A method for differentiating and quantitatively evaluating remaining oil in high water cut stage

Abstract

The application provides a high water cut period differential remaining oil classification characterization and quantitative evaluation method, comprising: establishing a differential remaining oil characterization sample matrix; standardizing differential remaining oil characterization sample data; establishing a sample data correlation coefficient matrix; obtaining sample data eigenvector eigenvalues; determining differential remaining oil main control factors; and calculating differential remaining oil classification characterization comprehensive quantitative evaluation values. The evaluation method is applied to characterize and quantitatively evaluate the formation reasons, present situation contradictions and adjustment effects of differential remaining oil in the high water cut period, so as to realize the classification understanding of high water cut remaining oil. The application solves the classification characterization of remaining oil in the high water cut period, directly improves the classification understanding of remaining oil by field technical personnel, and provides a differential countermeasure method for further improving the differential remaining oil recovery rate.

Description

Technical Field

[0001] This invention relates to the field of residual oil evaluation in water-drive reservoirs, and more specifically, to a method for differentiated classification, characterization, and quantitative evaluation of residual oil during high water-cut periods. Background Technology

[0002] After years of water-drive development, most of my country's onshore oilfields have undergone various enhancement-of-oil measures, including changes in formation, well network, production regime, profile control, and water shut-off, resulting in an extremely complex distribution of remaining oil. Conventional characterization models for remaining oil distribution, after multiple hydrodynamic measures, are no longer sufficient to meet the requirements for further enhancement during periods of high water cut. Therefore, it is necessary to research and establish new and more detailed methods for characterizing remaining oil and for quantitatively evaluating remaining oil.

[0003] The difficulty in characterizing the differential remaining oil during the high water-cut period lies in the fact that after years of complex adjustments using various measures, the distribution of remaining oil becomes extremely complex, making its characterization extremely difficult. However, further efforts to improve the recovery rate of old oilfields require a deeper understanding of the characterization of remaining oil. Summary of the Invention

[0004] To address the existing difficulties in understanding residual oil during high water cut periods, this application provides a method for classifying, characterizing, and quantifying differentiated residual oil during high water cut periods. This method enables the classification, characterization, and quantification of differentiated residual oil during high water cut periods, providing an effective technical approach for further improving oil recovery during these periods.

[0005] This application provides a method for classifying, characterizing, and quantitatively evaluating differentiated residual oil during high water cut periods, including: S1, establishing a sample matrix for differentiated residual oil characterization; S2, standardizing the sample data for differentiated residual oil characterization; S3, establishing a correlation coefficient matrix for the sample data; S4, obtaining the eigenvalues ​​of the feature vectors of the sample data; S5, determining the main controlling factors of differentiated residual oil; and S6, calculating the comprehensive quantitative evaluation value for differentiated residual oil classification and characterization.

[0006] In step S1, establishing the differentiated remaining oil characterization sample matrix includes calculating the parameters within the characterization parameter group of each grid node in the reservoir: the characterization parameter group of the cause of differentiated remaining oil formation, the characterization parameter group of the contradictions in the current status of differentiated remaining oil, and the characterization parameter group of the adjustment effect of differentiated remaining oil.

[0007] In step S1, the formation coefficient F is determined. c , water dilution ratio C wD Water drive intensity Q v The four parameters, namely extrapolated reflux degree (PR), constitute a group of characterizing parameters for the formation of differentiated residual oil, and the formula is as follows:

[0008] Stratigraphic coefficient Fc :F c =K·H

[0009] Water flow ratio C wD :

[0010] Water drive strength Q v Q v =Q w +Q o

[0011] Extrapolated reflux PR:

[0012] In the formula, K is the permeability; H is the thickness; Q w Q represents the velocity of the water phase. o V represents the oil phase velocity. p denoted as pore volume; T is the recirculation time at point (x,y); T' is the recirculation time at position (x',y') after time Δt.

