An oilfield emulsion polymer dosage numerical simulation method and system

Abstract

The application relates to the technical field of digital twin simulation, in particular to an oilfield emulsion polymer dosage numerical simulation method and system, which are used to solve the technical problem that the accurate dosage of salt-resistant emulsion polymer in the oil displacement process cannot be accurately determined at present. The method comprises the following steps: based on core displacement experiment data and geological static data, the geological feature complexity of each three-dimensional data body in a target oil reservoir area is calculated, and the grid division of a three-dimensional oil reservoir model is optimized according to the geological feature complexity; based on the degradation kinetics characteristics of the emulsion polymer, a three-dimensional oil reservoir numerical model with optimized grid division and fused geological heterogeneity is constructed; the three-dimensional oil reservoir numerical model is calibrated through history fitting based on the core displacement experiment data; the displacement process under different emulsion polymer dosage schemes is simulated based on the calibrated three-dimensional oil reservoir numerical model, development effect evaluation indexes are obtained, and the economically optimal emulsion polymer dosage is determined based on the evaluation indexes and cost parameters.

Description

Technical Field

[0001] This application relates to the field of digital twin simulation technology, specifically to a numerical simulation method and system for the dosage of oilfield emulsion polymers. Background Technology

[0002] Polymer flooding is required in oil extraction. However, traditional polymers are prone to degradation and failure in high-temperature, high-salt reservoirs. Therefore, high-temperature resistant, salt-resistant emulsion polymers are often used to replace traditional polymers for oil displacement. However, salt-resistant emulsion polymers are expensive, and insufficient dosage leads to substandard oil displacement effects, while excessive use increases development costs. Therefore, determining the accurate dosage of salt-resistant emulsion polymers in the oil displacement process has become a pressing technical problem. Summary of the Invention

[0003] To address the current technical problem of being unable to accurately determine the precise dosage of salt-resistant emulsion polymers during oil displacement processes, this application aims to provide a numerical simulation method for oilfield emulsion polymer dosage. The specific technical solution adopted is as follows:

[0004] Acquire geological static data and core displacement test data of the target oil reservoir area. The geological static data includes a three-dimensional data volume consisting of hard data based on wellbore measurements and soft data based on geophysical exploration.

[0005] Based on core displacement experimental data and geological static data, the geological feature complexity of each three-dimensional data volume in the target reservoir area is calculated, and the mesh division of the three-dimensional reservoir model is optimized according to the geological feature complexity.

[0006] Based on the degradation kinetics of emulsion polymers, a three-dimensional reservoir numerical model with optimized mesh generation and incorporating geological heterogeneity is constructed.

[0007] Historical fitting calibration of the three-dimensional reservoir numerical model was performed based on core displacement experimental data.

[0008] Based on the calibrated three-dimensional reservoir numerical model, the displacement process under different emulsion polymer dosage schemes was simulated to obtain development effect evaluation indicators, and the economically optimal emulsion polymer dosage was determined based on the evaluation indicators and cost parameters.

[0009] In one possible implementation, the geological feature complexity of each three-dimensional data volume within the target reservoir area is calculated based on core displacement experimental data and geological static data. This includes: performing spatial interpolation correction on soft data based on hard data to obtain corrected three-dimensional data volumes; performing geological feature clustering on the corrected three-dimensional data volumes based on a preset clustering algorithm; calculating the geological feature difference between each three-dimensional data volume and the core based on the core displacement experimental data and the geological static data of each three-dimensional data volume within the cluster where the core is located; and calculating the geological feature complexity of each three-dimensional data volume based on the geological feature difference and the core displacement features determined from the core displacement experimental data.

[0010] In one possible implementation, the meshing of the three-dimensional reservoir model is optimized based on the complexity of geological features, including: dynamically adjusting the subdivision level of the corresponding three-dimensional data volume region in the octree meshing algorithm according to the complexity of geological features; and generating a non-uniform optimized mesh structure based on the adjusted subdivision level.

[0011] In one possible implementation, based on the core displacement experimental data and the geological static data of each 3D data volume within the cluster containing the core, the geological feature difference degree between each 3D data volume and the core is calculated. This includes: for the target geological static data, constructing a graph structure with each 3D data volume within the cluster as a node and the difference in target geological static data values ​​between nodes as the edge weights; the target geological static data is one of multiple types of geological static data; calculating the first topological complexity feature value of the graph structure; sequentially setting the target geological static data corresponding to each node to zero, recalculating the second topological complexity feature value of the graph structure, and determining the change in the topological complexity feature value of each node based on the difference between each second topological complexity feature value and the first topological complexity feature value; determining the contribution of each node to the spatial structure of the target geological static data based on the change in the topological complexity feature value of each node; calculating the difference between the contribution of each 3D data volume node and the contribution between the core nodes to obtain the difference component of the target geological static data; and determining the geological feature difference degree based on the difference component of each geological static data.

[0012] In one possible implementation, a three-dimensional reservoir numerical model with optimized mesh generation and incorporating geological heterogeneity is constructed based on the degradation kinetics of the emulsion polymer. This includes: determining the degradation kinetic parameters of the emulsion polymer based on data showing the decay of the emulsion polymer concentration over time; obtaining the data on the decay of the emulsion polymer concentration over time based on core displacement experiments; coupling the degradation kinetic parameters with the temperature field in the three-dimensional reservoir numerical model to obtain the degradation kinetic relationship, which is used to dynamically characterize the effective viscosity change of the emulsion polymer under reservoir conditions; and inputting the optimized mesh generation, geological static data, and the coupled degradation kinetic relationship into numerical simulation software to construct the three-dimensional reservoir numerical model.

[0013] In one possible implementation, the core displacement experimental data includes experimental recovery curves and experimental pressure responses. Based on the core displacement experimental data, a three-dimensional reservoir numerical model is historically fitted and calibrated, including: establishing an initial numerical model; ensuring the initial numerical model is consistent with the conditions used when acquiring the core displacement experimental data; running the initial numerical model to obtain simulated recovery curves and simulated pressure responses; comparing the simulated recovery curves and simulated pressure responses with the experimental recovery curves and experimental pressure responses, respectively; when the deviation between the simulated recovery curve and the experimental recovery curve exceeds a first preset threshold, or the deviation between the simulated pressure response and the experimental pressure response exceeds a second preset threshold, adjusting at least one physical property parameter in the initial numerical model and rerunning the model for comparison until all deviations are less than their corresponding preset thresholds.

[0014] In one possible implementation, the development effect evaluation indicators include: recovery rate evaluation indicators and heterogeneity evaluation indicators; based on a calibrated three-dimensional reservoir numerical model, the displacement process under different emulsion polymer dosage schemes is simulated to obtain development effect evaluation indicators, including: obtaining the cumulative recovery rate enhancement value and reservoir pressure field distribution data simulated under different emulsion polymer dosage schemes; calculating the recovery rate evaluation indicator characterizing the recovery rate enhancement efficiency based on the sequence of cumulative recovery rate enhancement value changing with dosage; and calculating the heterogeneity evaluation indicator characterizing the degree of spatial heterogeneity of the pressure field based on the reservoir pressure field distribution data.

