverage of hydrophilic, neutral, and oleophilic reservoirs, as well as scenarios with varying interfacial interaction intensities, without adding any additional parameters. Each set of operating condition parameters (contact angle + interfacial tension) is input into the micropore fluid transport numerical simulation model of each reservoir unit, and steady-state iterative calculations are initiated until the model meets the convergence criteria (the rate of change of fluid velocity between adjacent iteration steps < 10). -6 The simulation of microfluidic transport under this working condition was completed, and simulation results under different working conditions were obtained. For the simulation results of each working condition, fluid distribution characteristics and fluid transport characteristics were extracted. The fluid distribution characteristics are the spatial distribution of CO2 and formation water saturation and the interface morphology of the two-phase fluid within the pore phase grid. The fluid transport characteristics are the fluid velocity, pressure gradient, and CO2 displacement efficiency (formation water discharge ratio) data in the pore throats of different regions, which are correlated with the porosity and permeability data of the reservoir unit according to spatial coordinates. The parameters of all working conditions are combined with the corresponding extracted feature data, and the data are classified and organized according to the reservoir unit to form a multi-working-condition feature dataset of microfluidic transport of the reservoir unit, and the working condition parameters corresponding to each data are labeled.

[0036] In one embodiment of this application, a correlation analysis is performed on the multi-condition characteristic data of microscopic fluid transport of each reservoir unit and the corresponding reservoir pore structure data to construct a mapping relationship model between the microscopic parameters and macroscopic seepage response of each reservoir unit, which may include: From the multi-condition characteristic dataset of micro-fluid transport in each reservoir unit, micro-quantitative parameters under each condition are extracted. These micro-quantitative parameters may include the average CO2 displacement efficiency, the average flow velocity of fluid in the pore throat, the average propulsion velocity of the two-phase interface, and the saturation ratio of the high-permeability zone. Extract operating parameters (contact angle, interfacial tension) and reservoir pore structure data (average porosity, average permeability) of different reservoir units under different operating conditions to form a standardized correlation analysis dataset. The micro-quantitative parameters of each reservoir unit are set as dependent variables, and the operating parameters and corresponding reservoir pore structure data are set as independent variables to clarify the correlation dimension of the mapping relationship. The multivariate nonlinear regression algorithm is used to fit the standardized correlation analysis dataset to establish the mathematical correlation between the dependent and independent variables, and to clarify the influence weight and correlation coefficient of each independent variable on the macroscopic seepage response. The dataset is divided into training and validation sets proportionally. The regression model is built using the training set, and the validation set data is substituted into the model to calculate the predicted values. The deviation between the predicted and actual values ​​is compared. If the deviation is ≤10%, the mapping relationship model is considered effective. If the deviation exceeds the threshold, the core micro-quantitative indicators are re-selected and the fitting algorithm is optimized. The mapping relationship model between the micro-parameters and macro-seepage response of the reservoir unit is completed. The model includes clear mathematical correlations, the influence weights of each parameter, and the applicable scope. The macro-seepage effect can be directly predicted through micro-parameters and pore structure parameters.

[0037] Step S250: Obtain seepage monitoring data for each reservoir segment during CO2 injection and migration.

[0038] In practice, based on the spatial distribution, seepage characteristics, and monitoring requirements of each reservoir unit, corresponding seepage monitoring equipment is deployed in each reservoir segment of the target area to collect seepage monitoring data at different monitoring nodes and time periods in each reservoir segment. Specifically, in conjunction with the previously defined reservoir unit boundaries, representative monitoring points are selected within each reservoir unit, and pressure sensors, flow meters, and other equipment are deployed to collect macroscopic seepage response data at each monitoring point, including seepage velocity, displacement efficiency, pore pressure distribution, and fluid saturation. The collected monitoring data undergoes time synchronization calibration, outlier removal, and accuracy verification. Abnormal data points caused by equipment failure or environmental interference are removed. The data format and units of measurement are standardized, and the data is organized in the order of reservoir unit, monitoring node, and monitoring time to form a standardized seepage monitoring dataset for each reservoir segment, ensuring that the monitoring data accurately corresponds to each reservoir unit.

[0039] Step S260: Based on seepage monitoring data and geological spatial parameters, the mapping relationship model of micro-parameters-macro-seepage response of each reservoir unit is modified and integrated to construct a porous medium CO2 transport model for the target area.