[0013] In step S1, the differentiated remaining oil status is reflected in the current control status of the remaining oil, using the water drive intensity Q. v Water drive degree D w Residual oil starting performance Δ or The four parameters, extrapolated reflux rate (PR), form a group of parameters characterizing the contradictions in the current status of differentiated residual oil. The formula is as follows:

[0014] Water drive strength Q v Q v =Q w +Q o

[0015] Water drive degree D w :

[0016] Residual oil startability Δ or Δ or =(41.493-Q) v ·44.979)·e -0.0252K

[0017] Extrapolated reflux PR:

[0018] In the formula: S wi The initial water saturation; S o This represents the current oil saturation level.

[0019] In step S1, the effect of the differentiated residual oil adjustment is manifested in the improvement of the current residual oil control status, using water drive intensity Q. v ·S o Residual oil starting performance Δor Extrapolated reflux PR·S o The three parameters are used to characterize the effectiveness of differentiated residual oil control measures. The formula is as follows:

[0020] Water drive strength Q v ·S o Q v ·S o =(Q w +Q o )·S o

[0021] Residual oil startability Δ or Δ or =(41.493-Q) v ·44.979)·e -0.0252K

[0022] Extrapolated reflux PR·S o :

[0023] In step S2, the sample data is standardized using the following formula:

[0024]

[0025] In the formula: For variable index x j The average value, s j For scalar index x j variance

[0026]

[0027] In step S3, the correlation coefficient matrix R is established, and the y in the standardized matrix is ​​calculated. i With y j correlation coefficient r ij The formula is:

[0028]

[0029] In the formula: For variable index y i The average value, Let yj be the average value of the variable index.

[0030] In step S4, for a non-zero vector V, the following condition is satisfied: R·V=λ·V, where λ is a scalar called an eigenvalue, and V is called the eigenvector corresponding to the eigenvalue λ. Solve the characteristic equation of the correlation coefficient matrix R, |λI-R|=0, to find the eigenvector and eigenvalue.

[0031] In step S5, principal component analysis is used to calculate the parameter groups representing the causes of differentiated residual oil, the parameter groups representing the contradictions in the current situation of differentiated residual oil, and the parameter groups representing the effects of adjustments to differentiated residual oil. The parameter with the greatest influence in each parameter group is determined as the controlling factor of each parameter group.

[0032] The main effect factors are:

[0033] y i j is a standardized parameter variable, a i j is the i-th composite scalar, a i j is called the principal component coefficient, i.e., the eigenvector corresponding to the eigenvalue; λ i is the eigenvalue; m is the number of parameters in the parameter group.

[0034] In step S6, principal components with a cumulative contribution rate greater than 85% are selected, and their eigenvalues ​​are used as weighting coefficients. TT is defined as the differentiated residual oil comprehensive characterization and evaluation coefficient for each parameter group, and its expression is:

[0035]

[0036] The method and apparatus for differentiated residual oil classification, characterization, and quantitative evaluation during high water cut periods presented in this application have the following beneficial effects:

[0037] This invention establishes a parameter group characterizing residual oil from three perspectives: the causes of differentiated residual oil formation, the contradictions in the current status of differentiated residual oil, and the effects of adjustments to differentiated residual oil. Utilizing the principle of principal component analysis, it identifies the main controlling factors, achieving quantitative evaluation of residual oil at three levels. This invention further enriches the methods for characterizing and evaluating residual oil, enabling rapid, intuitive, and quantitative characterization and evaluation. It has significant practical value for the application of hydrodynamic enhanced oil recovery technologies in oilfields. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the process for classifying, characterizing, and quantitatively evaluating the differentiated residual oil during the high water cut period, as described in this application.

[0039] Figure 2 This is a schematic diagram of the penetration rate model in an embodiment of this application;

[0040] Figure 3 This is a schematic diagram of the penetration rate model in an embodiment of this application;

[0041] Figure 4 This is a schematic diagram of the penetration rate model in an embodiment of this application;

[0042] Figure 5 Example E1f1 of this application 1-1 Sublayer residual oil formation factor diagram;

[0043] Figure 6 Example E1f1 of this application 1-1 Diagram showing the current contradictions regarding the remaining oil in the small layer;

[0044] Figure 7 Example E1f1 of this application 1-1 Distribution map of water drive intensity in small layers;

[0045] Figure 8 Example E1f1 of this application 1-1 Incremental diagram of water drive intensity in small layers;

[0046] Figure 9 Example E1f1 of this application 1-1 Small layer of remaining oil starts up to increase volume;

[0047] Figure 10 Example E1f1 of this application 1-1 Small layer extrapolation reflux boosting diagram. Detailed Implementation

[0048] The present application will be further described below with reference to the accompanying drawings and embodiments.