[0015] In one possible implementation, the economically optimal emulsion polymer dosage is determined based on evaluation indicators and cost parameters, including: calculating the crude oil production increase revenue for each emulsion polymer dosage scheme based on the recovery rate evaluation indicators corresponding to different emulsion polymer dosage schemes, combined with geological reserves and crude oil prices; calculating the total input cost for each dosage scheme based on the total polymer dosage, polymer unit price, and injection operation cost corresponding to each dosage scheme; calculating the net present value (NPV) for each dosage scheme based on the crude oil production increase revenue and total input cost; determining the inflection point where the marginal NPV changes from positive to negative in the curve of NPV versus dosage, and determining the emulsion polymer dosage corresponding to the inflection point as the economically optimal emulsion polymer dosage.

[0016] In one possible implementation, the method further includes: generating injection control commands based on the economically optimal emulsion polymer dosage to control the multi-well injection equipment to perform emulsion polymer injection; monitoring the polymer injection concentration and instantaneous injection flow rate of each injection well in real time; and generating adjustment commands to dynamically adjust the operating parameters of the multi-well injection equipment in response to the deviation of the monitored polymer injection concentration or instantaneous injection flow rate from the target set value exceeding an allowable threshold, thereby achieving closed-loop control of the injection parameters.

[0017] This application also provides a numerical simulation system for oilfield emulsion polymer usage, the system comprising:

[0018] The acquisition unit is used to acquire geological static data and core displacement test data of the target reservoir area. The geological static data includes a three-dimensional data volume consisting of hard data based on wellbore measurements and soft data based on geophysical exploration.

[0019] The processing unit is used to calculate the geological feature complexity of each three-dimensional data volume in the target reservoir area based on core displacement experimental data and geological static data, and optimize the mesh division of the three-dimensional reservoir model according to the geological feature complexity.

[0020] The processing unit is also used to construct a three-dimensional reservoir numerical model with optimized mesh generation and geological heterogeneity based on the degradation kinetics of emulsion polymers.

[0021] The processing unit is also used to perform historical fitting calibration of the three-dimensional reservoir numerical model based on core displacement experimental data.

[0022] The processing unit is also used to simulate the displacement process under different emulsion polymer dosage schemes based on the calibrated three-dimensional reservoir numerical model, obtain development effect evaluation indicators, and determine the economically optimal emulsion polymer dosage based on the evaluation indicators and cost parameters.

[0023] This application offers the following advantages: The three-dimensional reservoir numerical model constructed in this application combines hard and soft data, making the geological foundation of the model more closely resemble the actual reservoir. The combination of geological feature complexity calculation and mesh optimization enables the model to accurately reflect the heterogeneous characteristics of the reservoir, avoiding the limitations of uniform meshes in highlighting key areas. Simultaneously, the integration of degradation kinetics characteristics characterizes the dynamic changes of emulsion polymers in the reservoir environment, reducing prediction bias caused by neglecting degradation effects. Subsequently, experimental calibration further reduces model uncertainty and improves simulation reliability. The economically optimal dosage is determined through simulation with multiple dosage schemes. Based on this, the numerical simulation method for oilfield emulsion polymer dosage provided in this application can accurately determine the precise dosage of salt-resistant emulsion polymers during oil displacement, solving the problems of traditional dosage designs either failing to achieve the desired oil displacement effect or incurring excessive costs. Attached Figure Description

[0024] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A flowchart illustrating a numerical simulation method for oilfield emulsion polymer dosage provided in one embodiment of this application. Figure 1 ;

[0026] Figure 2 A flowchart illustrating a numerical simulation method for oilfield emulsion polymer dosage provided in one embodiment of this application. Figure 2 ;

[0027] Figure 3 A flowchart illustrating a numerical simulation method for oilfield emulsion polymer dosage provided in one embodiment of this application. Figure 3 ;

[0028] Figure 4 A flowchart illustrating a numerical simulation method for oilfield emulsion polymer dosage provided in one embodiment of this application. Figure 4 ;

[0029] Figure 5 A flowchart illustrating a numerical simulation method for oilfield emulsion polymer dosage provided in one embodiment of this application. Figure 5 ;

[0030] Figure 6 This is a schematic diagram illustrating the relationship between the amount of emulsion polymer used and the recovery rate improvement value provided in one embodiment of this application.

[0031] Figure 7 A flowchart illustrating a numerical simulation method for oilfield emulsion polymer dosage provided in one embodiment of this application. Figure 6 ;

[0032] Figure 8 This is a schematic diagram of the architecture of a numerical simulation method system for oilfield emulsion polymer usage provided in one embodiment of this application. Detailed Implementation

[0033] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a numerical simulation method for oilfield emulsion polymer dosage proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0035] The following description, in conjunction with the accompanying drawings, details the specific scheme of the numerical simulation method for oilfield emulsion polymer usage provided in this application.

[0036] Please see Figure 1 It illustrates a flowchart of a numerical simulation method for oilfield emulsion polymer dosage provided in one embodiment of this application, as shown below. Figure 1 As shown, the method includes the following steps:

[0037] Step 101: Obtain the geological static data and core displacement experiment data of the target oil reservoir area.

[0038] Among them, the geological static data includes a three-dimensional data volume consisting of hard data based on wellbore measurements and soft data based on geophysical exploration.

[0039] Optionally, when numerically simulating the amount of emulsion polymer used in oilfields, it is necessary to obtain the geological static data and core displacement experimental data of the target block in order to construct a reliable numerical model and accurately characterize the oil displacement mechanism. Specifically, this includes: obtaining the geological static data of the target block from the oilfield's geological database (e.g., location information, permeability, porosity, saturation, profile data, and other logging data and seismic data); the target block contains multiple wells, and for each well, multiple vertical monitoring points (corresponding to different rock layers) are set in the well trajectory, and each monitoring point corresponds to a set of geological static data.

[0040] For the target area block, all three-dimensional data volumes of the block can be obtained through methods such as seismic surveys. The three-dimensional data volumes must contain the location information and geological static data of each data volume, and are themselves a regular three-dimensional mesh structure composed of discrete voxels. Detailed oil displacement test reports of core samples from wells within the target area block are obtained from the oilfield laboratory, and key experimental parameters (such as median emulsion particle size, emulsion viscosity enhancement curves, etc.) are extracted from them.

[0041] During data acquisition, geological static data may be affected by factors such as sensor interference or ground vibration, resulting in noise interference in the acquired static data. This application can filter the geological static data based on filtering algorithms (e.g., moving average filtering, median filtering, etc.) to eliminate interference from external factors. It should be noted that the above-mentioned process of filtering geological static data based on filtering algorithms can refer to relevant technologies, and this application does not limit it in this regard.

[0042] Step 102: Based on core displacement experimental data and geological static data, calculate the geological feature complexity of each three-dimensional data volume in the target reservoir area, and optimize the mesh division of the three-dimensional reservoir model according to the geological feature complexity.

