Three-dimensional time-varying grading evaluation method for development degree of dominant channel of water-drive sandstone reservoir

Abstract

The invention discloses a three-dimensional time-varying grading evaluation method for the development degree of a dominant channel of a water-drive sandstone reservoir. The method comprises the steps that (1) grading evaluation indexes of dominant channeling channels are screened; (2) calculating the weight of the grading evaluation index of the dominant channeling channel, and establishing a new dynamic evaluation index; (3) establishing a time-varying three-dimensional dominant channeling channel grading method; and (4) determining a grading standard by using the clustered contour coefficient, and the like. According to the method, a machine learning algorithm and characteristics of actual geological conditions of an oil field are combined, and classification of the time-varying dominant fluid channeling channels can be completed under the condition that actual development of an oil reservoir is met; the method has the advantages of being accurate in grading effect and high in stability in the aspect of grading of the dominant channeling channels of the water-driven sandstone, and has a certain guiding effect on grading regulation and control of the dominant channeling channels of the water-driven sandstone.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of oilfield development and enhanced oil recovery, and particularly relates to a three-dimensional time-varying grading evaluation method for the development degree of dominant channels in water-driven sandstone reservoirs. BACKGROUND

[0002] The dominant channel of a reservoir refers to a space for the ordered flow of underground fluid in a porous medium, and the fluid inside the channel is controlled by the flow field force, which continuously exchanges matter and energy conservation, and promotes the flow of fluid from a high-energy area to a low-energy area. After long-term water injection development, the capillary force between sand and gravel gradually disappears due to the continuous increase of water content, resulting in the increase of sand shear fracture and the increase of permeability of sand production site. And under the rapid scouring of injected water, the cementation inside the high-permeability strip is reduced, and the wettability of the rock surface is also converted to hydrophilic, and finally the dominant channeling channel is formed. The formation of the dominant channeling channel will cause the injected water of the water well to rush along the high-permeability strip in the channeling area, forming a dominant channeling with high-permeability flow velocity; the area, resulting in the poor water injection effect of the low-permeability strip, forming a dead oil area, and the efficiency of oilfield development is reduced.

[0003] The static index describes the geological basis for forming the dominant channel, which is the congenital condition for the formation of the dominant channel, and can indicate the potential of the formation of the dominant channel. The direct representation index of the dominant channel of the reservoir is the dynamic index, and the value reflects the actual scouring strength of the reservoir. With the continuous improvement of water drive degree, the physical properties of the reservoir will change obviously, which will affect the development of adjustment measures, so it is particularly important to establish a time-varying grading method for the dominant channeling channel. SUMMARY

[0004] The application is proposed to solve the problems in the prior art, and the purpose is to provide a three-dimensional time-varying grading evaluation method for the development degree of the dominant channeling channel in the water-driven sandstone reservoir.

[0005] The application is realized by the following technical scheme: A three-dimensional time-varying grading evaluation method for the development degree of the dominant channel in the water-driven sandstone reservoir, comprising the following steps: (I) screening static heterogeneity evaluation indexes for grading the dominant channeling channel; (II) calculating static comprehensive evaluation indexes for grading the dominant channeling channel; (III) establishing a new dynamic evaluation index; (IV) establishing a time-varying three-dimensional dominant channeling channel grading method; (V) determining the grading standard by using the contour coefficient of clustering.

[0006] In the above technical solution, the evaluation indicators for the superior crossflow channels include pore radius, permeability, permeability ratio and permeability variation coefficient.

[0007] In the above technical solution, the selection method for the graded evaluation index of the superior crossflow channel is the logical analysis method.

[0008] In the above technical solution, the calculation of the weight of the graded evaluation index of the dominant crossflow channel is specifically as follows: the weight of the graded evaluation index of different dominant crossflow channels is determined based on the AHP analytic hierarchy process, and the weight of the graded evaluation index of the dominant crossflow channel is obtained by the arithmetic mean method, the geometric mean method and the eigenvalue method respectively.

[0009] In the above technical solution, the new dynamic evaluation index is the dynamic oil-to-flow ratio.