[0049] Example 1

[0050] Based on the parameter group representing the causes of differentiated residual oil formation, the parameter group representing the contradictions in the current status of differentiated residual oil, the parameter group representing the adjustment effect of differentiated residual oil, the method for determining the main controlling factors of differentiated residual oil classification, and the comprehensive quantitative evaluation calculation method of differentiated residual oil classification, this application provides a method for classifying and quantitatively evaluating differentiated residual oil in high water-cut periods.

[0051] like Figure 1 As shown in the embodiment of this application, a method for classifying, characterizing, and quantitatively evaluating differentiated residual oil during high water cut periods is provided, including: S1 establishing a differentiated residual oil characterization sample matrix; S2 standardizing the differentiated residual oil characterization sample data; S3 establishing a sample data correlation coefficient matrix; S4 obtaining the eigenvalues ​​of the sample data feature vectors; S5 determining the main controlling factors of differentiated residual oil; and S6 calculating the comprehensive quantitative evaluation value of differentiated residual oil classification characterization.

[0052] The specific implementation is as follows:

[0053] S1: Establish a differentiated residual oil characterization sample matrix

[0054] Principal component analysis (PCA) uses a few uncorrelated comprehensive indicators to represent various types of information. Through dimensionality reduction, it transforms multiple variables into a few uncorrelated ones, thus obtaining the principal components with the greatest dissimilarity. Comparative analysis shows that PCA effectively removes redundant variables from the data and improves the problem of slow model convergence. The PCA process is as follows.

[0055] For m observed variables X1, ..., X2 m A sample size matrix of n observations:

[0056]

[0057] Calculate the parameters within the parameter group representing each grid node of the reservoir separately:

[0058] (1) Characterization parameter group for the formation of differential residual oil, m=4;

[0059] Stratigraphic coefficient F c :F c =K·H

[0060] Water flow ratio C wD :

[0061] Water drive strength Q v Q v =Q w +Q o

[0062] Extrapolated reflux PR:

[0063] (2) Parameter group representing the contradictions in the current status of differentiated surplus oil, m=4;

[0064] Water drive strength Q v Q v =Q w +Q o

[0065] Water drive degree D w :

[0066] Residual oil startability Δ or Δ or =(41.493-Q) v ·44.979)·e -0.0252K

[0067] Extrapolated reflux PR:

[0068] (3) Group of parameters characterizing the effect of differential residual oil adjustment, m=3;

[0069] Water drive strength Qv ·S o Q v ·S o =(Q w +Q o )·S o

[0070] Residual oil startability Δ or Δ or =(41.493-Qv·44.979)·e -0.0252K

[0071] Extrapolated reflux PR·S o :

[0072] Where: K is the permeability, D; H is the thickness, m; Q w Let m be the velocity of the water phase. 3 / s;Q o Let m be the oil phase velocity. 3 / s;V p T is the pore volume; T is the recirculation time at point (x,y), in seconds; T' is the recirculation time at position (x',y') after time Δt, in seconds; S wi f; S represents the initial water saturation. o f represents the current oil saturation.

[0073] S2: Standardization of Differentiated Residual Oil Characterization Sample Data

[0074] The following formula is used to standardize sample data and eliminate the influence of units on parameter indicators.

[0075]

[0076] in: For variable index x j The average value, s j For scalar index x j The variance.

[0077]

[0078] S3: Construct a correlation coefficient matrix for the sample data

[0079] Calculate y in the normalized matrix i With y j correlation coefficient r ij That is, the covariance matrix of the data, and the correlation coefficient matrix R is established.

[0080]

[0081] in: For variable index y i The average value, For variable index y j The average value.

[0082] S4: Find the eigenvalues ​​of the feature vector of the sample data.