[0043] Among them, geological feature complexity is used as a static index to quantify the geological differences between each three-dimensional data volume of the target reservoir and the oil-displaceable area where the core is located.

[0044] As one possible approach, in this step, soft data (3D seismic exploration data volume) is corrected using hard data (well monitoring point data) to reduce data errors. The corrected 3D data volume is then clustered according to geological features to define a specific range for subsequent complexity calculations. After this, the core displacement situation is calculated by combining the core parameters extracted from the core experiments. The contribution of each 3D data volume to the geological spatial structure is analyzed by constructing a graph structure, quantifying the geological feature complexity of each data volume, and clarifying the geological criticality of different regions. Finally, the subdivision level of the octree mesh is dynamically adjusted according to the complexity, making the mesh more refined for geologically complex and critical regions for simulation, while the mesh of clusters without core data is not subdivided, thereby generating a non-uniform optimized mesh.

[0045] Step 103: Based on the degradation kinetics of emulsion polymers, construct a three-dimensional reservoir numerical model with optimized mesh generation and incorporating geological heterogeneity.

[0046] Optionally, the above degradation kinetics characteristics are used to characterize the relationship between the degradation rate of the fixed emulsion polymer and temperature.

[0047] The optimization of mesh generation includes: static processing based on the geological feature complexity of the 3D data volume after hard data correction and cluster preprocessing, and the derived average static data weights, and determining the degree of mesh subdivision for each 3D data volume through an octree mesh optimization algorithm.

[0048] Integrating geological heterogeneity includes: taking into account the spatial differences in parameters such as reservoir permeability and porosity reflected in the three-dimensional data volume (including location and geological static data) after correction of in-well measured hard data and division by clustering algorithm, and combining the quantitative results of complex geological features to fix and integrate them into the three-dimensional reservoir model.

[0049] Step 104: Perform history fitting calibration on the three-dimensional reservoir numerical model based on core displacement experimental data.

[0050] Optionally, in this step, core displacement experimental data is used as the calibration benchmark. Through reverse engineering, key model parameters are iteratively adjusted to eliminate the deviation between simulated and experimental data, ensuring that the three-dimensional reservoir numerical model can accurately characterize the displacement process of emulsion polymers, reducing model uncertainty and providing a reliable model basis for subsequent full-oilfield-scale dosage simulation and decision-making.

[0051] As one possible approach, when performing history fitting calibration on a three-dimensional reservoir numerical model, a virtual core model with geometric dimensions and initial state completely identical to the experimental core is first constructed in numerical simulation software (such as Chemeor). This virtual core model is then run to obtain simulated oil recovery curves and simulated pressure response data. The simulated oil recovery curves and pressure response data are then compared with the experimental oil recovery curves and pressure responses from the core displacement experiment data. If the deviation of the oil recovery curve exceeds a preset threshold (e.g., 3%) or the pressure response error exceeds a preset threshold (e.g., 5%), the key physical property parameters in the model (such as the bound water saturation of the relative permeability curve, Corey index, etc.) are adjusted, and the model is rerun for data comparison. This process is iterated repeatedly until the errors between the simulation results and the experimental data both meet the threshold requirements (oil recovery evaluation index error < 3%, pressure field distribution evaluation index average relative error < 5%).

[0052] Step 105: Based on the calibrated three-dimensional reservoir numerical model, simulate the displacement process under different emulsion polymer dosage schemes, obtain development effect evaluation indicators, and determine the economically optimal emulsion polymer dosage based on the evaluation indicators and cost parameters.

[0053] Optionally, this step includes: based on the calibrated three-dimensional reservoir numerical model, first setting multiple different emulsion polymer dosage schemes (e.g., 600 to 1400 mg / L·PV, fixing basic parameters such as emulsion polymer injection concentration and injection rate), running the model to simulate the displacement process under each scheme, and obtaining development effect evaluation indicators. These indicators include: a recovery rate evaluation indicator reflecting the efficiency of recovery improvement (calculated based on the change in recovery rate improvement value corresponding to different dosages) and a heterogeneity evaluation indicator characterizing the spatial heterogeneity of the reservoir pressure field (calculated based on the standard deviation of pressure values ​​in each grid cell). After this, combining cost parameters (polymer unit price, labor and energy costs, etc.) and geological reserves and crude oil prices, calculating the net present value (NPV) of each dosage scheme over its entire life cycle, and identifying the inflection point where the NPV increment from a positive to a negative value is caused by a unit increase in dosage through marginal analysis. The dosage corresponding to this inflection point is the economically optimal dosage.

[0054] Based on the above technical solutions, the three-dimensional reservoir numerical model constructed in this application adopts a combination of hard and soft data to make the geological basis of the model more consistent with the actual reservoir. The calculation of geological feature complexity is combined with mesh optimization, enabling the model to accurately reflect the heterogeneous characteristics of the reservoir and avoiding the limitations of uniform meshes in highlighting key areas. Simultaneously, degradation kinetics are integrated to characterize the dynamic changes of emulsion polymers in the reservoir environment, reducing prediction bias caused by neglecting degradation effects. Experimental calibration is then used to further reduce model uncertainty and improve simulation reliability. The economically optimal dosage is determined through simulation of multiple dosage schemes. Based on this, the numerical simulation method for oilfield emulsion polymer dosage provided in this application can accurately determine the precise dosage of salt-resistant emulsion polymers during oil displacement, solving the problems of traditional dosage designs either failing to achieve the desired oil displacement effect or incurring excessive costs.

[0055] like Figure 2 As shown, in one possible implementation, step 102 above, which calculates the geological feature complexity of each three-dimensional data volume within the target reservoir area based on core displacement experimental data and geological static data, specifically includes:

[0056] Step 201: Perform spatial interpolation correction on the soft data based on the hard data to obtain the corrected three-dimensional data volume.

[0057] Optionally, considering that the geological static data in the 3D data volume of the target area block acquired through seismic measurement is soft data and may contain certain errors, while the geological static data actually measured in the well is hard data, this application uses geological interpolation algorithms (e.g., co-kriging algorithm, stochastic simulation algorithm, etc.) to correct the soft data in the 3D data volume. Taking the co-kriging algorithm as an example, the process of correcting the soft data in the 3D data volume includes: taking the coordinate information of the target area block and the latitude and longitude data of each well as input, aligning the coordinates using a Geographic Information System (GIS) projected coordinate system (e.g., Universal Transverse Mercator (UTM) coordinate system), and transforming the two to the same spatial coordinate system; then taking the spatially aligned 3D data volume and the geological static data of all monitoring points in the well as input, and correcting the soft data in the 3D data volume using the co-kriging algorithm.

[0058] Step 202: Perform geological feature clustering on the corrected 3D data volume based on the preset clustering algorithm.