[0010] In the above technical solution, the formula for calculating the dynamic oil-to-flow ratio is: ...(1) In the formula: D is the dynamic oil-to-flow ratio, in meters. -1 S o S represents oil saturation, dimensionless; oi F represents the original oil saturation, dimensionless; W The flux is the aqueous phase displacement flux, expressed in m³ / s. x The grid cross-sectional area is in the x-direction, in meters. 2 S y The grid cross-sectional area is in the y-direction, in meters (m²). 2 S z The grid cross-sectional area is in the z-direction, in meters. 2 Q wx The volume of water phase passing through the cross section per unit time in the x-direction is expressed in cubic meters (m³). 3 / s;Q wy The volume of water phase passing through the cross section per unit time in the y-direction is expressed in cubic meters (m³). 3 / s;Q wz The volume of water phase passing through the cross section per unit time in the z-direction is expressed in cubic meters (m³). 3 / s; t1 is the starting time step, dimensionless; t2 is the ending time step, dimensionless.

[0011] In the above technical solution, step (III) of establishing a time-varying three-dimensional dominant crossflow channel classification method specifically includes: (III-ⅰ) Linear normalization was used to normalize the static heterogeneity index and dynamic oil-to-flux ratio data respectively; (III-II) The fuzzy C-means clustering algorithm was used to classify the normalized dynamic oil-to-flow ratio and static heterogeneity index to determine the cluster centers of different dominant crossflow channel development levels.

[0012] In the above technical solution, step (IV) of determining the grading standard by using the contour coefficient of clustering specifically involves: drawing the contour coefficients under different numbers of clusters, determining the optimal number of clusters, and using the optimal number of clusters as the grading number to classify the dominant crossflow channels of the oilfield based on fuzzy C-means clustering.

[0013] In the above technical solution, the formula for calculating the silhouette coefficient of the clustering is: ... (8) In the formula: a i b is the average distance between a sample and other samples in its cluster; i is the minimum average distance between a sample and all samples in other clusters; n is the number of samples in the dataset; S k The number of clusters for the silhouette coefficient.

[0014] The beneficial effects of this invention are: This invention provides a three-dimensional time-varying classification and evaluation method for the development degree of dominant crossflow channels in water-driven sandstone reservoirs. Combining machine learning algorithms and the characteristics of actual geological conditions in the oilfield, it can complete the classification of time-varying dominant crossflow channels while meeting the actual development needs of the reservoir. The method of this invention has the advantages of accurate classification effect and strong stability in the classification of dominant crossflow channels in water-driven sandstone, and has a certain guiding role in the classification and control of dominant crossflow channels in water-driven sandstone. Attached Figure Description

[0015] Figure 1 This is a flowchart of the three-dimensional time-varying classification evaluation method for the development degree of dominant crossflow channels in water-driven sandstone reservoirs according to the present invention. Figure 2 This is a graph showing the silhouette coefficient calculation for different numbers of clusters in Embodiment 1 of the present invention. Figure 3 This is a distribution diagram of the clustering results in Embodiment 1 of the present invention; Figure 4 The diagram shows the classification results of dominant crossflow channels in the S area of ​​Q oilfield at different stages in Example 1 of this invention (a: undeveloped; b: early development; c: late development). Figure 5 The diagram shows the classification results of the dominant crossflow channels in the I1 well group of the S area of ​​the Q oilfield in this embodiment of the invention (a is the classification result of the dominant crossflow channels; b is the three-dimensional spatial distribution of the strongly developed crossflow channels; c is the streamline distribution).

[0016] For those skilled in the art, other related figures can be obtained from the above figures without any creative effort. Detailed Implementation

[0017] To enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0018] Example 1 like Figure 1 As shown, a three-dimensional time-varying classification and evaluation method for the development degree of dominant channeling in water-driven sandstone reservoirs includes the following steps: (I) Screening static heterogeneity evaluation indicators for classifying dominant crossflow channels The selection of static heterogeneity evaluation indicators for the classification of dominant crossflow channels is based on the following principles: ① Ensure that each indicator accurately represents the dominant channels of the reservoir; ② The indicators are highly independent; ③ Select data that can be obtained in large quantities and is relatively easy to obtain in production to establish indicators.

