Method for determining polymer flooding recovery efficiency improvement value of high-salinity oil reservoir

Abstract

The invention relates to a method for determining a polymer flooding recovery efficiency improvement value of a hypersalinity oil reservoir, and belongs to the technical field of oilfield development. According to the method for determining the polymer flooding recovery efficiency improvement value of the hypersalinity oil reservoir, the defects that in the prior art, the influence factors of the polymer flooding recovery efficiency are incomplete, and the weight coefficients of the influence factors are inaccurate are overcome, all the factors influencing the polymer flooding effect are comprehensively considered, and the improvement value of the polymer flooding recovery efficiency of the hypersalinity oil reservoir is determined by conducting analytic hierarchy process on the factors. According to the method, main factors in all the factors are clarified, and then the polymer flooding recovery efficiency improvement value of the to-be-predicted hypersalinity oil reservoir can be calculated according to the main factors of the to-be-predicted hypersalinity oil reservoir by determining the corresponding relation between the polymer flooding recovery efficiency improvement value and the main factors. The method for determining the polymer flooding recovery efficiency improvement value of the hypersalinity oil reservoir has the advantages of high precision and small error, and the recovery efficiency improvement value can be simply, quickly and reasonably calculated.

Description

Technical Field

[0001] The present invention relates to a method for determining the enhanced oil recovery value of polymer flooding in high salinity reservoirs, belonging to the technical field of oilfield development. Background Art

[0002] Polymer flooding is a technology to improve the oil recovery by expanding the sweep efficiency through polymers. The enhanced oil recovery value is an important parameter to evaluate the potential and effect of polymer flooding. When multiple reservoirs are involved in the evaluation of polymer flooding potential and block screening, the calculation of the enhanced oil recovery value of polymer flooding is particularly important.

[0003] There are many existing methods for obtaining the enhanced oil recovery value of polymer flooding. For example, Chinese Patent Document CN108119110A discloses a method for predicting the technical effect of polymer flooding. This method divides the factors affecting the reservoir oil recovery into main control factors (polymer injection volume, polymer concentration) and auxiliary factors (reservoir permeability, crude oil viscosity, reservoir water salinity, reservoir temperature). By obtaining the relationship diagram between each factor and the increase amplitude of oil recovery, calculating the enhanced oil recovery value corresponding to each factor, and combining the weight ratio of each factor, the increase amplitude of oil recovery of the polymer flooding technical scheme is finally obtained. Chinese Patent Document CN108491625A discloses a method for predicting the enhanced oil recovery of a ternary composite flooding system. This method defines a water cut decline funnel, establishes a water cut model, and then establishes a quantitative characterization model of the oil increment curve of polymer flooding; finally, the enhanced oil recovery value is calculated by using the difference between the fitted water flooding water cut, the continuous polymer flooding water cut and the water flooding water cut. Chinese Patent Document CN110457857A discloses a method for predicting the effect of polymer flooding under different crude oil viscosity conditions. This method uses the commercial simulation software Eclipse to simulate the oil displacement effect of different polymer solutions under different crude oil viscosity conditions, and performs multiple non-linear regression processing on the oil recovery and the maximum injection pressure difference in the obtained simulation results with the effective viscosity and the residual resistance coefficient respectively, to obtain the functional relationships between the oil recovery and the maximum injection pressure difference with the effective viscosity and the residual resistance coefficient respectively, and predict the final oil recovery and the maximum injection pressure difference.

[0004] The above existing technologies all set one or two parameters affecting the enhanced oil recovery value of polymer flooding as independent variables, set the enhanced oil recovery value as the dependent variable, fit a binary or ternary regression equation, and then add them together through the binary or ternary regression equations of different parameters and their weights to obtain the final enhanced oil recovery value.