[0083] For a non-zero vector V, if it satisfies the following condition:

[0084] R·V=λ·V

[0085] Here, λ is a scalar called an eigenvalue, and V is called the eigenvector corresponding to the eigenvalue λ. An eigenvector represents a vector that undergoes only scaling without changing direction under a linear transformation. The eigenvalue represents the scale of this scaling, i.e., the eigenvector and eigenvalue of solving the characteristic equation |λI-R|=0 of the correlation coefficient matrix R.

[0086] S5: Identify the key factors controlling the differential residual oil

[0087] Sort the eigenvalues ​​such that λ1≥λ2≥…≥λ m If the result is ≥0, the m predictor variables are combined into p new variables (combined variables), that is:

[0088]

[0089] Where: a i1 a i2 a im Let a be the i-th comprehensive scalar, i.e., the eigenvector corresponding to the eigenvalue. ij These are called principal component coefficients.

[0090] Principal component analysis was used to calculate the parameter groups representing the causes of differentiated surplus oil, the contradictions in the current situation of differentiated surplus oil, and the effects of adjustments to differentiated surplus oil. The parameter with the greatest influence in each parameter group was determined as the controlling factor for that group.

[0091] The main controlling factors are:

[0092] y i j is a standardized parameter variable; a i j is the i-th composite scalar, a i j is called the principal component coefficient, i.e., the eigenvector corresponding to the eigenvalue; λ i is the eigenvalue; m is the number of parameters in the parameter group.

[0093] S6: Calculate the comprehensive quantitative evaluation value of differentiated residual oil classification characterization

[0094] Principal components with a cumulative contribution rate greater than 85% were selected, and their eigenvalues ​​were used as weighting coefficients. TT was defined as the comprehensive evaluation coefficient for the differentiated remaining oil of each parameter group, specifically expressed as follows:

[0095] Comprehensive quantitative evaluation value T:

[0096] This application, based on the aforementioned parameter groups representing the causes of differentiated residual oil formation, the parameter groups representing the current contradictions of differentiated residual oil status, the parameter groups representing the adjustment effects of differentiated residual oil, the method for determining the main controlling factors of differentiated residual oil classification, and the comprehensive quantitative evaluation calculation method for differentiated residual oil classification, applies a method for classifying and quantitatively evaluating differentiated residual oil in high water-cut periods. It characterizes the causes of differentiated residual oil formation, current contradictions, and adjustment effects in high water-cut periods and performs quantitative evaluation calculations to achieve a categorized understanding of high water-cut residual oil. This application solves the problem of classifying and characterizing residual oil in high water-cut periods, directly improving field technicians' understanding of residual oil classification, and providing differentiated countermeasures for further targeted improvement of differentiated residual oil recovery rates.

[0097] Example 2

[0098] This application addresses the complexity of residual oil in the high water-cut stage by analyzing and identifying influencing factors at three levels: the causes of residual oil formation, the current contradictions of residual oil status, and the effects of residual oil adjustment. It establishes a parameter group to characterize each level. Using principal component analysis, the application analyzes the influence of each parameter in the parameter group to determine the main controlling factors for the causes of residual oil formation, the current contradictions of residual oil status, and the effects of residual oil adjustment. Furthermore, using the parameters, eigenvectors, and eigenvalues ​​of the parameter group, it establishes comprehensive quantitative evaluation values ​​for the causes of residual oil formation, the current contradictions of residual oil status, and the effects of residual oil adjustment, thereby achieving a classification, characterization, and comprehensive evaluation of the main controlling factors of residual oil at different sub-layer locations in the high water-cut stage.

[0099] The specific implementation method is as follows:

[0100] 1. Establishment of the one-injection-three-collection model

[0101] (1) Permeability design of high-permeability injection and low-permeability extraction model

[0102] For a model of water injection in a high-permeability location and oil production in a relatively low-permeability location with one injection and three production wells, the design permeability and the relationship between injection and production wells are as follows: Figure 2 As shown, the high-permeability rate is 300 mD, the low-permeability rate is 100 mD, and the intermediate-permeability rate is 200 mD.