[0019] The comprehensive index screening method serves as the theoretical foundation of the reservoir dominant channel evaluation system, efficiently evaluating dominant channels in reservoirs during the high water-cut period and providing a basis for the subsequent classification of dominant channel development. Therefore, it is crucial to describe the development degree of flow dominance channels using the fewest and most optimal indices.

[0020] The evaluation index for dominant flow channels includes all dynamic evaluation indexes for oilfield production and static heterogeneity evaluation indexes. Static heterogeneity evaluation indexes can indicate the potential for the formation of dominant channels, and the heterogeneity and pore throat radius of sandstone reservoirs are important factors in the formation of dominant flow channels. Therefore, combining reservoir heterogeneity with permeability to judge the development level of dominant flow channels has a scientific basis. The static heterogeneity evaluation indexes used in this invention include pore radius, permeability, permeability ratio, and permeability variation coefficient.

[0021] The method for screening static heterogeneous evaluation indicators for classifying advantageous crossflow channels is the logical analysis method.

[0022] The logical analysis method specifically involves: first, analyzing the physical meaning of each indicator in the selected system, studying the degree of influence of the indicators on the dominant channels of the reservoir, and identifying which indicators are involved in the entire influence process, thereby determining the logical relationships between the indicators. The logical relationships between various indicators can be categorized as causal (one indicator is the cause of another; from the perspective of the interaction mechanism, the cause precedes the effect), equivalence (a completely equivalent relationship, which can also be understood as indicators of the same type), definitional relationship (the definitional relationship between indicator values ​​means that the definition of some indicators already implies some other indicators, thus eliminating duplicate indicators), and process relationship (some indicators always exist in the evaluation process), etc., and then selecting appropriate grading indicators.

[0023] (II) Calculate the static comprehensive evaluation index for the classification of dominant crossflow channels. (II-i) Calculate the weights of static heterogeneity evaluation indicators The weights of the static heterogeneity evaluation indexes for different dominant crossflow channels were determined based on the AHP analytic hierarchy process. The weights of the static heterogeneity evaluation indexes for different dominant crossflow channels were calculated by the arithmetic mean method, the geometric mean method, and the eigenvalue method, respectively. The average value of the weights of the static heterogeneity evaluation indexes for different dominant crossflow channels calculated by the three methods was taken as the final weights of the static heterogeneity evaluation indexes for different dominant crossflow channels. The static comprehensive evaluation index is based on the idea of ​​the Analytic Hierarchy Process (AHP), combined with the production practice of oilfields and the experience of reservoir engineers. The AHP is a multi-criteria decision-making method used to help people make trade-offs and choices in complex decision-making environments. The main thing is to construct a parameter judgment matrix, combine it with mathematical methods for qualitative analysis, and then solve the problem based on the weights calculated for each scheme. The qualitative description of parameter comparison is shown in Table 1.