[0005] Polymer flooding is suitable for heterogeneous reservoirs after water flooding development. The permeability variation coefficient is an important parameter to characterize the reservoir heterogeneity. When evaluating the increased value of polymer flooding recovery, the permeability variation coefficient is indispensable. Both indoor physical simulation experiments and field tests have shown that when the permeability variation coefficient is less than 0.7, the polymer flooding effect gets better as the permeability variation coefficient increases. When the permeability variation coefficient is greater than 0.7, with the increase of the permeability variation coefficient, the polymer flooding effect drops sharply. Therefore, the influence of the reservoir permeability variation coefficient on the increased value of polymer flooding recovery cannot be ignored.

[0006] However, the above existing technologies all ignore the influence of the reservoir permeability variation coefficient on the increased value of polymer flooding recovery, and do not comprehensively consider multiple factors affecting the increased value of polymer flooding recovery. They only artificially assign weight coefficients to each factor, resulting in a relatively low accuracy of the calculated increased value of polymer flooding recovery. Summary of the Invention

[0007] The purpose of the present invention is to provide a method for determining the increased value of polymer flooding recovery in high salinity reservoirs, which can solve the problem of relatively low accuracy in determining the increased value of polymer flooding recovery at present.

[0008] In order to achieve the above purpose, the technical solution adopted by the method for determining the increased value of polymer flooding recovery in high salinity reservoirs of the present invention is as follows:

[0009] A method for determining the increased value of polymer flooding recovery in high salinity reservoirs, comprising the following steps:

[0010] S1, using the analytic hierarchy process to calculate the weight coefficients of the factors affecting the polymer flooding recovery in high salinity reservoirs, and then selecting the main factors from these factors according to the magnitudes of the weight coefficients;

[0011] S2, assigning several set values to each of the main factors, and then combining the set values of all the main factors to obtain several different test conditions. Each combination formed by the set values of all the main factors is a test condition;

[0012] S3, determining the increased value of polymer flooding recovery under each test condition, and then performing regression analysis on the increased value of polymer flooding recovery and the main factors to obtain the corresponding relationship between the increased value of polymer flooding recovery and the main factors; finally, according to the main factors of the high salinity reservoir to be predicted and the corresponding relationship, obtaining the increased value of polymer flooding recovery of the high salinity reservoir to be predicted; the values of the main factors of the high salinity reservoir to be predicted are within the numerical ranges of the corresponding main factors assigned in step S2.

[0013] The method for determining the enhanced polymer flooding recovery value in high salinity reservoirs of the present invention overcomes the defects in the prior art that the influencing factors of polymer flooding recovery are incomplete and the weight coefficients of the influencing factors are inaccurate. It comprehensively considers all factors affecting the polymer flooding effect. By performing hierarchical analysis on these factors, the main factors among all factors are identified. Then, by determining the corresponding relationship between the enhanced polymer flooding recovery value and the main factors, the enhanced polymer flooding recovery value of the to-be-predicted high salinity reservoir can be calculated based on the main factors of the to-be-predicted high salinity reservoir. Experimental results show that the method for determining the enhanced polymer flooding recovery value in high salinity reservoirs of the present invention has the advantages of high precision and small error, and can simply, quickly and reasonably calculate the enhanced recovery value, providing scientific guidance for polymer flooding block screening and potential evaluation.

[0014] It can be understood that the numerical range of a certain main factor is from the minimum value to the maximum value among the set values assigned to the main factor.

[0015] In the present invention, a high salinity reservoir refers to a reservoir with a salinity of not less than 10000 mg / L, an average reservoir permeability of 50 - 450 mD and meeting the conditions for polymer flooding.

[0016] The factors affecting the enhanced polymer flooding recovery in high salinity reservoirs can be determined according to production experience or the records in existing literature. Preferably, the factors affecting the enhanced polymer flooding recovery in high salinity reservoirs include reservoir depth, reservoir temperature, formation water salinity, formation crude oil viscosity, crude oil density, average reservoir permeability, permeability variation coefficient, polymer injection concentration, production degree and comprehensive water cut.