[0103] (2) Permeability Design of Low-Permeability Injection and High-Permeability Extraction Model

[0104] For a model involving injection and production in a relatively low-permeability location with water injection and a high-permeability location, the design permeability and the relationship between injection and production wells are as follows: Figure 3 As shown, the high-permeability rate is 300 mD, the low-permeability rate is 100 mD, and the intermediate-permeability rate is 200 mD.

[0105] (3) Permeability design of injection-production model in the vertical 90-degree direction

[0106] For a model with one injection and three production lines in the vertical direction, the design permeability and the relationship between injection and production wells are as follows: Figure 4 As shown, the high-permeability rate is 300 mD, the low-permeability rate is 100 mD, and the intermediate-permeability rate is 200 mD.

[0107] 2. Characterization and evaluation of the causes of residual oil formation in one injection and three recovery processes

[0108] As can be seen from the principal component analysis of the residual oil from the first injection and third recovery, Table 1 shows that:

[0109] (1) Under injection and production at a vertical 90-degree angle, the main factors in the formation of residual oil are determined by a combination of factors from all directions;

[0110] (2) Under high-permeability injection and low-permeability production, the main factors in the formation of residual oil are determined by a comprehensive range of factors.

[0111] (3) Under low-permeability injection and high-permeability production, the main factors in the formation of residual oil are determined by a comprehensive range of factors.

[0112] Therefore, it can be seen that the main factors in the formation of residual oil in the one-injection-three-production model are determined by a combination of factors from all aspects. However, the water ratio of injection and production in the vertical 90-degree direction has the greatest impact, followed by the water injection intensity. For the two modes of high-permeability injection and low-permeability production and low-permeability injection and high-permeability production, the water injection intensity is the most influential factor.

[0113] Table 1. Principal Component Analysis of Residual Oil Formation from First-Injection and Third-Recovery Oil

[0114]

[0115]

[0116] (4) Comprehensive comparative evaluation

[0117] For three well patterns with one injection and three production, comprehensive evaluations were conducted under different heterogeneous conditions. The results are shown in Table 2. When high-permeability injection is performed and low-permeability production is performed, the absolute value of the comprehensive evaluation is the highest, which also corresponds to the highest degree of recovery. When low-permeability injection is performed and high-permeability production is performed, the absolute value of the comprehensive evaluation is the lowest, which corresponds to the lowest degree of recovery. It can be seen that for the same block, the comprehensive evaluation value using principal component analysis can quantitatively characterize the effect of waterflooding residual oil.

[0118] Table 2 Comparison of Comprehensive Evaluation of Differentiated Residual Oil Formation Factors

[0119]

[0120] 3. Evaluation of the contradictions in the current status of remaining oil in the three-stage oil recovery system.

[0121] As can be seen from the principal component analysis of the residual oil from the first injection and third recovery, Table 3 shows that:

[0122] (1) Under injection and production at a vertical 90-degree angle, the main factors in the formation of residual oil are determined by a combination of factors from all directions;

[0123] (2) Under high-permeability injection and low-permeability production, the main factors in the formation of residual oil are determined by a comprehensive range of factors.

[0124] (3) Under low-permeability injection and high-permeability production, the main factors in the formation of residual oil are determined by a comprehensive range of factors.

[0125] Therefore, it can be seen that the main factors in the formation of residual oil in a single injection and three-stage recovery system are determined by a combination of various factors. The start-up capability of residual oil has the greatest impact, followed by the degree of water drive, which is the most influential factor.

[0126] Table 3. Principal Component Analysis of Remaining Oil Status in First Injection and Third Recovery.

[0127]

[0128]

[0129] (4) Comprehensive comparative evaluation

[0130] For three well patterns with one injection and three production, comprehensive evaluations were conducted under different heterogeneous conditions. The results are shown in Table 4. The comprehensive evaluation absolute value was the highest for low-permeability injection and high-permeability production, corresponding to the lowest production degree. The comprehensive evaluation absolute value was the lowest for high-permeability injection and low-permeability production, corresponding to the highest production degree. It can be seen that for the same block, the comprehensive evaluation value using principal component analysis can quantitatively characterize the degree of the current water drive contradiction.