[0059] As one possible implementation, since the geological static data of different rock layers varies significantly with increasing depth, this application employs clustering algorithms (e.g., K-means clustering, DBSCAN clustering, etc.) to cluster the spatially aligned 3D data volumes. Taking the DBSCAN clustering algorithm for each 3D data volume as an example: the position coordinates of each 3D data volume are normalized to the maximum-minimum value of the geological static data (e.g., porosity). The normalized position coordinates of each 3D data volume are then concatenated with the geological static data (e.g., porosity) to form a feature vector. The Euclidean distance between the feature vectors of two different 3D data volumes is calculated and used as the DBSCAN metric distance. All 3D data volumes are then clustered, with each cluster containing 3D data volumes from different rock layers that share the same geological characteristics.

[0060] Step 203: Based on the core displacement experiment data and the geological static data of each three-dimensional data volume within the cluster where the core is located, calculate the degree of difference in geological characteristics between each three-dimensional data volume and the core.

[0061] As one possible implementation, this step can be specifically implemented as follows: For the target geological static data, a graph structure is constructed using each 3D data volume within a cluster as a node and the difference in target geological static data values ​​between nodes as the edge weights; the target geological static data is one of several types of geological static data; the first topological complexity feature value of the graph structure is calculated; the target geological static data corresponding to each node is sequentially set to zero, and the second topological complexity feature value of the graph structure is recalculated; based on the difference between each second topological complexity feature value and the first topological complexity feature value, the change in the topological complexity feature value of each node is determined; based on the change in the topological complexity feature value of each node, the contribution of each node to the spatial structure of the target geological static data is determined; the difference between the contribution of each 3D data volume node and the contribution between the core nodes is calculated to obtain the difference component of the target geological static data; based on the difference component of each geological static data, the geological feature difference is determined.

[0062] The above process can be implemented through the following steps: Step 1: Using each 3D data volume within the cluster where the core is located as a node, construct an undirected graph with the difference in the target geological static data (porosity) value between two nodes (3D data volumes) as the edge weight. The difference in porosity value between nodes is expressed as the reciprocal of the Euclidean distance between the porosity values ​​of the two 3D data volumes; that is, the smaller the Euclidean distance between the porosity values ​​of the two 3D data volumes, the larger the edge weight, indicating that the porosity characteristics of the two are more similar.

[0063] Step 2: Using the undirected graph constructed above as input, set the number of iterations (for example, the value range is [50, 300], and the empirical value is 200), and use the Louvain algorithm to analyze the undirected graph to obtain the topological complexity corresponding to the three-dimensional porosity data within the cluster, which is also the first topological complexity feature value.

[0064] Step 3: Sequentially set the target geological static data (porosity) corresponding to each node (3D data volume) to zero, keeping other parameters unchanged. Reconstruct the undirected graph as in Step 1, and recalculate the topological complexity using the method in Step 2 (same number of iterations, Louvain algorithm), which is the second topological complexity feature value after setting the nodes to zero. Calculate the absolute value of the difference between the second and first topological complexity feature values. This absolute value is the change in the topological complexity feature value of the i-th node (3D data volume) relative to the spatial structure of the target geological static data (porosity).

[0065] Step 4: Directly use the change in the topological complexity feature value of the i-th node as the contribution of that node (3D data volume) to the spatial structure of the target geological static data (porosity). The larger the change, the more significant the impact of the node's porosity data on the spatial structure of porosity within the cluster.

[0066] Step 5: Extract the contribution of the 3D data volume (core node) corresponding to the core location to the spatial structure of the target geological static data (porosity). Calculate the absolute value of the difference between the contribution of the i-th 3D data volume node and the contribution of the core node. This absolute value of the difference is the difference component of the i-th 3D data volume under the target geological static data of porosity (denoted as...). , where k represents the number of geological static data such as porosity.

[0067] For each type of geological static data such as permeability and saturation, the corresponding difference component is calculated based on steps 1-5 above; assuming the number of types of geological static data is N, the mean of all difference components of the i-th three-dimensional data volume is calculated.

[0068] As an example, the mean of all dissimilarity components of the i-th 3D data volume Satisfy the following formula

[0069]

[0070] in, This indicates the number of types of geological static data. Indicates the first Various types of static geological data, Represents the i-th three-dimensional data volume. The static data weights of geological static data. This mean. This refers to the degree of difference in geological features between the i-th three-dimensional data volume and the core sample; The larger the value, the more significant the difference between the three-dimensional data volume and the core sample in terms of geological static characteristics; The smaller the value, the more similar the static geological characteristics of the two.

[0071] Step 204: Calculate the geological feature complexity of each three-dimensional data volume based on the geological feature difference degree and the core displacement characteristics determined from the core displacement experiment data.

[0072] As one possible implementation, this step includes: determining the core displacement characteristics based on core displacement experimental data, specifically including: reading the core's location information and matching it with its corresponding geological static data (such as porosity and permeability) in a spatial coordinate system; and simultaneously extracting the core experimental parameters, including the core's oil displacement efficiency. (Obtained directly from the experimental report), median emulsion particle size, and emulsification viscosity curve. Based on the porosity and permeability of the core location, the shear rate of the core region was calculated using Darcy's formula. Based on this shear rate, the corresponding effective viscosity was extracted from the emulsification viscosity curve of the core, denoted as [missing information]. Calculate the displacement characteristics of the core.

[0073] As an example, the core displacement characteristics satisfy the following formula:

[0074]

[0075] This indicates the oil displacement status of the core sample. This indicates the oil displacement efficiency of the core sample. Indicates the effective viscosity of the core. This represents the ratio of the pore throat diameter of the core to the median emulsion grain size (i.e., the pore throat to grain size ratio). This indicates min-max normalization.

[0076] Following this, based on the core displacement characteristics The mean of the difference components calculated in step 203 above. Calculate the geological feature complexity of each 3D data volume.

[0077] As an example, the geological feature complexity of the i-th 3D data volume satisfies The following formula:

[0078]

[0079] in, This represents the geological feature complexity of the i-th 3D data volume in the cluster corresponding to the core location. This indicates the oil displacement status of the core sample. Let represent the hyperbolic tangent function. It should be noted that when... When the value is 0, it indicates that the 3D data volume is completely consistent with the geological features of the core, and the geological feature complexity is the highest. The value is directly assigned as 1.

[0080] In the above calculation formula, This represents the mean of the feature differences of the i-th 3D data volume. After setting the geological static data of the i-th 3D data volume to zero, the greater the change in the graph's topology and the greater the change in complexity, indicating a greater contribution from the geological static data of the i-th 3D data volume. Conversely, the greater the difference in contribution between the geological static data of the i-th 3D data volume and the corresponding 3D data volume, the greater the difference between the rocks and oil-displaceable rocks within the i-th 3D data volume. Therefore, the complexity of the i-th 3D data volume should be relatively low. The bigger The smaller. This is a traditional nonlinear formula. This can highlight the difference between the i-th 3D data volume and the 3D data volume corresponding to the core location, and use the difference to determine the correlation between the i-th 3D data volume and the oil-displaceable location. Since they are inversely proportional, this application uses a nonlinear exponential weighting method to construct the formula.