[0024] Table 1: Definition of Scale of the Judgment Matrix in AHP (Analytic Hierarchy Process) (II-II) Calculate the static comprehensive evaluation index The static comprehensive evaluation index is obtained by multiplying each static heterogeneous evaluation index by its corresponding weight and then summing the results. (III) Establish new dynamic evaluation indicators The new dynamic evaluation index is the dynamic oil-to-flow ratio; The scouring intensity over any given time period is measured by the time-varying displacement flux, which is defined as the cumulative volume of fluid passing through a unit pore cross-sectional area over any given time period. Its expression is: ...(1) In the formula: F is the time-varying displacement flux, in meters; Q x The cumulative fluid volume in the x-direction, in meters (m). 3 Q y The cumulative fluid volume in the y-direction, in meters (m). 3 Q z The cumulative fluid volume in the z-direction is expressed in m³. 3 S x The grid cross-sectional area is in the x-direction, in meters. 2 S y The grid cross-sectional area is in the y-direction, in meters (m²). 2 S z The grid cross-sectional area is in the z-direction, in meters. 2 t1 is the starting time step, dimensionless; t2 is the ending time step, dimensionless. In practical applications of oilfield development, time-varying displacement flux can only qualitatively analyze the relative intensity distribution of reservoir scour, but cannot quantitatively delineate different crossflow regions, let alone characterize the development location and degree of dominant crossflow channels. Furthermore, time-varying displacement flux includes the combined scour intensity of the oil and water phases, while dominant crossflow channels are controlled by the scour effect of injected water. Therefore, this application, based on time-varying displacement flux, further proposes a water phase time-varying displacement flux. The expression for the water phase time-varying displacement flux is as follows: ...(2) In the formula: F W The flux is the aqueous phase displacement flux, expressed in m³ / s. x The grid cross-sectional area is in the x-direction, in meters. 2 S y The grid cross-sectional area is in the y-direction, in meters (m²). 2 S z The grid cross-sectional area is in the z-direction, in meters. 2 Q wx The volume of water phase passing through the cross section per unit time in the x-direction is expressed in cubic meters (m³). 3 / s;Q wy The volume of water phase passing through the cross section per unit time in the y-direction is expressed in cubic meters (m³). 3 / s;Q wz The volume of water phase passing through the cross section per unit time in the z-direction is expressed in cubic meters (m³). 3 / s; t1 is the start time step, dimensionless; t2 is the end time step, dimensionless; The time-varying displacement flux of the aqueous phase characterizes the dynamic degree of aqueous phase scouring, and the change in oil saturation characterizes the degree of oil phase displacement over a period of time. The time-varying displacement flux of the aqueous phase and the change in oil saturation are combined into a new evaluation index for dominant crossflow channels, namely the dynamic oil flux ratio, which is the change in grid oil saturation caused by the flow rate of water over a period of time. The expression for the dynamic oil-to-pass ratio is: ... (3) In the formula: D is the dynamic oil-to-flow ratio, in meters. -1 S o S represents oil saturation, dimensionless; oi F represents the original oil saturation, dimensionless; W The displacement flux in the aqueous phase is expressed in m³. (IV) Establishing a time-varying three-dimensional dominant flow channel classification method (Ⅳ-ⅰ) Linear normalization was used to normalize the static comprehensive evaluation index and the dynamic oil-to-flow ratio data respectively: Since the dynamic oil-to-pass ratio and the static comprehensive evaluation index have different dimensions, in order to eliminate the influence of the two dimensions and make the model more stable and converge faster, after removing the extreme values ​​in the sample that may affect the results, the membership function is used to normalize the data. The linear normalization method is used to normalize the data of the static comprehensive evaluation index and the dynamic oil-to-pass ratio respectively, mapping the data values ​​to the range [0,1]. The formula for linear normalization is: ... (4) (Ⅳ-ⅱ) The fuzzy C-means clustering algorithm is used to classify the normalized dynamic oil-to-flow ratio and static comprehensive evaluation index to determine the cluster centers of different dominant crossflow channel development levels; Classifying the obtained index values ​​involves common methods such as fuzzy comprehensive evaluation and cluster analysis. However, fuzzy comprehensive evaluation is heavily influenced by subjective factors in determining index boundaries and weights, which can affect the identification results. Cluster analysis is a common data analysis method used to group samples in a dataset based on their similarity. Common clustering methods include K-means clustering, hierarchical clustering, and DBSCAN clustering. K-means clustering is sensitive to the selection of initial cluster centers, while hierarchical clustering and DBSCAN clustering are not efficient for processing large datasets. Fuzzy C-means clustering offers higher objectivity compared to comprehensive evaluation, better robustness compared to other clustering methods, and more efficient data processing, significantly improving identification accuracy. The core idea of ​​the fuzzy C-means clustering algorithm is to find U and V such that the objective function J... m (U,V) is minimized; The specific steps of the fuzzy C-means clustering algorithm are as follows: (a) Given a sample set X = {x1, x2, ..., x...} n Determine the number of categories C (2≤C≤N), the fuzziness weighting exponent m, and a reasonably small iteration stopping threshold ε; (b) Set the initial membership matrix U (s) Let the number of iterations s = 0; (c) Calculate U (s) Cluster center v at time i (s) The cluster center v i (s) The calculation formula is: ... (5) In the formula: For the corresponding cluster centers, Indicates the first The sample belongs to the first Membership degree of a class The number of feature indicators, For the first The location of each sample The number of clusters, The number of samples; (d) Corrected membership matrix U (s) Calculate the objective function J m s : ... (6) In the formula: Represents element With cluster center The Euclidean distance between them Represents element With cluster center The Euclidean distance between them The number of feature indicators, The number of clusters, Indicates the first The sample belongs to the first Membership degree of a class; ... (7) In the formula: J m s The clustering objective function is... This is the membership matrix. As cluster center, Represents element With cluster center The Euclidean distance between them Indicates the first The sample belongs to the first Membership degree of a class The number of feature indicators, The number of clusters, The number of samples; (e) For a given threshold U > 0, if the objective function ||J|| m s -J m s-1 If ||≤ε, the algorithm terminates; otherwise, proceed to (c). (V) Determining grading criteria using the silhouette coefficient of clustering The contour coefficients under different numbers of clusters were plotted to determine the optimal number of clusters. The optimal number of clusters was used as the number of classifications. Based on fuzzy C-means clustering, the dominant crossflow channels of the oilfield were classified. The silhouette coefficient is an evaluation index of the density and dispersion of clusters, reflecting the quality of clustering. A silhouette coefficient closer to 1 indicates better clustering; a silhouette coefficient closer to -1 indicates unreasonable clustering. The silhouette coefficient of a certain clustering of the dataset can be derived from the silhouette coefficients of individual samples. The formula for calculating the silhouette coefficient of a certain clustering of the dataset is: ... (8) In the formula: a i b is the average distance between a sample and other samples in its cluster; i is the minimum average distance between a sample and all samples in other clusters; n is the number of samples in the dataset; S k The number of clusters for the silhouette coefficient.