[0040] In practice Based on the seepage monitoring data of each reservoir segment and the predicted data of the mapping relationship model of micro-parameters-macro-seepage response of each reservoir unit, the mapping relationship model is iteratively corrected to obtain the corrected mapping relationship model of micro-parameters-macro-seepage response for each reservoir unit. Specifically, the mapping relationship model of micro-parameters-macro-seepage response of each reservoir unit is called, and the predicted macro-seepage response data of each reservoir unit is output. This data is then matched and compared with the seepage monitoring data of each reservoir segment. Through deviation calculation and iterative optimization, the model correction is completed. The micro-parameters built into the mapping relationship model of each reservoir unit are used as initial inputs to drive the model calculation, obtaining the corresponding data for each monitoring node. The macroscopic seepage response prediction data (which fully correspond to the index types and monitoring nodes of the monitoring data) are obtained. A numerical difference algorithm is used to calculate the deviation between the prediction data and the seepage monitoring data node by node. The overall deviation is quantified by variance analysis to generate a micro-parameter correction deviation dataset for each reservoir unit. An ensemble Kalman filter iterative algorithm is used to substitute the correction deviation dataset into the model and dynamically adjust the built-in micro-parameters of the model parameter by parameter. Each time the model prediction data is adjusted, it is recalculated and compared with the monitoring data until the model prediction deviation is less than the preset accuracy threshold. The iteration stops, and finally the mapping relationship model between the corrected micro-parameters and macroscopic seepage response of each reservoir unit is obtained. Based on the modified mapping relationship model of micro-parameters and macro-permeability response for each reservoir unit, the optimal micro-parameter estimates for each reservoir unit are extracted. Specifically, the modified mapping relationship model for each reservoir unit is analyzed to extract the micro-parameter values ​​that minimize the deviation between the predicted data and the field monitoring data after iterative optimization, which are then used as the optimal micro-parameter estimates for each reservoir unit. The modified mapping relationship model is invoked, and all calibrated micro-parameter values ​​within the model are read. Combined with the adjustment records of each parameter during the iteration process, the parameter combinations with the smallest deviations are selected to clarify the optimal values ​​for each micro-parameter within each reservoir unit. The extracted optimal micro-parameter estimates are then validated for rationality, ensuring that their range conforms to the reservoir pore structure characteristics and engineering realities. Unreasonable values ​​are eliminated and the model is recalibrated, ultimately forming a dataset of optimal micro-parameter estimates specific to each reservoir unit, providing core parameters for subsequent sub-model construction. The optimal microscopic parameter estimates are substituted into the pre-constructed initial porous media CO2 transport models of each reservoir unit to construct refined porous media CO2 transport sub-models for each reservoir unit. Specifically, the pre-constructed initial porous media CO2 transport models of each reservoir unit (including reservoir unit spatial geometry information and basic pore structure information) are obtained, and the optimal microscopic parameter estimates of each reservoir unit are substituted into the initial models one by one to complete model parameter reconstruction and accuracy optimization. In this process, the parameter input interfaces of the initial CO2 transport models of each reservoir unit are extracted, and the optimal microscopic parameter estimates (porosity, permeability, throat radius, etc.) are categorized by parameter type. Each initial microscopic parameter in the model is replaced with a new value. Based on the new microscopic parameters, the calculation logic of CO2 transport within the model is recalibrated, including the parameters of the seepage equation, the calculation of fluid flow resistance, and the adaptation of the mass conservation equation, to optimize the calculation accuracy of the model. The calibrated sub-model is then tested and verified by inputting typical operating conditions to check whether the CO2 transport characteristics (pressure distribution, velocity changes, etc.) output by the model meet the engineering expectations. Parameters that do not meet the expectations are fine-tuned. Finally, the construction of refined porous media CO2 transport sub-models for each reservoir unit is completed, ensuring that each sub-model can accurately characterize the CO2 transport law within the corresponding reservoir unit.

[0041] A refined porous media CO2 migration sub-model for each reservoir unit is spatially stitched and parameter-coupled to construct a global refined porous media CO2 migration model for the target area reservoir. Specifically, based on the acquired geological spatial parameters, the refined CO2 migration sub-models of all reservoir units are spatially aligned and their boundaries are stitched together. Simultaneously, parameter coupling between the sub-models is completed to form a unified global model. The spatial geometric boundary coordinates of each reservoir unit sub-model are extracted, and combined with the reservoir planar distribution and vertical depth distribution characteristics in the geological spatial parameters, the spatial positioning calibration of each sub-model is performed to eliminate boundary gaps and spatial overlaps between sub-models, ensuring that the spatial distribution of the sub-models corresponds to the actual reservoir. The geological morphology is completely consistent; a sub-model parameter coupling matrix is ​​constructed to integrate the microscopic parameters and seepage parameters of each sub-model, and the parameter connection relationship between adjacent reservoir unit sub-models is established to ensure that the parameter transfer is continuous and reasonable when CO2 migrates across units; through global coupling calculation, the sub-models of all reservoir units are merged into a whole, and the merged global model is verified as a whole to simulate the global CO2 migration process in the target area, check whether the model output results conform to the overall reservoir seepage characteristics, correct coupling deviations, and finally complete the construction of a refined porous medium CO2 migration model of the target area reservoir, providing core technical support for subsequent reservoir CO2 storage, migration monitoring and scheme optimization.