[0081] Based on the above technical solutions, this application employs spatial interpolation correction of soft data using hard data, geological feature clustering of 3D data volumes, calculation of geological feature difference, and calculation of geological feature complexity in conjunction with core data. Specifically, the correction of soft data using hard data effectively reduces errors in geophysical exploration soft data, improves the accuracy of basic data, and avoids distortion in subsequent model construction due to data bias. Geological feature clustering of 3D data volumes groups data with similar geological attributes into one category, making subsequent complexity calculations more targeted and avoiding inaccurate feature quantification caused by the mixing of different geological feature data. The calculation of geological feature difference accurately identifies the correlation of geological features in different regions by quantifying the geological differences between each 3D data volume and the core data. Combined with core data, the geological feature complexity is calculated, allowing the complexity results to better reflect the geological criticality of each region in the actual reservoir, providing accurate quantitative basis for subsequent grid optimization and reducing model bias caused by unclear geological feature representation.

[0082] like Figure 2 As shown, in one possible implementation, the process of optimizing the mesh generation of the three-dimensional reservoir model based on the geological feature complexity in step 102 specifically includes:

[0083] Step 205: Based on the complexity of geological features, dynamically adjust the subdivision level of the corresponding 3D data volume region in the octree mesh partitioning algorithm.

[0084] Optionally, this application is based on Adjust the subdivision level of the octree mesh optimization algorithm for the i-th 3D data volume. As an example, the subdivision level of the i-th 3D data volume... Satisfy the following formula:

[0085]

[0086] This represents the subdivision level of the octree mesh optimization algorithm for the i-th 3D data volume. This represents the complexity of the i-th 3D data volume in the cluster corresponding to the core location. This represents the maximum subdivision level of the octree grid optimization algorithm (empirical value is 5). This indicates rounding up to the nearest integer.

[0087] It should be noted that the three-dimensional data volume of the target area obtained by seismic measurement is itself a regular three-dimensional grid structure. Each cluster is composed of three-dimensional data volumes of different rock layers. However, the rock layers that can be used for core flooding experiments are usually located in a few specific locations. Therefore, there may be a situation where there are no three-dimensional data volumes in a certain cluster that can be used for core experiments. In this case, the geological feature complexity of all three-dimensional data volumes in the cluster is 0, and no further mesh subdivision is performed. Therefore, the minimum subdivision level of the octree mesh optimization algorithm is 0.

[0088] Step 206: Generate a non-uniform optimized mesh structure based on the adjusted subdivision level.

[0089] Specifically, the geological feature complexity of all 3D data volumes is calculated, and the subdivision level in the octree mesh optimization algorithm is dynamically adjusted based on the calculation results of the geological feature complexity of all 3D data volumes to achieve adaptive 3D mesh optimization.

[0090] Therefore, traditional uniform meshes often suffer from problems such as insufficient simulation accuracy due to overall coarseness failing to reflect local geological features in key areas, or computational complexity and inefficiency due to fine meshes across the entire region. This application addresses this by linking geological feature complexity with mesh refinement levels. This allows for finer meshing in areas with complex geological features and critical displacement simulation (such as 3D data volumes near cores with strong geological heterogeneity), accurately capturing local geological features and ensuring simulation accuracy in key areas. Simultaneously, relatively coarse meshes are used in areas with simple geological features to avoid wasting computational resources. The non-uniform optimized mesh ensures that the model accurately reflects the actual heterogeneity of the reservoir, providing a high-quality mesh foundation for subsequent simulation calculations.

[0091] like Figure 3As shown, in one possible implementation, the process of constructing an optimized mesh generation and incorporating geological heterogeneity in step 103 above, based on the degradation kinetics of the emulsion polymer, can be specifically achieved through the following steps:

[0092] Step 301: Determine the degradation kinetic parameters of the emulsion polymer based on the data of the emulsion polymer concentration decaying over time.

[0093] The data on the decay of emulsion polymer concentration over time were obtained based on core displacement experiments. For example, the degradation kinetic parameters of the emulsion polymer include: degradation rate constant, pre-exponential factor, and activation energy.

[0094] Optionally, the process of determining the degradation kinetic parameters of the emulsion polymer includes: based on the data of emulsion polymer concentration versus time at different temperatures obtained from core displacement experiments, using the emulsion polymer concentration-time data at each temperature as input, fitting the first-order degradation kinetic equation to obtain the degradation rate constant at each temperature.

[0095] By substituting the temperature-degradation rate constant data into the Arrhenius equation, the pre-exponential factor and activation energy are determined through fitting calculations, and finally a fitting formula with well-defined parameters is obtained. This formula is used to quantitatively reflect the relationship between the degradation rate constant and temperature in the Arrhenius equation, that is, the corresponding degradation rate constant can be calculated if the temperature is known.

[0096] Step 302: Couple the degradation kinetic parameters with the temperature field in the three-dimensional reservoir numerical model to obtain the degradation kinetic relationship.

[0097] Among them, the degradation kinetics relationship is used to dynamically characterize the effective viscosity change of emulsion polymers under reservoir conditions.

[0098] Specifically, based on the well-defined fitting formula for the parameters obtained by fitting calculation to determine the pre-exponential factor and activation energy, and combined with the temperature field data of each grid unit in the three-dimensional reservoir model (different regions have different temperatures), the degradation rate of emulsion polymers at different locations in the reservoir is dynamically calculated, thereby realizing the dynamic coupling between the degradation rate of emulsion polymers and the temperature field.

[0099] Step 303: Input the optimized mesh generation, geological static data, and coupled degradation kinetics into the numerical simulation software to construct a three-dimensional reservoir numerical model.

[0100] Specifically, a chemical reaction module is embedded in the simulator of Chemeor numerical simulation software. The relationship between the degradation rate of the emulsion polymer and the temperature field is assigned as a grid attribute. The effective viscosity of each grid cell under the local temperature field is calculated in real time through a double-precision solver. At the same time, the degradation-related parameters are continuously corrected by dynamically tracking the evolution of the viscosity field under different emulsion polymer injection concentration gradients and combining the viscosity monitoring data of the produced liquid.

[0101] The optimized grid structure, geological static data of the target block, effective viscosity and other data are input into the Chemeor numerical simulation software to construct a three-dimensional reservoir numerical model.

[0102] Based on the above technical solution, this application determines the degradation kinetic parameters of emulsion polymers based on core displacement experimental data, couples these parameters with a temperature field to obtain the degradation kinetic relationship, and constructs a three-dimensional reservoir numerical model by combining optimized mesh and geological static data, effectively improving the model's characterization accuracy of the emulsion polymer displacement process. Specifically, the degradation kinetic parameters are obtained from core experiments, ensuring that the parameters accurately reflect the degradation law of emulsion polymers under actual reservoir conditions, avoiding the problem of theoretical parameters being out of sync with reality. Coupled with the degradation kinetic parameters and temperature field, the degradation rate of emulsion polymers can be dynamically reflected in different temperature regions of the reservoir, thus accurately characterizing the dynamic changes in the effective viscosity of the polymer, solving the problem of traditional models ignoring the influence of temperature on polymer degradation and causing inaccurate viscosity predictions. Combined with optimized mesh and comprehensive geological static data, the constructed three-dimensional reservoir numerical model can reflect both the geological heterogeneity of the reservoir and accurately reflect the dynamic characteristics of emulsion polymers in the reservoir, making the model closer to the actual displacement process.

