A method for evaluating the effect of gas injection development of offshore oil reservoirs

Abstract

This invention relates to a method for evaluating the development effect of gas injection in offshore low-permeability reservoirs, comprising: S1, constructing multiple indicators for individual evaluation of the development effect of gas injection in offshore low-permeability reservoirs, establishing a graded evaluation set, and determining the correspondence between the quantitative values ​​of each indicator for individual evaluation and the classification level in the graded evaluation set; S2, determining the weight of each indicator for comprehensive evaluation of multiple indicators; S3, establishing a fuzzy comprehensive evaluation matrix for comprehensive evaluation of multiple indicators based on the weight of each indicator; and S4, based on the fuzzy comprehensive evaluation matrix, and according to the principle of maximum membership, using fuzzy mathematical analysis, determining the corresponding development effect classification level under the quantitative values ​​of multiple indicators.

Description

Technical Field

[0001] This invention relates to the field of oil and gas extraction technology, specifically to a method for evaluating the development effect of gas injection in low-permeability offshore reservoirs, belonging to the field of in-depth value mining of big data in offshore oilfield development. Background Technology

[0002] Offshore low-permeability oil reservoirs are rich in reserves, but their narrow pore throats make water injection development difficult. Utilizing the good injectability of injection media such as natural gas, nitrogen, and carbon dioxide, as well as their tendency to dissolve, expand, and reduce viscosity with crude oil, gas injection development can reduce the difficulty of reservoir development.

[0003] Although gas injection development preserves formation energy while exhibiting good displacement effects, making it an important development method for low-permeability reservoirs, the grading and evaluation system for gas injection development effectiveness still needs further improvement and development. Currently, gas-driven development is influenced by many factors, and the development effects vary greatly among different reservoirs; a unified evaluation standard for gas-driven development effectiveness has not yet been established. Summary of the Invention

[0004] This invention provides a method for evaluating the development effect of gas injection in offshore low-permeability reservoirs. A single index for evaluating the development effect is constructed, the weight of each index is determined according to its importance, and a fuzzy comprehensive evaluation matrix is ​​determined. Based on the principle of fuzzy mathematical analysis, the classification level of the development effect evaluation is determined according to the principle of the largest membership degree.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] Firstly, this application provides a method for evaluating the effectiveness of gas injection development in low-permeability offshore reservoirs, including: S1. Construct multiple indicators for separate evaluation of the gas injection development effect of low-permeability offshore reservoirs, establish a graded evaluation set, and determine the correspondence between the quantitative values ​​of each indicator for separate evaluation and the classification level in the graded evaluation set. S2, determine the weight of each indicator used for multi-indicator comprehensive evaluation; S3 employs fuzzy mathematical analysis to establish a fuzzy comprehensive evaluation matrix for multi-indicator comprehensive evaluation based on the weight of each indicator; and S4. Based on the fuzzy comprehensive evaluation matrix, the development effect classification level is determined according to the principle of maximizing membership degree under the quantitative values ​​of multiple indicators.

[0007] In one implementation, S1 includes several indicators constructed for individual evaluation, including: well breakthrough rate, relative oil production from gas drive, recovery rate, cumulative oil replacement rate, cumulative gas storage rate, and formation pressure maintenance level.

[0008] In one implementation, a hierarchical evaluation set is established, including classification levels of development effectiveness at levels one, two, and three.

[0009] In one implementation, in S2, the weight of each indicator is determined using the analytic hierarchy process (AHP).

[0010] In one implementation, the steps of the analytic hierarchy process specifically include: S21, Analyze the relationships between the indicators and establish a hierarchical structure of relative importance; S22, compare each indicator in pairs to construct a judgment matrix formed by the pairwise comparison relationship between the indicators; S23. Perform a consistency check on the formed judgment matrix. If the check passes, calculate the weight of each indicator based on the judgment matrix. Otherwise, reconstruct the indicators, hierarchical relationships, and judgment matrix.

[0011] In one implementation, in step S23, the weights of each indicator are calculated based on the arithmetic mean, geometric mean, or eigenvalue method.

[0012] In one implementation, consistency checks are performed based on multiple preset check indicators.

[0013] In one implementation, in S3, a trapezoidal membership function is used to calculate the fuzzy comprehensive evaluation matrix for multi-index comprehensive evaluation.