[0042] Corresponding to the above method, this application also provides an apparatus for constructing a porous medium CO2 transport model, such as... Figure 3 As shown, the device includes: The first acquisition unit 310 is used to acquire the benchmark pore structure data and geological spatial parameters of the target area, as well as the well logging data at various depths of different reservoir sections in the target area. Division unit 320 is used to divide the target area into multiple reservoir units based on geological spatial parameters and well logging data at various depths of different reservoir sections in the target area. Extraction unit 330 is used to extract reservoir pore structure data of each reservoir unit from the reference pore structure data; The first building unit 340 is used to build a mapping relationship model of micro parameters and macro seepage response of each reservoir unit based on geological spatial parameters and reservoir pore structure data of each reservoir unit. The second acquisition unit 350 is used to acquire seepage monitoring data of each reservoir segment during CO2 injection and migration. The second building unit 360 is used to modify and integrate the mapping relationship model of micro parameters-macro seepage response of each reservoir unit based on seepage monitoring data and geological spatial parameters, and to build a porous medium CO2 transport model for the target area.

[0043] The functions of each unit in the porous media CO2 transport model construction device provided in the above embodiments of this application can be realized through the above method steps. Therefore, the specific working process and beneficial effects of each unit in the porous media CO2 transport model construction device provided in the embodiments of this application will not be repeated here.

[0044] This application also provides an electronic device, such as... Figure 4 As shown, it includes a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other through the communication bus 440.

[0045] Memory 430 is used to store computer programs; When the processor 410 executes the program stored in the memory 430, it performs the following steps: Acquire baseline pore structure data and geological spatial parameters of the target area, as well as well logging data at various depths in different reservoir sections of the target area; Based on geological spatial parameters and well logging data at various depths in different reservoir sections of the target area, the target area is divided into multiple reservoir units. Reservoir pore structure data for each reservoir unit is extracted from the baseline pore structure data. Based on geological spatial parameters and reservoir pore structure data of each reservoir unit, a mapping model of the micro-parameters and macro-seepage response of each reservoir unit is constructed. Acquire seepage monitoring data for each reservoir segment during CO2 injection and migration; Based on seepage monitoring data and geological spatial parameters, the mapping relationship model between micro-parameters and macro-seepage response of each reservoir unit is modified and integrated to construct a porous media CO2 transport model for the target area.

[0046] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0047] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0048] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0049] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0050] The implementation methods and beneficial effects of the various components of the electronic device in the above embodiments for solving the problem can be found in [reference needed]. Figure 2 The steps in the illustrated embodiments are used to implement the electronic device. Therefore, the specific working process and beneficial effects of the electronic device provided in this application will not be repeated here.

[0051] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform the method for constructing a porous medium CO2 transport model as described in any of the above embodiments.

[0052] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute the method for constructing a porous medium CO2 transport model as described in any of the above embodiments.

[0053] Those skilled in the art will understand that the embodiments in this application can be provided as methods, systems, or computer program products. Therefore, the embodiments in this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments in this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0054] This application describes embodiments of methods, apparatus (systems), and computer program products according to embodiments of this application with reference to flowchart illustrations and / or block diagrams. 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 illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0055] 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.

[0056] 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 1One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0057] Although preferred embodiments have been described in this application, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of this application.

[0058] 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 this application and its equivalents, then these modifications and variations are also intended to be included in the embodiments of this application.

Claims

1. A method for constructing a CO2 transport model in porous media, characterized in that, The method includes: Acquire baseline pore structure data and geological spatial parameters of the target area, as well as well logging data at various depths in different reservoir sections of the target area; Based on the geological spatial parameters and well logging data at various depths in different reservoir sections of the target area, the target area is divided into multiple reservoir units. Reservoir pore structure data for each reservoir unit are extracted from the reference pore structure data. Based on the geological spatial parameters and reservoir pore structure data of each reservoir unit, a mapping relationship model between the micro parameters and macroscopic seepage response of each reservoir unit is constructed. Acquire seepage monitoring data for each reservoir segment during CO2 injection and migration; Based on the seepage monitoring data and the geological spatial parameters, the mapping relationship model between the micro-parameters and macro-seepage response of each reservoir unit is modified and integrated to construct a porous media CO2 transport model for the target area.