[0017] When assigning set values to each main factor, the selection of the set values can be carried out according to the screening criteria of polymer flooding or according to the values of the main factors in the polymer-injected blocks in the reservoir.

[0018] Preferably, the main factor refers to a factor with a weight coefficient of not less than 0.1.

[0019] Preferably, the orthogonal experiment method is used to combine the set values of all main factors. To improve the accuracy of the results, the levels of each main factor in the orthogonal experiment are not less than 5. For example, an n-factor five-level orthogonal experiment is used to combine the set values of all main factors, where n is the number of main factors.

[0020] Orthogonal experiment is a design method for studying multi-factors and multi-levels. It selects some representative points from the comprehensive experiment according to orthogonality. These representative points have the characteristics of "even dispersion and neat comparability". Orthogonal experiment design is the main method for analyzing factor design and is a highly efficient, fast and economical experimental design method.

[0021] To simplify the calculation and improve the calculation efficiency, preferably, the regression analysis is linear regression analysis.

[0022] In the present invention, methods such as physical experiments or numerical simulations can be used to determine the enhanced polymer flooding recovery value under each test condition. To improve efficiency and simplify the experiment, preferably, the numerical simulation method is used to determine the enhanced polymer flooding recovery value under each test condition. Preferably, the numerical simulation is carried out using the commercial simulation software Eclipse. To avoid the influence of differences in other conditions except the main factors on the results, during physical experiments or numerical simulations, ensure that other conditions except the main factors are the same under different test conditions.

[0023] To improve the accuracy of the results, preferably, the method for determining the enhanced polymer flooding recovery value further includes the following steps: after obtaining the corresponding relationship, test the corresponding relationship. If the test result meets the requirements, then determine the enhanced polymer flooding recovery value of the high salinity reservoir to be predicted. Preferably, the test includes correlation test and significance test. Preferably, the method of the test includes the following steps: according to the values of each main factor in each test condition and the corresponding relationship, obtain the calculated value of the enhanced polymer flooding recovery value under each test condition, and then linearly fit the enhanced polymer flooding recovery value determined in step S3 and the calculated value of the enhanced polymer flooding recovery value. Preferably, the test result meeting the requirements means that the correlation coefficient of the equation obtained by linear fitting analysis is not less than 0.95, and the significance statistic of the equation obtained by linear fitting analysis is less than 0.05. The significance test is carried out using the analysis of variance method. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic flow chart of the method for determining the enhanced polymer flooding recovery value of the high salinity reservoir in the embodiment of the present invention;

[0025] Figure 2 It is a schematic diagram of the result of linear fitting analysis of the calculated values and simulated values of the enhanced polymer flooding recovery values corresponding to 25 orthogonal test schemes in step (6) of the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] The technical solutions of the present invention will be further described below in conjunction with specific embodiments.

[0027] Embodiment

[0028] The method for determining the enhanced polymer flooding recovery value of the high salinity reservoir in this embodiment takes the high salinity reservoir unit A as the research object, as Figure 1 shown, and specifically includes the following steps:

[0029] (1) According to the characteristics of polymer flooding in high salinity reservoirs and production practice experience, the factors affecting the polymer flooding recovery rate in high salinity reservoirs are determined. The factors determined in this embodiment are reservoir depth, reservoir temperature, formation water salinity, formation crude oil viscosity, formation crude oil density, average reservoir permeability, permeability variation coefficient, polymer injection concentration, recovery factor, and comprehensive water cut. Among them, reservoir depth, reservoir temperature, and formation water salinity are reservoir condition factors, formation crude oil viscosity and formation crude oil density are crude oil condition factors, average reservoir permeability and permeability variation coefficient are reservoir condition factors, and polymer injection concentration, recovery factor, and comprehensive water cut are development condition factors;