[0025] To address the issues of inconsistent effectiveness and the inability to perform time-varying dominant flow channel classification in current reservoir classification methods, this paper utilizes machine learning and dimensionality reduction classification techniques to achieve time-varying dominant flow channel classification while meeting actual reservoir development conditions. The overall optimization process is as follows: Figure 1 .

[0026] Example 1 The Q oilfield, a test site, is a large, complex fluvial facies heavy oil field. It is a large, low-amplitude anticline structure with an average porosity of 32% and an average porosity of 2300 mD. The viscosity of the underground crude oil ranges from 22 to 260 mPa·s. Currently, the oilfield has entered a development stage characterized by high recovery rates and extremely high water cut. After long-term water injection development, the conflict between intra-layer and planar conditions has intensified, high water consumption zones are well-developed, and ineffective water circulation is a prominent issue, resulting in high water cut and low recovery rates. To facilitate targeted remediation strategies for ineffective circulation channels, it is necessary to classify the development level of dominant crossflow channels.

[0027] (I) First, based on the logical analysis method, all dynamic and static indicators of oilfield production are screened and appropriate classification indicators are selected. The selected static heterogeneous evaluation indicators include pore radius, permeability, permeability ratio, and permeability variation coefficient.

[0028] (II) Based on the specific production and geological characteristics of reservoir Q, the weights of different static heterogeneity evaluation indicators were determined using the AHP (Analytic Hierarchy Process) method. The weights of the three methods were calculated using the arithmetic mean method, geometric mean method, and eigenvalue method, respectively, as shown in Formula 9. Table 2 shows the results of the AHP judgment matrix. Table 2: Results of the AHP (Analytic Hierarchy Process) Judgment Matrix Subsequently, a dynamic evaluation index—the dynamic oil-to-pass ratio—was established based on the development characteristics of the research.