2. The method as described in claim 1, characterized in that, The method for obtaining the reference pore structure data includes: A three-dimensional scan of the core sample in the target area was performed to obtain a three-dimensional grayscale image of the core sample; The three-dimensional grayscale image of the core sample is subjected to pore-matrix binarization segmentation to obtain a three-dimensional binarized model of the core pore space; The pore structure parameters of the three-dimensional binary model of the core pore space are calculated to obtain the reference pore structure data.

3. The method as described in claim 1, characterized in that, The geological spatial parameters include: planar geological distribution data and wellbore location data corresponding to each depth point in different reservoir sections.

4. The method as described in claim 3, characterized in that, Based on the aforementioned geological spatial parameters and well logging data at various depths in different reservoir sections of the target area, the target area is divided into multiple reservoir units, including: For any depth point in any reservoir segment, feature extraction is performed on the logging data at that depth point to obtain the logging response features at that depth point; Clustering was performed on the logging response characteristics at each depth point to obtain multiple clusters; Based on the planar geological distribution data and the well location data corresponding to each depth point in different reservoir segments, the depth points within each cluster are integrated into spatially continuous reservoir feature similarity sub-regions, resulting in multiple reservoir units.

5. The method as described in claim 4, characterized in that, Reservoir pore structure data for each reservoir unit is extracted from the baseline pore structure data, including: For any reservoir unit, the average logging response characteristics of the reservoir unit are calculated based on the logging response characteristics at different depth points within the reservoir unit. The reservoir pore structure data of the reservoir unit is obtained by performing correlation matching between the baseline pore structure data and the average logging response characteristics of the reservoir unit.

6. The method as described in claim 1, characterized in that, Based on the geological spatial parameters and reservoir pore structure data of each reservoir unit, a mapping model of the microscopic parameters-macroscopic seepage response of each reservoir unit is constructed, including: Based on the geological spatial parameters and reservoir pore structure data of each reservoir unit, a numerical simulation model of micropore fluid transport for each reservoir unit is constructed. Based on the configured multi-condition parameters, the numerical simulation model of micropore fluid transport in each reservoir unit is simulated under multiple conditions to obtain the micro-fluid transport characteristic data of each reservoir unit under different conditions. Multivariate statistical regression was used to perform correlation analysis on the reservoir pore structure data of each reservoir unit and the corresponding microscopic fluid transport characteristics data of the reservoir unit under different operating conditions, and to construct a mapping relationship model between the microscopic parameters and macroscopic seepage response of each reservoir unit.

7. The method as described in claim 6, characterized in that, Based on the aforementioned geological spatial parameters and reservoir pore structure data for each reservoir unit, a numerical simulation model of microscopic pore fluid transport for each reservoir unit is constructed, including: For any reservoir unit, the geological spatial parameters and the reservoir pore structure data of the reservoir unit are coupled to generate an initial three-dimensional mesh of the reservoir unit. The initial three-dimensional mesh of the reservoir unit is binarized to obtain a three-dimensional pore space binarized model of the reservoir unit. Based on the configured scene parameters, the three-dimensional pore space binarization model of the reservoir unit is processed to obtain a geometric computational domain model including boundary conditions. The geometric computational domain model containing boundary conditions is filled with global physical properties to obtain a computational domain model containing physical properties. A mathematical description of the two-phase fluid transport dynamics process in the computational domain model containing physical properties is performed to obtain a numerical simulation model of the micropore fluid transport of the reservoir unit.

8. A device for constructing a porous medium CO2 transport model, characterized in that, The device includes: The first acquisition unit is used to acquire the benchmark pore structure data and geological spatial parameters of the target area, as well as the well logging data at various depths of different reservoir sections in the target area. The division unit is used to divide the target area into multiple reservoir units based on the geological spatial parameters and well logging data at various depths of different reservoir sections in the target area. The extraction unit is used to extract reservoir pore structure data of each reservoir unit from the reference pore structure data. The first construction unit is used to construct a mapping relationship model between the micro-parameters and macro-permeability response of each reservoir unit based on the geological spatial parameters and the reservoir pore structure data of each reservoir unit. The second acquisition unit is used to acquire seepage monitoring data of each reservoir segment during CO2 injection and migration. The second construction unit is used to modify and fuse the mapping relationship model of micro parameters-macro seepage response of each reservoir unit according to the seepage monitoring data and the geological spatial parameters, and construct a porous medium CO2 transport model of the target area.

9. An electronic device, characterized in that, The electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method described in any one of claims 1-7.

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