[0103] like Figure 4 As shown, in one possible implementation, the core displacement experimental data includes the experimental recovery curve and experimental pressure response; the process of performing history fitting calibration of the three-dimensional reservoir numerical model based on the core displacement experimental data in step 104 above can be specifically implemented through the following steps:

[0104] Step 401: Establish the initial numerical model.

[0105] The initial numerical model was consistent with the conditions used when obtaining the core displacement experimental data.

[0106] Step 402: Run the initial numerical model to obtain the simulated oil recovery curve and simulated pressure response.

[0107] Specifically, in the Chemeor software, a virtual core model is established that is completely identical to the experimental core in terms of geometry and initial state; the virtual core model with the initial parameter configuration is then run in the Chemeor software to extract the simulated recovery curve and pressure response data.

[0108] Step 403: Compare the simulated recovery rate curve and simulated pressure response with the experimental recovery rate curve and experimental pressure response, respectively.

[0109] Step 404: When the deviation between the simulated recovery curve and the experimental recovery curve exceeds the first preset threshold, or the deviation between the simulated pressure response and the experimental pressure response exceeds the second preset threshold, adjust at least one physical property parameter in the initial numerical model and rerun the model for comparison until all deviations are less than their corresponding preset thresholds.

[0110] Specifically, the simulated recovery rate curve and pressure response data are compared with the recovery rate data and pressure data from the core displacement experiment, respectively: when the deviation between the simulated recovery rate curve and the experimental recovery rate data is less than 3%, and the error between the simulated pressure response data and the experimental pressure data is less than 5%, the model parameters are not adjusted.

[0111] When the deviation between the simulated recovery curve and the experimental recovery data exceeds 3%, or the error between the simulated pressure response data and the experimental pressure data exceeds 5%, adjust the bound water saturation and Corey index of the relative permeability curve in the model. For bound water saturation, make a slight upward adjustment of 0.02-0.05 units each time, with an empirical value of 0.03 units; for Corey index, make a slight upward adjustment of 0.01-0.03 units each time, with an empirical value of 0.015 units.

[0112] After each parameter adjustment, the virtual core model simulation is run again until the error between the simulation results and the recovery rate evaluation index of the core displacement experimental data is less than 3%, and the average relative error of the pressure field distribution evaluation index is less than 5%.

[0113] This process uses core displacement experimental data as the model calibration benchmark and achieves model optimization through reverse engineering: that is, by iteratively adjusting parameters such as the relative permeability curve (including bound water saturation and Corey index), emulsion viscosity increase rate, and resistance coefficient in the model, all the results of the numerical simulation output, such as recovery rate and pressure change, are highly consistent with the core displacement experimental curve.

[0114] Based on the above technical solution, this application constructs an initial numerical model consistent with core experimental conditions, compares the deviations between simulated and experimental data, and adjusts physical property parameters until the deviations are less than a preset threshold, thereby achieving accurate calibration of the three-dimensional reservoir numerical model and significantly reducing model uncertainty. Specifically, the initial model is completely consistent with the core experimental conditions, ensuring a unified benchmark between simulation and experiment, and avoiding calibration errors caused by differences in initial conditions. Direct comparison between simulated and experimental data accurately identifies the direction and degree of deviation between model parameters (such as parameters related to the relative permeability curve) and actual conditions. Targeted adjustment of physical property parameters and iterative calibration until the recovery rate and pressure response deviations both meet the standards allows for model correction at the core indicators of displacement effect (recovery rate and pressure), ensuring that the calibrated model output results highly match the actual core displacement experiments. Through this calibration process, the model can effectively avoid the problem of distorted displacement process predictions caused by inaccurate parameters, improving the model's reliability and generalization ability.

[0115] like Figure 5 As shown, in one possible implementation, the development effect evaluation indicators include: recovery rate evaluation indicators and heterogeneity evaluation indicators; the process of obtaining the development effect evaluation indicators in step 105 above, based on the calibrated three-dimensional reservoir numerical model, simulating the displacement process under different emulsion polymer dosage schemes, can be specifically implemented through the following steps:

[0116] Step 501: Obtain the cumulative recovery rate and reservoir pressure field distribution data under different emulsion polymer dosage schemes.

[0117] As a specific implementation method, for the emulsion polymer dosage scheme, this embodiment sets the polymer dosage to 600, 800, 1000, 1200, and 1400 mg / L·PV, respectively, with an emulsion polymer injection concentration of 900 mg / L and an injection rate of 0.14 PV / a. The improved oil recovery rate is simulated for different polymer dosages to determine the appropriate polymer dosage. According to the numerical simulation results, under the condition of a polymer concentration of 900 mg / L, the improvement in oil recovery increases with increasing polymer dosage.

[0118] As an example, Figure 6 This diagram illustrates the relationship between the amount of emulsion polymer used and the increase in oil recovery rate, as provided in the embodiments of this application. Figure 6As shown, when the emulsion polymer concentration is 900 mg / L, increasing the polymer dosage from 600 to 800 mg / L·PV increases the recovery rate by 1.30 percentage points; increasing the polymer dosage from 800 to 1000 mg / L·PV increases the recovery rate by 0.96 percentage points; increasing the polymer dosage from 1000 to 1200 mg / L·PV increases the recovery rate by 0.74 percentage points; and increasing the polymer dosage from 1200 to 1400 mg / L·PV increases the recovery rate by 0.56 percentage points.

[0119] Step 502: Based on the sequence of cumulative recovery rate improvement values ​​changing with usage, calculate the recovery rate evaluation index characterizing the efficiency of recovery rate improvement.

[0120] As an example, the recovery rate evaluation index X satisfies the following formula:

[0121]

[0122] in, and , and Let A represent the polymer dosage and recovery rate increase at iterations a and a+1, respectively, and let A represent the number of times the polymer dosage was increased. It should be noted that in this application, the polymer dosage increases progressively with each iteration. The value is always greater than 0.

[0123] Step 503: Based on the reservoir pressure field distribution data, calculate the heterogeneity evaluation index that characterizes the degree of spatial heterogeneity of the pressure field.

[0124] Specifically, in the output control module of the Chemeor software, the grid cell pressure output function is enabled to output a three-dimensional pressure field snapshot throughout the entire injection cycle (e.g., before injection, at 0.5 PV injection, and at 1 PV injection). The pressure field calculation results are then loaded through Chemeor's post-processing module. Planar color patches are used to observe the pressure distribution characteristics on the reservoir plane at different times; that is, pressure propagation speed is usually faster and pressure changes are more pronounced in high-permeability zones. Profile diagrams are used to analyze the vertical pressure distribution in the reservoir to assess whether the fluid flow has changed direction. Comparing the pressure fields of different polymer dosage schemes reveals that polymer injection changes the reservoir fluid flow resistance, thus affecting the pressure field distribution: as the polymer dosage increases, the high-pressure zone usually expands, and the pressure field distribution becomes more uniform. This change helps drive crude oil to flow from low-permeability areas to production wells. Therefore, the standard deviation of the pressure values ​​of all grid cells in the three-dimensional reservoir numerical model is used as an evaluation index of the degree of heterogeneity of the pressure field under the corresponding polymer dosage scheme.