[0014] This invention addresses the challenges of objectively and comprehensively evaluating the development effects of gas-driven reservoirs, which are influenced by numerous factors and exhibit significant differences in development outcomes across different reservoirs. Based on a mathematical method using maximum membership, it comprehensively evaluates the development effects of gas-driven reservoirs in low-permeability areas using six key parameters: well breakthrough rate, relative oil gain from gas drive, recovery rate, cumulative oil exchange rate, cumulative gas storage rate, and formation pressure maintenance level. This evaluation guides the summary and comparison of gas-driven reservoir development effects and the screening of potential low-permeability gas-driven reservoirs. Attached Figure Description

[0015] Figure 1 This is a flowchart of a method for evaluating the gas injection development effect of a low-permeability offshore oil reservoir according to an embodiment of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0017] To address the shortcomings and problems of existing technologies, this application provides a method for evaluating the effectiveness of gas injection development in offshore low-permeability reservoirs, including: S1. Construct multiple indicators for separate evaluation of the gas injection development effect of low-permeability offshore reservoirs, establish a graded evaluation set, and determine the correspondence between the quantitative values ​​of each indicator for separate evaluation and the classification level in the graded evaluation set. S2, determine the weight of each indicator used for multi-indicator comprehensive evaluation; S3 employs fuzzy mathematical analysis to establish a fuzzy comprehensive evaluation matrix for multi-indicator comprehensive evaluation based on the weight of each indicator; and S4. Based on the fuzzy comprehensive evaluation matrix, the development effect classification level is determined according to the principle of maximizing membership degree under the quantitative values ​​of multiple indicators.

[0018] The above method is described below in a more detailed embodiment with reference to more accompanying drawings.

[0019] This invention proposes a method for grading and evaluating the development effects of different types of offshore oil fields. The specific steps are as follows: S1. Construct multiple indicators for separate evaluation of the gas injection development effect of low-permeability offshore reservoirs, establish a graded evaluation set, and determine the correspondence between the quantitative values ​​of each indicator for separate evaluation and the classification level in the graded evaluation set. Specifically, six evaluation indicators were selected to assess the effectiveness of gas drive development, including well breakthrough rate, relative oil production increase from gas drive, recovery rate, cumulative oil replacement rate, cumulative gas storage rate, and formation pressure maintenance level.

[0020] First, let me explain that the graded evaluation set in this embodiment includes three levels of development effect: Level 1, Level 2, and Level 3, with Level 1 representing the highest level of development effect and Level 2 being the next highest.

[0021] Further explanations of each indicator are as follows: (1) Oil well efficiency Oil well efficiency refers to the proportion of wells that have benefited from gas drive development out of the total number of wells evaluated. The higher the oil well efficiency, the more uniform the gas drive progresses and the better the effect.

[0022] The calculation formula is:

[0023] in: —Oil well efficiency, in % —Number of gas injection wells that participated in the evaluation, unit: wells; —Total number of wells participating in the evaluation, unit: wells.

[0024] The following table shows the correspondence between the well breakthrough rate and the development effect classification results under a single indicator evaluation:

[0025] (2) Relative increase in fuel volume due to air drive The relative increase in oil production from gas-driven development refers to the ratio of the difference in production between gas injection and water injection in an oil reservoir to the production from water injection development. It can be used to describe changes in oil production. The greater the relative increase in oil production from gas-driven development, the greater the daily oil production after gas-driven development, and the better the production enhancement effect. The oil production from water injection development can be predicted using water drive curves and numerical simulations.

[0026] The calculation formula is:

[0027] in: —Relative increase in fuel consumption due to air drive, % —Gas-driven development output, t; —Water-driven development output, t.

[0028] Compared to water-driven systems, gas-driven systems have higher development costs, and to achieve economic benefits, they need to achieve a significant increase in fuel production. A summary of research findings reveals that when gas-driven development is successful, the increase in fuel production is approximately 50%; when the effect is poor, it is often less than 20%. Therefore, the evaluation criteria for the relative increase in fuel production from gas-driven systems are shown in the table below.

[0029]

[0030] (3) Recovery rate Oil recovery rate refers to the ratio of the amount of crude oil extracted to the original geological reserves of the reservoir, usually expressed as a percentage. The calculation formula is:

[0031] The calculation formula is: in: —Recovery rate, % —Amount of crude oil extracted, in tons; —Original geological reserves, t.