[0131] Table 4 Comparison of Comprehensive Evaluation Factors of Differentiated Residual Oil Status

[0132]

[0133] 4. Evaluation of the effectiveness of the one-injection-three-recovery residual oil measure

[0134] As can be seen from the principal component analysis of the effect of the one-injection-three-recovery residual oil measure, Table 5 shows that:

[0135] (1) Under injection and production at a vertical 90-degree angle, the main factors in the formation of residual oil are determined by a combination of factors from all directions;

[0136] (2) Under high-permeability injection and low-permeability production, the main factors in the formation of residual oil are determined by a comprehensive range of factors.

[0137] (3) Under low-permeability injection and high-permeability production, the main factors in the formation of residual oil are determined by a comprehensive range of factors.

[0138] Therefore, the effectiveness of the residual oil recovery measures for the three-stage injection system is mainly determined by a combination of factors, with extrapolated backflow *SO having the greatest impact, followed by water drive intensity *SO being the most influential factor.

[0139] Table 5. Principal Component Analysis of the Effects of One-Injection-Three-Recovery Oil Recovery Measures on Residual Oil

[0140]

[0141]

[0142] (4) Comprehensive comparative evaluation

[0143] For three well patterns with one injection and three production, comprehensive evaluations were conducted under different heterogeneous conditions. The results are shown in Table 6. The comprehensive evaluation effect of fluid extraction is better than that of increased injection, and the effect of fluid extraction and increased injection is better than that of simple fluid extraction.

[0144] Table 6. Comparison of Comprehensive Evaluation Factors of Differentiated Residual Oil Measures

[0145]

[0146] This application utilizes the principle of principal component analysis to analyze the influence of each parameter in the parameter group, determine the main controlling factors of the causes of residual oil formation, the contradictions in the current status of residual oil, and the effects of residual oil adjustment. At the same time, using each parameter in the parameter group, as well as eigenvectors and eigenvalues, comprehensive quantitative evaluation values ​​for the causes of residual oil formation, the contradictions in the current status of residual oil, and the effects of residual oil adjustment are established respectively. This enables the classification, characterization, and quantitative evaluation of differentiated residual oil in the high water-cut period, providing an effective technical method for further improving oil recovery in the high water-cut period.

[0147] Example 3

[0148] The specific implementation is as follows:

[0149] 1. Characterization and evaluation of the causes of differentiated residual oil

[0150] The formation coefficient, water flow ratio, water drive intensity, and extrapolated reflux parameters were dimensionless, and principal component analysis was performed to determine the causes of residual oil formation. The results of solving the eigenvectors and eigenma are shown in Table 7. It can be seen that extrapolated reflux and permeability are the principal factors in the first principal component, while water flow ratio and water drive intensity are the principal factors in the second principal component. This shows that the formation of residual oil in Block Chen 2 is affected by multiple factors.

[0151] Table 7 Principal Component Analysis of the Causes of Residual Oil Formation in Piece 2 of Chen's Oil

[0152] 1 0.703968 -0.06862 -0.08682 -0.70156 1.347469 * 2 0.056742 0.710338 0.694621 -0.09850 1.057148 * 3 0.707837 0.024446 0.017259 0.705742 0.944953 * 4 0.013291 -0.70008 0.713909 -0.00654 0.65043 *

[0153] Note: 0. Water boundary area; 1. Formation coefficient; 2. Water flow ratio; 3. Water drive intensity; 4. Extrapolation backflow; 5. Not affected by water injection.

[0154] like Figure 5 As shown, Figure 5 In the diagram, 0 represents the edge water region, 1 represents water drive intensity, 2 represents water drive degree, 3 represents residual oil initiation, 4 represents extrapolated backflow, and 5 represents areas unaffected by water injection. The formation of residual oil at various locations within each sub-layer plane is characterized. Locations 4 (extrapolated backflow) and 5 (unaffected by water injection) represent areas with well-developed injection-production systems where fluid flow has shifted. Location 2 (high water flow ratio) represents areas with high water flow ratios, and location 3 (high water drive intensity) represents areas with high-speed water drive. It can be seen that: E1f1 1-1 The areas not affected by water injection in the small layers are located in the high part of the fault and the local edge water area, and water injection through the well network has not been achieved to date. The remaining oil formed by the outward backflow is concentrated in the local area of ​​the waist, and the reservoir properties are the main influencing factor in the remaining oil enrichment area.