[0125] like Figure 7As shown, in one possible implementation, the evaluation indicators for development effectiveness include: recovery rate evaluation indicators and heterogeneity evaluation indicators; the process of determining the economically optimal emulsion polymer dosage based on the evaluation indicators and cost parameters in step 105 above can be specifically implemented through the following steps:

[0126] Step 701: Based on the recovery rate evaluation index corresponding to different emulsion polymer dosage schemes, and combined with geological reserves and crude oil prices, calculate the crude oil production increase revenue of each emulsion polymer dosage scheme.

[0127] Step 702: Calculate the total input cost of each usage plan based on the total polymer usage, polymer unit price, and injection operation cost corresponding to each usage plan.

[0128] Step 703: Calculate the net present value of each usage plan based on the revenue from increased crude oil production and the total input cost.

[0129] Step 704: Determine the inflection point in the curve of net present value changing with usage where the marginal net present value turns from positive to negative, and determine the emulsion polymer usage corresponding to the inflection point as the economically optimal emulsion polymer usage.

[0130] Specifically, by first running different polymer dosage schemes (e.g., 700 to 1400 mg / L·PV), a quantitative relationship between dosage and recovery rate is established. Then, based on this relationship, the net present value (NPV) of each dosage scheme over its entire life cycle is calculated. The formula is: NPV = ((Recovery rate - Benchmark recovery rate) × Geological reserves × Crude oil price) - (Polymer dosage × Polymer price) - (Labor and energy costs). The NPV-dosage relationship curve is plotted using the above calculation results. Marginal analysis is used to identify key inflection points: when the marginal NPV (i.e., the increase in NPV resulting from a unit increase in polymer dosage, such as the change in NPV corresponding to a 200 mg / L·PV increase in dosage) turns from positive to negative, the corresponding dosage is the economically optimal dosage.

[0131] Optional, such as Figure 7 As shown, after determining the economically optimal emulsion polymer dosage, the emulsion polymer dosage can be dynamically adjusted, specifically including:

[0132] Step 705: Based on the economically optimal emulsion polymer dosage, generate injection control commands to control the multi-well injection equipment to perform emulsion polymer injection.

[0133] Step 706: Monitor the polymer injection concentration and instantaneous injection flow rate of each injection well in real time.

[0134] Step 707: In response to the deviation of the monitored polymer injection concentration or instantaneous injection flow rate from the target set value exceeding the allowable threshold, an adjustment command is generated to dynamically adjust the operating parameters of the multi-well injection equipment, thereby achieving closed-loop control of the injection parameters.

[0135] Specifically, during the injection implementation phase, a multi-well online injection skid system enables simultaneous injection of emulsion polymers into three wells. First, the central control platform sets the target injection concentration and flow rate for each well based on the economically optimal dosage (the target injection volume is determined by the economically optimal dosage, the effective reservoir volume of the corresponding well, and the injection cycle). Then, an integrated programmable logic controller (PLC) controls the stroke frequency of the three metering pumps in real time to ensure that the injection parameters of each well meet the set requirements. Using the online density meter and electromagnetic flowmeter equipped in the skid system, the actual concentration and instantaneous flow rate of the emulsion polymer in each well's branch are continuously monitored, and the monitoring data is transmitted to the central monitoring system in real time. When the actual concentration or instantaneous flow rate deviates from the set value by more than ±5%, the system automatically triggers a dynamic adjustment mechanism: the stroke frequency of the corresponding metering pump is corrected in real time through a PID control algorithm, and the mother liquor preparation process is automatically started and stopped based on the liquid level sensor data of the mother liquor storage tank to ensure a stable mother liquor supply. Furthermore, a supervisory control and data monitoring system is used to further control the flow rate. The Acquisition (SCADA) system generates daily injection reports, enabling real-time visual monitoring and closed-loop management of injection dynamics in the three wells, ensuring that actual injection parameters are always maintained within the optimal economic dosage range.

[0136] This application also provides a numerical simulation system for the dosage of oilfield emulsion polymers, such as... Figure 8 As shown, the system includes:

[0137] The acquisition unit 801 is used to acquire geological static data and core displacement test data of the target oil reservoir area. The geological static data includes a three-dimensional data volume composed of hard data based on wellbore measurements and soft data based on geophysical exploration.

[0138] The processing unit 802 is used to calculate the geological feature complexity of each three-dimensional data volume in the target reservoir area based on core displacement experimental data and geological static data, and optimize the mesh division of the three-dimensional reservoir model according to the geological feature complexity.

[0139] The processing unit 802 is also used to construct a three-dimensional reservoir numerical model with optimized mesh generation and incorporating geological heterogeneity based on the degradation kinetics of emulsion polymers.

[0140] The processing unit 802 is also used to perform historical fitting calibration of the three-dimensional reservoir numerical model based on core displacement experimental data.

[0141] The processing unit 802 is also used to simulate the displacement process under different emulsion polymer dosage schemes based on the calibrated three-dimensional reservoir numerical model, obtain development effect evaluation indicators, and determine the economically optimal emulsion polymer dosage based on the evaluation indicators and cost parameters.

[0142] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0143] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A numerical simulation method for the dosage of oilfield emulsion polymers, characterized in that, The method includes: Acquire geological static data and core displacement test data of the target oil reservoir area, wherein the geological static data includes a three-dimensional data volume composed of hard data based on wellbore measurements and soft data obtained based on geophysical exploration; Based on the core displacement experimental data and the geological static data, the geological feature complexity of each three-dimensional data volume in the target reservoir area is calculated, and the mesh division of the three-dimensional reservoir model is optimized according to the geological feature complexity. Based on the degradation kinetics of emulsion polymers, a three-dimensional reservoir numerical model with optimized mesh generation and incorporating geological heterogeneity is constructed. The three-dimensional reservoir numerical model was historically fitted and calibrated based on the core displacement experimental data. Based on the calibrated three-dimensional reservoir numerical model, the displacement process under different emulsion polymer dosage schemes was simulated to obtain development effect evaluation indicators, and the economically optimal emulsion polymer dosage was determined based on the evaluation indicators and cost parameters. The method for obtaining the geological feature complexity is as follows: spatial interpolation correction is performed on the soft data based on the hard data to obtain a corrected three-dimensional data volume; geological feature clustering is performed on the corrected three-dimensional data volume based on a preset clustering algorithm; the geological feature difference degree between each three-dimensional data volume and the core is calculated based on the core displacement experimental data and the geological static data of each three-dimensional data volume in the cluster where the core is located; the geological feature complexity of each three-dimensional data volume is calculated based on the geological feature difference degree and the core displacement features determined from the core displacement experimental data.