[0032] Generally, waterflooding recovery rates in low-permeability reservoirs are around 25%, while based on statistics from successfully implemented gasflooding operations, the increase in recovery rate is typically between 5% and 15%. Therefore, the evaluation criteria for gasflooding recovery rate are shown in the table below.

[0033]

[0034] (4) Cumulative oil change rate Cumulative oil change rate refers to the gas volume required to add one ton of oil, and is an important parameter for evaluating the effectiveness of gas drive. The oil change rate monitors the utilization efficiency of the gas; a lower oil change rate indicates better gas drive development, while a continuously increasing oil change rate signifies that the gas drive oil has become ineffective. The calculation formula is:

[0035] in: —Cumulative oil change rate, m 3 / t; —Cumulative gas injection volume for gas drive, m 3 ; —Cumulative oil production after gas drive, m.

[0036] According to statistics, the cumulative total oil change rate is typically between 1500-2500 m. 3 The cumulative oil change rate evaluation criteria are obtained between / t, as shown in Table 4.

[0037]

[0038] (5) Cumulative gas storage rate The cumulative gas storage ratio is the ratio of the difference between the cumulative gas injection volume and the cumulative gas production volume to the cumulative gas injection volume. It is an indicator of gas injection utilization and an important parameter for evaluating gas drive performance. A higher cumulative gas storage ratio indicates a higher utilization rate of the injected gas and a better gas drive development effect.

[0039] The calculation formula is:

[0040] in: —Cumulative gas storage rate; —Cumulative gas injection volume, m 3 ; —Cumulative volume of injected gas produced, m 3 .

[0041] Based on industry standards and CO2 miscible drive development effect evaluation standards, the cumulative gas storage rate evaluation standard is obtained, as shown in the table below.

[0042]

[0043] (6) Formation pressure remains horizontal Formation pressure maintenance level is the ratio of current formation pressure to original formation pressure, mainly reflecting the degree of formation pressure maintenance and whether the required drainage volume is met at this pressure level. As the formation pressure maintenance level increases, the gas drive development effect will gradually increase. When the formation pressure reaches a certain reasonable level, further increases in formation pressure will have little impact on oil recovery.

[0044] The calculation formula is:

[0045] in: —Formation pressure remains at a level, % —Current formation pressure; —Original formation pressure.

[0046] For gas drive development, formation pressure directly affects the development effect, and a reasonable formation pressure level will determine the development effect to a certain extent. Combining industry standards and CO2 miscible flooding development effect evaluation standards, the formation pressure maintenance level evaluation standards are obtained, as shown in the table below.

[0047]

[0048] S2, determine the weight of each indicator used for multi-indicator comprehensive evaluation; Fuzzy comprehensive evaluation is a comprehensive evaluation method based on the principles of fuzzy mathematics, primarily used to solve evaluation problems that are difficult to describe with precise mathematical models. By quantifying qualitative evaluation, it ensures that the evaluation process considers both the importance of each factor and the fuzziness and uncertainty between them, thus better reflecting the actual situation. Combining the fuzzy comprehensive evaluation method with the principle of maximum membership, a comprehensive evaluation method for the effectiveness of gas-driven development is established. The steps of the fuzzy comprehensive evaluation are as follows: ① Set the evaluation index factor set ; ② Set up a comment collection ; ③ Determine the weight set of evaluation indicators ; ④ Establish a fuzzy comprehensive evaluation matrix ; ⑤ Comprehensive evaluation, making a judgment based on the principle of maximizing membership degree.

[0049] The maximum membership principle means that, among the results of fuzzy comprehensive evaluation, the evaluation level with the highest membership degree is selected as the final evaluation or decision result. Simply put, among all possible choices or evaluation results, the level corresponding to the highest membership function value is selected as the final decision or evaluation result.