[0029] ... (9) (III) Based on the clustering results of fuzzy C-means, the target oilfield Q oilfield S block is classified, and the classification results are as follows: Figure 4 As shown in Table 2, the cluster centers for different dominant crossflow channel development levels are given.

[0030] Table 3: Results of Development Level of Dominant Flow Channels (IV) The silhouette coefficients for different numbers of clusters were plotted as follows: Figure 2 As shown, the optimal number of clusters was determined to be 4, and 4 was used as the number of levels. Based on fuzzy C-means clustering, the dominant crossflow channels of oilfield Q were classified, and the clustering results are as follows. Figure 3 As shown.

[0031] To determine the effectiveness of this classification method, well group I1 in this block was selected and validated based on tracer and streamline results measured in the oilfield. The classification results ( Figure 5 a) In the interconnected area between wells in the I1 well group, the overall dominant crossflow channels are highly developed. Among them, the connectivity between wells P3 and P1 and well I1 is relatively strong, mostly at level I (strongly developed area) and level II (weakly developed area). The connectivity between wells P2 and P4 and well I1 is relatively weak, mostly at level III (potentially developed area). Figure 5 b is a three-dimensional spatial distribution diagram of strongly developed crossflow channels. From the three-dimensional spatial distribution of strongly developed crossflow channels, P1, P3 and injection well I1 have formed obvious dominant crossflow channels with a high degree of connectivity; P2 also forms a relatively obvious dominant crossflow channel with the injection well, while P4 has not formed a dominant crossflow channel. Figure 5 c represents the streamline distribution at this time point. The streamlines are more concentrated between injection well I1 and wells P1 and P3, while wells P2 and P4 have fewer streamlines, indicating that the connectivity between injection wells and wells P1 and P3 is higher than that between wells P2 and P4. In summary, the connectivity between well group I1 and its four production wells should be in the following order: P3 > P1 > P2 > P4. According to the tracer results for this well group in 2019 (see Table 4), the connectivity between the wells and the water wells, from strongest to weakest, is in the following order: P3, P1, P2, P4. This classification result is consistent with the actual situation.

[0032] Table 4: Tracer Results for Well Group I1 This invention's method, based on reservoir static geological data and dynamic development data, firstly screens dynamic and static evaluation indicators using logical analysis, then establishes a static comprehensive evaluation index using the AHP (Analytic Hierarchy Process) method, and establishes a dynamic oil-to-flux ratio as a comprehensive evaluation index using time-varying water phase displacement flux. Next, it optimizes the classification method, introducing the concept of contour coefficient to determine the optimal standard for classifying dominant crossflow channels, avoiding the influence of subjective experience on classification categories. Finally, it forms a complete time-varying three-dimensional dominant crossflow channel classification system. The classification results are then compared with actual reservoir tracer data, showing good agreement. This method can provide a basis for subsequent dominant crossflow channel classification and control.

[0033] The applicant declares that the above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention fall within the protection and disclosure scope of the present invention.

Claims

1. A three-dimensional time-varying classification and evaluation method for the development degree of dominant channels in water-drive sandstone reservoirs, characterized in that: Includes the following steps: (I) Screening static heterogeneous evaluation indicators for the classification of dominant crossflow channels; (II) Calculate the static comprehensive evaluation index for the classification of dominant crossflow channels; (III) Establish new dynamic evaluation indicators; (IV) Establish a time-varying three-dimensional dominant crossflow channel classification method; (V) Use the silhouette coefficient of clustering to determine the grading criteria.

2. The three-dimensional time-varying classification and evaluation method for the development degree of dominant channels in water-drive sandstone reservoirs according to claim 1, characterized in that: The static heterogeneity evaluation indicators include pore radius, permeability, permeability ratio, and permeability variation coefficient.

3. The three-dimensional time-varying classification and evaluation method for the development degree of dominant channels in water-drive sandstone reservoirs according to claim 1, characterized in that: The method for selecting static heterogeneous evaluation indicators for the classification of advantageous crossflow channels is the logical analysis method.