[0155] The remaining reserves were statistically analyzed for the main factors forming various remaining oils, and the results are shown in Table 8. It can be seen that:

[0156] (1) The area controlled by the main factor of water drive intensity has the least remaining recoverable reserves and the average remaining oil saturation is 0.251;

[0157] (2) The remaining recoverable reserves in the main control area of ​​the water dilution ratio are 100,859 t, and the average remaining oil saturation is 0.379.

[0158] (3) The area controlled by the main factors of the formation coefficient has the largest remaining recoverable reserves, which is 57,643t, and the average remaining oil saturation is 0.279.

[0159] (4) The remaining recoverable reserves in the main factor control areas unaffected by extrapolation reflux and water injection are 29,097t and 117,242t, respectively. The average oil saturation in the extrapolation reflux area is 0.348, and the average oil saturation in the water injection unaffected area is 0.482.

[0160] It is evident that the areas unaffected by water injection, the formation coefficient, and the water flow ratio are the main contributing factors to the remaining oil potential.

[0161] Table 8. Quantitative Statistical Table of the Causes of Differentiated Residual Oil Formation

[0162]

[0163]

[0164] 2. Characterization and evaluation of the contradictions in the current status of differentiated surplus oil

[0165] like Figure 6 As shown, Figure 6In the middle, 0: edge water zone; 1: water drive intensity; 2: water drive degree; 3: residual oil start-up capability; 4: extravasation backflow; 5: water injection not affected. E1f1 1-1 The main contradictions in the small layer are the contradiction of 4 outward backflow and the 5 uncontrolled water injection zone near the edge water position. This area is mainly concentrated on the outer side of the oil wells in the injection-production well group; the main contradiction around the water injection well is the contradiction of water drive intensity.

[0166] The current situation regarding the control of remaining oil supply and demand is statistically analyzed, and the results are shown in Table 9.

[0167] ① The current situation is contradictory. The water drive intensity control has a remaining geological reserve of 35,400t, a remaining recoverable reserve of 5,277t, and an average remaining oil saturation of 0.24. The remaining recoverable reserves are small and the oil saturation is low, indicating that the reserves are in a state of no potential.

[0168] ② The current situation is contradictory. The water-driven area has a remaining geological reserve of 38,311 tons, a remaining recoverable reserve of 15,476 tons, and an average oil saturation of 0.336.

[0169] ③ The remaining recoverable reserves in the current oil-starting area are 34,714 tons, with an average oil saturation of 0.451, indicating certain potential.

[0170] ④ The remaining recoverable reserves in the current contradiction-prone area are controlled by the extrapolation and return of oil, with an average oil saturation of 0.336, which has certain potential.

[0171] ⑤ The remaining recoverable reserves in the areas where the injected water has not yet affected are 118,845, with an average oil saturation of 0.482, making it a major potential area.

[0172] Table 9: Status of Remaining Oil in Current Conflict Control Measures

[0173] 1 Water drive intensity 35400 5277 0.24 2 Water drive degree 38311 15476 0.336 3 Residual oil startability 62781 34714 0.451 4 Extrapolation backflow 344136 136140 0.336 5 The water injection did not affect [the affected area]. 219432 118845 0.482

[0174] 3. Characterization and evaluation of the effects of differentiated residual oil measures

[0175] like Figure 7-10 As shown, with the current E1f1 1-1 Based on the distribution of water drive intensity in the small layer, by comparing the increase in water drive intensity, the increase in residual oil start-up, and the increase in extrapolated reflux at different locations, it can be seen that all three indicators have been effectively improved, especially in the eastern part of the fault block where the water drive intensity is relatively low (green and red areas).

[0176] This application provides a differentiated classification, characterization, and quantitative evaluation method for residual oil during high water cut periods, which further enriches the methods for characterizing and evaluating residual oil. It enables rapid, intuitive, and quantitative characterization and evaluation of residual oil, and has significant practical value for the application of hydrodynamic techniques to further enhance oil recovery in oilfields.