2. The numerical simulation method for oilfield emulsion polymer usage according to claim 1, characterized in that, The mesh generation of the three-dimensional reservoir model is optimized based on the complexity of the geological features, including: Based on the complexity of the geological features, the subdivision level of the corresponding three-dimensional data volume region in the octree mesh partitioning algorithm is dynamically adjusted. Based on the adjusted subdivision level, a non-uniform optimized mesh structure is generated.

3. The numerical simulation method for oilfield emulsion polymer usage according to claim 1, characterized in that, Based on the core displacement experimental data and the geological static data of each 3D data volume within the core cluster, the geological characteristic difference between each 3D data volume and the core is calculated, including: For the target geological static data, a graph structure is constructed using each three-dimensional data volume within a cluster as a node and the difference in the target geological static data values ​​between nodes as the edge weight; the target geological static data is one type of geological static data. Calculate the first topological complexity eigenvalue of the graph structure; The target geological static data corresponding to each node is sequentially set to zero, and the second topological complexity feature value of the graph structure is recalculated. Based on the difference between each second topological complexity feature value and the first topological complexity feature value, the change in the topological complexity feature value of each node is determined. Based on the change in the topological complexity eigenvalue of each node, the contribution of each node to the spatial structure of the target geological static data is determined. The difference between the contribution of each three-dimensional data volume node and the contribution between the core nodes is calculated to obtain the difference component of the target geological static data. The degree of difference of the geological features is determined based on the degree of difference component of each geological static data.

4. The numerical simulation method for oilfield emulsion polymer usage according to claim 1, characterized in that, Based on the degradation kinetics of emulsion polymers, a three-dimensional reservoir numerical model with optimized mesh generation and incorporating geological heterogeneity was constructed, including: The degradation kinetic parameters of the emulsion polymer were determined based on the data of the emulsion polymer concentration decaying over time; the data of the emulsion polymer concentration decaying over time were obtained based on core displacement experiments. The degradation kinetic parameters are coupled with the temperature field in the three-dimensional reservoir numerical model to obtain the degradation kinetic relationship, which is used to dynamically characterize the effective viscosity change of the emulsion polymer under reservoir conditions. The optimized mesh division, the geological static data, and the coupled degradation kinetic relationship are input into numerical simulation software to construct the three-dimensional reservoir numerical model.

5. The numerical simulation method for oilfield emulsion polymer usage according to claim 1, characterized in that, The core displacement experiment data includes the experimental recovery curve and experimental pressure response. Based on the core displacement experimental data, the three-dimensional reservoir numerical model is subjected to history fitting calibration, including: An initial numerical model is established; the initial numerical model is consistent with the conditions under which the core displacement experimental data were obtained. Run the initial numerical model to obtain the simulated oil recovery curve and simulated pressure response; The simulated recovery rate curve and simulated pressure response are compared with the experimental recovery rate curve and the experimental pressure response, respectively. When the deviation between the simulated recovery curve and the experimental recovery curve exceeds a first preset threshold, or the deviation between the simulated pressure response and the experimental pressure response exceeds a second preset threshold, at least one physical property parameter in the initial numerical model is adjusted, and the model is rerun for comparison until all deviations are less than their corresponding preset thresholds.

6. The numerical simulation method for oilfield emulsion polymer usage according to claim 1, characterized in that, The evaluation indicators for development effectiveness include: recovery rate evaluation indicators and heterogeneity evaluation indicators; Based on the calibrated three-dimensional reservoir numerical model, the displacement process under different emulsion polymer dosage schemes was simulated to obtain development effect evaluation indicators, including: Obtain the cumulative recovery value and reservoir pressure field distribution data under different emulsion polymer dosage schemes; Based on the sequence of the cumulative recovery rate improvement value as a function of dosage, the recovery rate evaluation index characterizing the efficiency of the recovery rate improvement is calculated. Based on the reservoir pressure field distribution data, the heterogeneity evaluation index characterizing the degree of spatial heterogeneity of the pressure field is calculated.

7. The numerical simulation method for oilfield emulsion polymer usage according to claim 6, characterized in that, Determining the economically optimal emulsion polymer dosage based on the aforementioned evaluation indicators and cost parameters includes: Based on the recovery rate evaluation index corresponding to different emulsion polymer dosage schemes, and combined with geological reserves and crude oil prices, the crude oil production increase revenue of each emulsion polymer dosage scheme is calculated. Based on the total polymer usage, polymer unit price, and injection operation cost corresponding to each usage plan, calculate the total input cost of each usage plan. Calculate the net present value of each usage plan based on the crude oil production increase revenue and the total input cost; The inflection point where the marginal net present value changes from positive to negative in the curve of net present value versus usage is determined, and the amount of emulsion polymer used corresponding to the inflection point is determined as the economically optimal amount of emulsion polymer used.

8. The numerical simulation method for oilfield emulsion polymer usage according to claim 7, characterized in that, The method further includes: Based on the economically optimal emulsion polymer dosage, injection control commands are generated to control the multi-well injection equipment to perform emulsion polymer injection; Real-time monitoring of polymer injection concentration and instantaneous injection flow rate in each injection well; In response to the detected deviation of the polymer injection concentration or the instantaneous injection flow rate from the target set value exceeding the allowable threshold, an adjustment command is generated to dynamically adjust the operating parameters of the multi-well injection equipment, thereby achieving closed-loop control of the injection parameters.

9. A numerical simulation system for the dosage of oilfield emulsion polymers, characterized in that, The system includes: The acquisition unit is used to acquire geological static data and core displacement test data of the target oil reservoir area. The geological static data includes a three-dimensional data volume composed of hard data based on wellbore measurements and soft data based on geophysical exploration. The processing unit is used to calculate the geological feature complexity of each three-dimensional data volume in the target reservoir area based on the core displacement experimental data and the geological static data, and optimize the mesh division of the three-dimensional reservoir model according to the geological feature complexity. The method for obtaining the geological feature complexity is as follows: spatial interpolation correction is performed on the soft data based on the hard data to obtain a corrected three-dimensional data volume; geological feature clustering is performed on the corrected three-dimensional data volume based on a preset clustering algorithm; the geological feature difference degree between each three-dimensional data volume and the core is calculated based on the core displacement experimental data and the geological static data of each three-dimensional data volume in the cluster where the core is located; the geological feature complexity of each three-dimensional data volume is calculated based on the geological feature difference degree and the core displacement features determined from the core displacement experimental data. The processing unit is also used to construct a three-dimensional reservoir numerical model with optimized mesh division and geological heterogeneity based on the degradation kinetics of emulsion polymers. The processing unit is also used to perform historical fitting calibration of the three-dimensional reservoir numerical model based on the core displacement experimental data. The processing unit is also used to simulate the displacement process under different emulsion polymer dosage schemes based on the calibrated three-dimensional reservoir numerical model, obtain development effect evaluation indicators, and determine the economically optimal emulsion polymer dosage based on the evaluation indicators and cost parameters.

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