[0050] Based on the above classification of gas-driven development effects, the factor set consists of individual indicators, and the evaluation set consists of categories. The weights of the evaluation indicators are determined using the Analytic Hierarchy Process (AHP). The AHP is a structured decision analysis method that breaks down complex decision problems into smaller parts, analyzes and evaluates each part separately, and then synthesizes the results to make a final decision. The advantages of the AHP lie in its structured analytical framework and its ability to reasonably handle qualitative and quantitative factors. However, it is important to note that this method may be influenced by subjective judgment in some cases; therefore, consistency checks are particularly important to ensure the scientific and rational nature of the decision. The process of obtaining the weights of each factor using the AHP is as follows: 1) Analyze the relationships between the various factors in the system and establish a hierarchical structure.

[0051] This hierarchical structure is used to determine the weight of each factor in the effectiveness of reservoir development.

[0052] 2) Construct the judgment matrix The evaluation indicators are compared in pairs, using a nine-point scale (as shown in Table 4-10 below).

[0053]

[0054] Construct the judgment matrix as shown in the table below.

[0055]

[0056] 3) Consistency check Consistency index (CI) and consistency ratio (CR) are important indicators used to measure the degree of consistency of the judgment matrix. Their role is to ensure that the consistency of the judgment matrix is ​​within a reasonable range, thereby making the results obtained by the analytic hierarchy process more reliable and effective.

[0057] Consistency Index (CI) measures the degree to which a judgment matrix changes from perfect consistency to complete inconsistency. In AHP, the consistency of the judgment matrix is ​​determined by comparing the relationship between the largest eigenvalue of the judgment matrix and the matrix order. The consistency index CI is calculated as follows:

[0058] in: —Determine the largest eigenvalue of the matrix; n —Determine the order of the matrix.

[0059] Ideally, the judgment matrix should be completely consistent, in which case λmax equals n, and CI equals 0, indicating that the judgments are completely consistent. The larger the value of CI, the worse the consistency of the judgment matrix.

[0060] CR is the ratio of CI to the Random Consistency Index (RI), used to measure whether the consistency of a judgment matrix is ​​within an acceptable range. Calculate the consistency ratio CR:

[0061] RI is a pre-calculated average random consistency index based on the matrix order, used to represent the average consistency level of a completely random decision matrix. Generally, if CR is less than or equal to 0.1, the consistency of the decision matrix is ​​considered acceptable; if CR is greater than 0.1, the decision matrix needs to be re-evaluated to improve its consistency.

[0062] The following diagram illustrates the relationship between n and RI:

[0063] 4) Analysis of Calculation Results The consistency index (CI) was 0.0198, and the consistency ratio (CR) was 0.0157. Since CR < 0.10, it indicates that the consistency of the judgment matrix is ​​acceptable.

[0064] When using the Analytic Hierarchy Process (AHP) to solve problems, a single method is often used to calculate the weights, but different calculation methods may lead to deviations in the results. To ensure the robustness of the results, the arithmetic mean, geometric mean, and eigenvalue method are used to calculate the weights and then calculate the average value. This avoids the bias caused by using a single method, and the conclusions obtained are more comprehensive and effective.

[0065] The arithmetic mean method calculates the arithmetic mean of the elements in each column of the judgment matrix and then normalizes each mean to obtain the weight vector. The geometric mean method calculates the geometric mean of the elements in each row of the judgment matrix and then normalizes these geometric means to obtain the weight vector. The eigenvalue method finds the eigenvector corresponding to the largest eigenvalue of the judgment matrix and normalizes the eigenvector to obtain the weight vector. The calculated weights of each indicator are shown in the table below.

[0066]

[0067] Therefore, the weight set A = [0.1274, 0.1936, 0.3106, 0.1521, 0.1361, 0.0801] is obtained.

[0068] S3. Using fuzzy mathematical analysis, a fuzzy comprehensive evaluation matrix for multi-indicator comprehensive evaluation is established based on the weight of each indicator. To calculate the evaluation matrix, a trapezoidal membership function is used. The trapezoidal membership function is a function in fuzzy mathematics used to describe the membership degree of elements in a fuzzy set. It is widely used in fuzzy logic and fuzzy systems, especially in fuzzy control and fuzzy decision support systems. Unlike traditional binary logic, fuzzy logic allows the membership degree of an element to vary between completely belonging (membership degree of 1) and completely not belonging (membership degree of 0). The trapezoidal membership function is one of the mathematical expressions of this fuzziness. The trapezoidal membership function has high practical value in processing fuzzy information and fuzzy decision-making, and can effectively describe and handle uncertainty and fuzziness problems.