[0030] Then, the analytic hierarchy process is used to quantitatively calculate the weight coefficients of each factor. The results are shown in Table 1. The factors with weight coefficients greater than or equal to 0.1 are selected as the main factors, and the main factors are used to participate in the calculation to determine the increased value of the polymer flooding recovery rate. The main factors selected in this embodiment are reservoir temperature, formation water salinity, formation crude oil viscosity, average reservoir permeability, permeability variation coefficient, and polymer injection concentration;

[0031] Table 1 Weight coefficients of each factor

[0032]

[0033]

[0034] The analytic hierarchy process (AHP), also known as the multi-level weight analysis method, decomposes complex problems into several ordered levels such as objectives, criteria, and solutions. Based on objective judgments, the relative importance of different elements at each level is quantitatively represented. Then, mathematical methods are used to calculate the relative importance values of all evaluation indicators at each level, and consistency tests are used to judge whether there are logical errors in the importance degree of indicators, so as to conduct quantitative analysis on the evaluation system. The basic steps of the analytic hierarchy process are as follows:

[0035] ① Establish a hierarchical structure model

[0036] When constructing the hierarchical structure model, it is necessary to deeply analyze the factors and their mutual relationships involved in the actual problem, decompose each factor into several levels from top to bottom according to different attributes. The factors at the same level belong to the factors at the upper level or have an impact on the factors at the upper level, and at the same time dominate the factors at the lower level or are affected by the factors at the lower level. The hierarchical structure model is usually represented by a structure model diagram composed of an objective layer, a criterion layer, and a solution layer;

[0037] ② Construct a judgment matrix

[0038] The judgment matrix represents the comparison of the relative importance of all factors at this level with respect to a certain factor at the upper level, that is: make a judgment based on the relative importance of each factor at each level, and use the numbers 1 to 9 and their reciprocals as scales to define the judgment matrix; the scales and their meanings of the judgment matrix are shown in Table 2;

[0039] Table 2 Scales and Their Meanings of the Judgment Matrix

[0040]

[0041]

[0042] ③ Calculate the weights of evaluation indicators

[0043] Use the sum-product method to calculate the maximum eigenvalue of the judgment matrix and its corresponding eigenvector;

[0044] ④ Consistency check

[0045] The analytic hierarchy process requires that the judgment matrix has consistency to make the calculation results basically reasonable, and it is necessary to conduct a consistency check on the judgment matrix; use the consistency ratio CR to conduct the consistency check. If CR < 0.1, it is considered that the judgment matrix passes the consistency check, otherwise it fails; CR = CI / RI, where RI is the consistency index, CI = (λmax - n) / (n - 1), and n is the order of the judgment matrix; if the test passes, normalize the eigenvector, and the normalized eigenvector is the weight vector; if it fails, it is necessary to reconstruct the judgment matrix until the consistency check is passed;

[0046] In this embodiment, the factors A affecting the polymer flooding recovery rate of high salinity reservoirs include the first-level factors B1 reservoir conditions, B2 crude oil conditions, B3 reservoir conditions, B4 development conditions. The first-level factor B1 (reservoir conditions) includes the second-level factors C1 (reservoir depth), C2 (reservoir temperature), and C3 (formation water salinity). The first-level factor B2 (crude oil conditions) includes the second-level factors C4 (formation crude oil viscosity) and C5 (formation crude oil density). The first-level factor B3 (reservoir conditions) includes the second-level factors C6 (average reservoir permeability) and C7 (permeability variation coefficient). The first-level factor B4 (development conditions) includes the second-level factors C8 (polymer injection concentration), C9 (recovery degree), and C10 (comprehensive water cut);

[0047] In this embodiment, the judgment matrix constructed based on all first-level factors is shown in Table 3;

[0048] Table 3 Judgment Matrix Constructed Based on All First-Level Factors

[0049]

[0050] In this embodiment, the judgment matrix constructed based on each second-level factor under each first-level factor is shown in Table 4-7;