[0177] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for classifying, characterizing, and quantitatively evaluating differentiated residual oil during high water cut periods, characterized in that, include: S1, Establish a sample matrix for characterizing differentiated residual oil; S2, Standardization of sample data for differential residual oil characterization; S3, Establish the correlation coefficient matrix of the sample data; S4, calculate the eigenvalues ​​of the feature vector of the sample data; S5, determine the main controlling factors of differentiated residual oil; S6, Calculate the comprehensive quantitative evaluation value of the differentiated residual oil classification characterization; In step S1, establishing the differentiated remaining oil characterization sample matrix includes calculating the parameters in the characterization parameter group of each grid node of the reservoir: the characterization parameter group of the cause of differentiated remaining oil formation, the characterization parameter group of the contradiction of the current status of differentiated remaining oil, and the characterization parameter group of the adjustment effect of differentiated remaining oil. In step S1, the formation coefficient is determined. , water dilution ratio Water drive intensity Extrapolated reflux The four parameters form a group of characterizing parameters for the formation of differential residual oil, and the formula is: stratigraphic coefficient : water dilution ratio : Water drive intensity : Extrapolated reflux : Where: K is the permeability; H is the thickness; The velocity of the water phase; Oil phase velocity; Pore ​​volume; For point Reflux time; For the passage of time Rear position Reflux time; In step S1, the differentiated remaining oil status is reflected in the current control status of the remaining oil, using water drive intensity. water drive degree Residual oil starting performance Extrapolated reflux The four parameters form a group of parameters representing the contradictions in the current status of differentiated surplus oil, and the formula is: Water drive intensity : Water drive degree : Residual oil startability : Extrapolated reflux : in: This represents the initial water saturation level. This represents the current oil saturation level. In step S1, the effect of the differentiated residual oil adjustment is manifested in the improvement of the current residual oil control status, using water drive intensity. · Residual oil starting performance Extrapolated reflux · The three parameters are used to characterize the effectiveness of differentiated residual oil control measures. The formula is as follows: Water drive intensity · : Residual oil startability : Extrapolated reflux · : .

2. The method for differentiated residual oil classification, characterization, and quantitative evaluation during high water cut periods according to claim 1, characterized in that, In step S2, the sample data is standardized using the following formula: in: Variable indicators The average value, ; Scalar indicator variance 。 3. The method for differentiated residual oil classification, characterization, and quantitative evaluation during high water cut periods according to claim 1, characterized in that, In step S3, the correlation coefficient matrix R is established, and the standardized matrix is ​​calculated. and correlation coefficient The formula is: in: Variable indicators The average value, Variable indicators The average value.

4. The method for differentiated residual oil classification, characterization, and quantitative evaluation during high water cut periods according to claim 1, characterized in that, In step S4, for a non-zero vector It satisfies the following conditions: , in It is a scalar, called an eigenvalue. This is called the corresponding eigenvalue. eigenvectors, Solve the characteristic equation of the correlation coefficient matrix R. eigenvectors and eigenvalues.

5. The method for differentiated residual oil classification, characterization, and quantitative evaluation during high water cut periods according to claim 1, characterized in that, In step S5, principal component analysis is used to calculate the parameter groups representing the causes of differentiated residual oil, the parameter groups representing the contradictions in the current situation of differentiated residual oil, and the parameter groups representing the effects of adjustments to differentiated residual oil. The parameter with the greatest influence in each parameter group is determined as the controlling factor of each parameter group, i.e.: The main effect factors are: For standardized parameter variables, For the i-th synthetic scalar, These are called principal component coefficients, which are the eigenvectors corresponding to the eigenvalues; For eigenvalues; The number of parameters in the parameter group.

6. The method for differentiated residual oil classification, characterization, and quantitative evaluation during high water cut periods according to claim 1, characterized in that, In step S6, principal components with a cumulative contribution rate greater than 85% are selected, and their eigenvalues ​​are used as weight coefficients to define... The differential residual oil comprehensive characterization and evaluation coefficients for each parameter group are expressed as follows: 。

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