[0069] The trapezoidal membership function is defined by four parameters a, b, c, and d, where a ≤ b ≤ c ≤ d. The value of the membership function changes depending on the input value, and its function distribution is as follows:

[0070]

[0071]

[0072] The evaluation matrix R is obtained according to the trapezoidal membership function; S4. Based on the fuzzy comprehensive evaluation matrix, the development effect classification level is determined according to the principle of maximizing membership degree under the quantitative values ​​of multiple indicators.

[0073] Specifically, based on the weight set mentioned above, the membership degree is calculated as: U = A x R.

[0074] The final results show the membership parameters for development effects at levels one, two, and three. The development effect at level one has the highest membership degree; therefore, based on the principle of maximum membership degree, the overall evaluation of this development effect is level one, indicating that the gas-driven development effect is good.

[0075] In summary, this invention addresses the challenges of objectively and comprehensively evaluating the development effects of gas-driven reservoirs, which are influenced by numerous factors and exhibit significant differences in development outcomes across different reservoirs. Based on a mathematical method using maximum membership, it comprehensively evaluates the development effects of low-permeability gas-driven reservoirs using six key parameters: well breakthrough rate, relative oil gain from gas-driven reservoirs, recovery rate, cumulative oil exchange rate, cumulative gas storage rate, and formation pressure maintenance level. This evaluation guides the summary and comparison of gas-driven reservoir development effects and the screening of potential low-permeability gas-driven reservoirs.

[0076] In the several embodiments provided by this invention, it should be understood that the disclosed methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0077] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. 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 evaluating the effectiveness of gas injection development in low-permeability offshore reservoirs, characterized in that, include: S1. Construct multiple indicators for separate evaluation of the gas injection development effect of low-permeability offshore reservoirs, establish a graded evaluation set, and determine the correspondence between the quantitative values ​​of each indicator for separate evaluation and the classification level in the graded evaluation set. S2, determine the weight of each indicator used for multi-indicator comprehensive evaluation; S3. Using fuzzy mathematical analysis, a fuzzy comprehensive evaluation matrix for multi-indicator comprehensive evaluation is established based on the weight of each indicator. as well as S4. Based on the fuzzy comprehensive evaluation matrix, the development effect classification level is determined according to the principle of maximizing membership degree under the quantitative values ​​of multiple indicators.

2. The method for evaluating the gas injection development effect of offshore low-permeability oil reservoirs according to claim 1, characterized in that, In S1, several indicators are constructed for individual evaluation, including: well breakthrough rate, relative oil production from gas drive, recovery rate, cumulative oil replacement rate, cumulative gas storage rate, and formation pressure maintenance level.

3. The method for evaluating the gas injection development effect of offshore low-permeability oil reservoirs according to claim 2, characterized in that, The established tiered evaluation system includes classification levels for development effectiveness at levels one, two, and three.

4. The method for evaluating the gas injection development effect of offshore low-permeability oil reservoirs according to claim 1, characterized in that, In S2, the weight of each indicator is determined using the analytic hierarchy process (AHP).

5. The method for evaluating the gas injection development effect of offshore low-permeability oil reservoirs according to claim 4, characterized in that, The steps of the analytic hierarchy process specifically include: S21, Analyze the relationships between the indicators and establish a hierarchical structure of relative importance; S22, compare each indicator in pairs to construct a judgment matrix formed by the pairwise comparison relationship between the indicators; S23. Perform a consistency check on the formed judgment matrix. If the check passes, calculate the weight of each indicator based on the judgment matrix. Otherwise, reconstruct the indicators, hierarchical relationships, and judgment matrix.

6. The method for evaluating the gas injection development effect of offshore low-permeability oil reservoirs according to claim 5, characterized in that, In step S23, the weights of each indicator are calculated based on the arithmetic mean, geometric mean, or eigenvalue method.

7. The method for evaluating the gas injection development effect of offshore low-permeability oil reservoirs according to claim 5, characterized in that, In the consistency test, the test operation is carried out based on multiple preset test indicators.

8. The method for evaluating the gas injection development effect of offshore low-permeability oil reservoirs according to claim 1, characterized in that, In S3, the trapezoidal membership function is used to calculate the fuzzy comprehensive evaluation matrix for multi-index comprehensive evaluation.

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