[0051] Table 4 Judgment matrix established based on each second-level factor under the first-level factor B1

[0052]

[0053] Table 5 Judgment matrix established based on each second-level factor under the first-level factor B2

[0054]

[0055] Table 6 Judgment matrix established based on each second-level factor under the first-level factor B3

[0056]

[0057] Table 7 Judgment matrix established based on each second-level factor under the first-level factor B4

[0058]

[0059] In this embodiment, the constructed judgment matrices all pass the consistency test, and the weight coefficients of each factor can be calculated according to the normalized weights in the judgment matrix table;

[0060] (2) According to the screening criteria of polymer flooding and combining with the characteristics of the high salinity reservoir unit A (the formation water salinity of the high salinity reservoir unit A is between 20000 and 100000 mg / L, the average reservoir permeability is between 50 and 450 mD, the reservoir temperature is between 60 and 80 °C, the formation crude oil viscosity is not more than 50 mPa·s, and the polymer injection concentration used in the polymer flooded reservoir is 0.05-0.25%), the value ranges of each main factor (reservoir temperature, formation water salinity, formation crude oil viscosity, average reservoir permeability, permeability variation coefficient, and polymer injection concentration) are determined; the polymer flooding screening criteria are shown in Table 8, and the value ranges of each main factor are shown in Table 9;

[0061] Table 8 Screening criteria for polymer flooding

[0062]

[0063] Note: The "-" in Table 8 represents that it can be any value.

[0064] Table 9 Value ranges of each main factor

[0065] Main factor Value range Reservoir temperature (°C) 60～100 Formation water salinity (mg / L) 20000～100000 Formation crude oil viscosity (mPa·s) 10～50 Average reservoir permeability (mD) 50～450 Reservoir permeability variation coefficient 0.4～0.8 Polymer injection concentration (%) 0.05～0.25

[0066] (3) According to the value ranges of the main factors determined in step (2), the levels of each main factor are set to 5, that is, 5 different values (the 5 different values include the maximum and minimum values in the value range) are selected at equal intervals from the value range of each main factor as experimental values. Then, an orthogonal experiment design is carried out to obtain a six-factor five-level orthogonal experiment table and 25 orthogonal experiment schemes. The 25 orthogonal experiment schemes are obtained by uniformly combining the 5 values selected from the value range of each main factor. The combination results of the values corresponding to each main factor in the 25 orthogonal experiment schemes are shown in Table 10;

[0067] Table 10 The 25 orthogonal experiment schemes designed

[0068]

[0069]

[0070] (4) According to the 25 orthogonal experiment schemes determined in step (3), using the commercial simulation software Eclipse, polymer flooding simulation is carried out respectively using the specific values of the main factors in the 25 orthogonal experiment schemes to obtain the simulated values of the polymer flooding recovery improvement values corresponding to the 25 orthogonal experiment schemes; to avoid affecting the accuracy of the results due to differences in other conditions except the main factors, when carrying out polymer flooding simulation using different orthogonal experiment schemes, other conditions except the main factors are controlled to be the same;

[0071] (5) Taking the main factors in each orthogonal experiment scheme in step (4) as independent variables and the simulated values of the polymer flooding recovery improvement values corresponding to each orthogonal experiment scheme as dependent variables, a six-variable linear regression analysis is carried out to obtain a six-variable linear regression equation. The six-variable linear regression equation is as follows: f m =-1.155×10 -4 ×T - 3.105×10 -9 ×C W +7.406×10 -5 ×K + 5.54×10 -4 ×C - 2.166×10 -2 ×C oe +3.12×10 -5 ×η + 0.0604; where f m is the simulated value of the polymer flooding recovery improvement value; T is the reservoir temperature, in °C; C W is the formation water salinity, in mg / L; K is the average reservoir permeability, in mD; C is the polymer injection concentration, dimensionless; C eois the permeability variation coefficient, dimensionless; η is the viscosity of formation crude oil, with the unit of mPa·s; the obtained six - variable linear regression equation is the corresponding relationship between the increment of polymer - flooding recovery factor and the main factors.