4. The three-dimensional time-varying classification and evaluation method for the development degree of dominant channels in water-drive sandstone reservoirs according to claim 1, characterized in that: The step (II) of calculating the static comprehensive evaluation index for the classification of dominant crossflow channels specifically includes: (II-i) Calculate the weights of the static heterogeneity evaluation index; (II-II) Calculate the static comprehensive evaluation index.

5. The three-dimensional time-varying classification and evaluation method for the development degree of dominant channels in water-drive sandstone reservoirs according to claim 4, characterized in that: The specific steps (II-ⅰ) for calculating the weights of the static heterogeneity evaluation index are as follows: the weights of the static heterogeneity evaluation indexes for different dominant flow channels are determined based on the AHP analytic hierarchy process, and the weights of the static heterogeneity evaluation indexes for dominant flow channels are calculated using the arithmetic mean method, the geometric mean method, and the eigenvalue method, respectively. The average value of the weights of the static heterogeneity evaluation indexes for dominant flow channels calculated by the three methods is taken as the final weights of the static heterogeneity evaluation indexes for dominant flow channels.

6. The three-dimensional time-varying classification and evaluation method for the development degree of dominant channels in water-drive sandstone reservoirs according to claim 4, characterized in that: The step (II-ⅱ) of calculating the static comprehensive evaluation index is as follows: multiply each static heterogeneous evaluation index by its corresponding weight and then sum them to obtain the static comprehensive evaluation index.

7. The three-dimensional time-varying classification and evaluation method for the development degree of dominant channels in water-drive sandstone reservoirs according to claim 1, characterized in that: The new dynamic evaluation index is the dynamic oil-to-flow ratio.

8. The three-dimensional time-varying classification and evaluation method for the development degree of dominant channels in water-drive sandstone reservoirs according to claim 7, characterized in that: The formula for calculating the dynamic oil-to-pass ratio is: ……（1） In the formula: D is the dynamic oil-to-flow ratio, in meters. -1 ; S o S represents oil saturation, dimensionless; oi The original oil saturation is dimensionless; F W The flux is the aqueous phase displacement flux, expressed in m³ / s. x The grid cross-sectional area is in the x-direction, in meters. 2 ; S y The grid cross-sectional area in the y-direction, in meters. 2 ; S z The grid cross-sectional area is in the z-direction, in meters. 2 Q wx The volume of water phase passing through the cross section per unit time in the x-direction is expressed in cubic meters (m³). 3 / s;Q wy The volume of water phase passing through the cross section per unit time in the y-direction is expressed in cubic meters (m³). 3 / s;Q wz The volume of water phase passing through the cross section per unit time in the z-direction is expressed in cubic meters (m³). 3 / s; t1 is the starting time step, dimensionless; t2 is the ending time step, dimensionless.

9. The three-dimensional time-varying classification and evaluation method for the development degree of dominant channels in water-drive sandstone reservoirs according to claim 1, characterized in that: Step (IV), which establishes the time-varying three-dimensional dominant crossflow channel classification method, specifically includes: (Ⅳ-ⅰ) The static comprehensive evaluation index and the dynamic oil-to-flow ratio data were normalized using the linear normalization method respectively; (Ⅳ-ⅱ) The fuzzy C-means clustering algorithm is used to classify the normalized dynamic oil-to-flow ratio and static comprehensive evaluation index to determine the cluster centers of different dominant crossflow channel development levels.

10. The three-dimensional time-varying classification and evaluation method for the development degree of dominant channels in water-drive sandstone reservoirs according to claim 1, characterized in that: The step (V) of determining the grading criteria using the contour coefficients of clusters specifically involves: drawing the contour coefficients under different numbers of clusters, determining the optimal number of clusters, and using the optimal number of clusters as the grading number to classify the dominant flow channels of the oilfield based on fuzzy C-means clustering. The formula for calculating the cluster number of the silhouette coefficient is: ……（8） In the formula: a i b is the average distance between a sample and other samples in its cluster; i is the minimum average distance between a sample and all samples in other clusters; n is the number of samples in the dataset; S k The number of clusters for the silhouette coefficient.

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