[0072] (6) According to the six - variable linear regression equation obtained in step (5), substitute the specific values of the main factors in the 25 orthogonal test schemes into the six - variable linear regression equation, and calculate the calculated values of the increments of polymer - flooding recovery factors corresponding to the 25 orthogonal test schemes.

[0073] Then, perform a linear fitting analysis on the calculated values and simulated values of the increments of polymer - flooding recovery factors corresponding to the 25 orthogonal test schemes to obtain a linear fitting equation. The fitting result is as Figure 2 shown. The obtained linear fitting equation is: f j = 0.9107×f m + 0.0052; where f j is the calculated value of the increment of polymer - flooding recovery factor, and f m is the simulated value of the increment of polymer - flooding recovery factor; the correlation coefficient R of the linear fitting equation is 0.9605, indicating that there is a good correlation between the calculated value and the simulated value of the increment of polymer - flooding recovery factor. Finally, use the analysis of variance method (F - test method) to test the significance of the linear fitting equation. Calculate that the significance statistic F of the linear fitting equation is 4.97062×10 -9 , which is much smaller than the significance level of 0.05, indicating that the linear fitting equation has a significant effect, confirming that the six - variable linear regression equation obtained in step (5) is relatively accurate and has high reliability. When used to determine the increment of polymer - flooding recovery factor, it can save a large amount of time and has great popularization value.

[0074] (7) Obtain the main factors of the reservoir with high salinity to be predicted, and then substitute the main factors of the reservoir with high salinity to be predicted into the six - variable linear regression equation obtained in step (5) to calculate the increment of polymer - flooding recovery factor of the reservoir with high salinity to be predicted; the values of the main factors of the reservoir with high salinity to be predicted are within the value ranges of the corresponding main factors determined in step (3); that is, the values of the main factors of the reservoir with high salinity to be predicted need to meet the following conditions: reservoir temperature is 60 - 100 °C; formation water salinity is 20000 - 100000 mg / L; viscosity of formation crude oil is 10 - 50 mPa·s; average reservoir permeability is 50 - 450 mD; reservoir permeability variation coefficient is 0.4 - 0.8; polymer injection concentration is 0.05 - 0.25%.

[0075] Experimental example

[0076] To verify the reliability of the method for determining the enhanced recovery value of polymer flooding in high salinity reservoirs in the examples, relevant parameters of reservoir B where polymer flooding has been implemented are collected from high salinity reservoir unit A. The relevant parameters of reservoir B are as follows: reservoir temperature is 72 °C, formation water salinity is 60,200 mg / L, average reservoir permeability is 330 mD, polymer injection concentration is 0.16%, permeability variation coefficient is 0.504, formation crude oil viscosity is 7.63 mPa·s, and the enhanced recovery value of polymer flooding (actual value) is 0.0623.

[0077] Since the values of the main factors of reservoir B are within the ranges of the corresponding main factors determined in step (3) of the examples, therefore, the six - variable linear regression equation determined in the examples can be used to calculate the enhanced recovery value of polymer flooding in reservoir B. Substituting the main factors of reservoir B into the six - variable linear regression equation obtained in the examples, it can be calculated that the enhanced recovery value of polymer flooding is 0.0648. The error between the calculated value and the actual value is 4.0%, indicating that the method for determining the enhanced recovery value of polymer flooding in the examples is reliable and has relatively high accuracy.

[0078] In addition, a large number of experimental simulation results show that when using the principal component analysis method or the entropy weight method to determine the weight coefficients of each factor in step (1) of the examples, or taking the factors with weight coefficients greater than or equal to 0.11 as the main factors, or setting the levels of each main factor in step (3) of the examples to 3 (i.e., using a six - factor three - level orthogonal experiment), the accuracy of the corresponding relationship between the determined enhanced recovery value of polymer flooding and the main factors is poor. When using reservoir B for verification, the errors between the calculated enhanced recovery value of polymer flooding and the actual value of the enhanced recovery value of polymer flooding are all greater than 20%.

Claims

1. A method for determining the enhanced recovery value of polymer flooding in high salinity reservoirs, characterized in that, it includes the following steps: S1. Calculate the weight coefficients of the factors affecting the enhanced recovery of polymer flooding in high salinity reservoirs by using the analytic hierarchy process, and then select the main factors from these factors according to the magnitudes of the weight coefficients; S2. Assign several set values to each of the main factors, and then combine the set values of all the main factors to obtain several different test conditions. Each combination formed by the set values of all the main factors is a test condition; S3. Determine the enhanced recovery value of polymer flooding under each test condition, and then perform a regression analysis on the enhanced recovery value of polymer flooding and the main factors to obtain the corresponding relationship between the enhanced recovery value of polymer flooding and the main factors; finally, based on the main factors of the high salinity reservoir to be predicted and the corresponding relationship, obtain the enhanced recovery value of polymer flooding of the high salinity reservoir to be predicted; the values of the main factors of the high salinity reservoir to be predicted are within the numerical ranges of the corresponding main factors assigned in step S2.

2. The method for determining the enhanced recovery value of polymer flooding in high salinity reservoirs according to claim 1, characterized in that, the factors affecting the enhanced recovery of polymer flooding in high salinity reservoirs include reservoir depth, reservoir temperature, formation water salinity, formation crude oil viscosity, crude oil density, average reservoir permeability, permeability variation coefficient, polymer injection concentration, production degree, and comprehensive water cut.

3. The method for determining the enhanced recovery value of polymer flooding in high salinity reservoirs according to claim 1, characterized in that, the main factors refer to the factors with weight coefficients not less than 0.

1.

4. The method for determining the enhanced recovery value of polymer flooding in high salinity reservoirs according to any one of claims 1 - 3, characterized in that, the orthogonal test method is used to combine the set values of all the main factors.

5. The method for determining the enhanced recovery value of polymer flooding in high salinity reservoirs according to claim 4, characterized in that, the levels of each main factor in the orthogonal test are not less than 5.

6. The method for determining the enhanced recovery value of polymer flooding in high salinity reservoirs according to any one of claims 1 - 3, characterized in that, the regression analysis is a linear regression analysis.

7. The method for determining the enhanced recovery value of polymer flooding in high salinity reservoirs according to any one of claims 1 - 3, characterized in that, the numerical simulation method is used to determine the enhanced recovery value of polymer flooding under each test condition.

8. The method for determining the enhanced recovery value of polymer flooding in high salinity reservoirs according to any one of claims 1 - 3, characterized in that, the method for determining the enhanced recovery value of polymer flooding further includes the following steps: after obtaining the corresponding relationship, test the corresponding relationship. If the test result meets the requirements, then determine the enhanced recovery value of polymer flooding of the high salinity reservoir to be predicted.

9. The method for determining the enhanced recovery value of polymer flooding in high salinity reservoirs according to claim 8, characterized in that, the test includes a correlation test and a significance test.

10. The method for determining the increased value of polymer flooding recovery rate in high salinity reservoirs as described in claim 9, characterized in that, the inspection method includes the following steps: obtaining the calculated value of the increased polymer flooding recovery rate under each test condition according to the values of the main factors in each test condition and the corresponding relationship, and then performing linear fitting on the increased value of polymer flooding recovery rate determined in step S3 and the calculated value of the increased polymer flooding recovery rate; that the inspection result meets the requirements means that the correlation coefficient of the equation obtained by linear fitting analysis is not less than 0.95, and the significance statistic of the equation obtained by linear fitting analysis is less than 0.05.

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