Relative permeability curve calculation method

Abstract

A relative permeability curve calculation method belongs to the technical field of petroleum and natural gas exploration and development, and comprises the following steps: collecting and sorting basic oil reservoir parameters and dynamic production data; the corresponding moisture content is calculated by using production data, and a relative permeability calculation formula is optimized for optimization of different types of oil reservoirs; parameterizing the relative permeability, and establishing an optimal mathematical model by taking the actual production water content as a fitting target; solving an optimal solution of the nonlinear function with constraint conditions by using a genetic algorithm, and finding the optimal solution through multiple iterations; and S4, calculating the relative permeability curve according to the results obtained in the steps S2, S3 and S4, and determining the relative permeability curve adapted to the current oilfield development form. According to the method, the limitation caused by experimental testing and the influence of reservoir change in the middle and later periods of development on the relative permeability are overcome, not only can the relative permeability ratio be obtained, but also the relative permeability value can be obtained, and the method has certain representativeness.

Description

Technical Field

[0001] The invention belongs to the technical field of oil and gas exploration and development, and in particular relates to a relative permeability curve calculation method. Background Art

[0002] In the middle and late stages of oilfield development, the reservoir lithology, physical properties, fluid properties, microscopic pore throat structure, fluid seepage characteristics, etc. have undergone varying degrees of change due to the influence of reservoir fracturing, matrix fracture changes, water injection flushing, reservoir sensitivity, and other production-increasing measures. It is generally difficult to obtain a relative permeability curve that can currently represent reservoir characteristics through experiments. A relative permeability curve calculation method is urgently needed to reflect the actual production dynamics of the oilfield and provide an accurate relative permeability curve for production and scientific research. Summary of the invention

[0003] In order to solve the above problems, the present invention proposes: a relative permeability curve calculation method, comprising the following steps:

[0004] S1. Collect and organize basic reservoir parameters and dynamic production data;

[0005] S2. Calculate the corresponding water content using production data, and select and optimize the relative permeability calculation formula for different types of reservoirs;

[0006] S3. Parameterize the relative permeability, take the actual production water content as the fitting target, and establish the optimal mathematical model;

[0007] S4, using genetic algorithm to solve the optimal solution of nonlinear function with constraints, and find the optimal solution through multiple iterations;

[0008] S5. Calculate the relative permeability curve according to the results obtained in steps S2, S3 and S4 to determine a relative permeability curve suitable for the current oil field development form.

[0009] Furthermore, in step S1, the main reservoir basic parameters required are summarized as follows: residual oil saturation, irreducible water saturation, oil viscosity, water viscosity, oil density, oil volume coefficient, water volume coefficient; the main dynamic parameters required are: recovery degree, actual water content.

[0010] Furthermore, in step S2, the result of step S1 is used to calculate the current average water content through the current cumulative production of the oil field, and the key parameters are solved and the relative permeability is normalized using the preferred relative permeability calculation formula.

[0011] Furthermore, in step S3, the result of step S2 is used to establish a target optimization mathematical model, parameterize the relative permeability, take the actual production water content as the fitting target, and establish the optimal objective function.

[0012] Furthermore, in step S4, the result of step S3 is used to solve the nonlinear optimization problem with constraints using the objective function, and a genetic algorithm is used to find the global optimal solution through multiple iterations.

[0013] Furthermore, in step S5, according to the conclusions obtained in steps S2, S3 and S4, based on production data and taking comprehensive water content as the objective function, a genetic algorithm is used to solve and obtain necessary parameters of relative permeability, and the effect of the fitting can be seen from the fitting curve of water content and degree of recovery.

[0014] The beneficial effects of the present invention are: optimizing the relative permeability calculation formula by using the basic parameters and dynamic data of the oil reservoir, establishing the optimal mathematical model and applying the genetic algorithm to obtain the global optimal solution, and solving the necessary parameters of the relative permeability. This method can overcome the limitations brought by experimental testing and the influence of reservoir changes in the middle and late stages of development on the relative permeability, and can not only obtain the relative permeability ratio, but also the relative permeability value, which has a certain representativeness. On this basis, the development indicators of the oil field can also be predicted and the development effect can be evaluated. This method is suitable for oil fields with low permeability sandstone reservoirs. According to this set of technical ideas and technical methods, the development effect of the oil field can be evaluated, and an efficient water drive adjustment policy can be formulated and studied. By formulating effective technical countermeasures, the recovery rate can be greatly improved, the recoverable reserves can be increased, and the production capacity can be efficiently exerted, which has a huge effect on the stable development of the oil field. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the steps of the present invention;

[0016] Figure 2 It is a water content and recovery degree curve diagram of the present invention;

[0017] Figure 3 This is a graph showing the calculated relative permeability of the present invention. DETAILED DESCRIPTION

[0018] In order to make the technical means and objectives of the present invention easy to understand, the present invention is further described below in combination with specific implementation methods. A relative permeability curve calculation method, as shown in Figure 1, includes the following steps:

[0019] S1 collects and organizes basic reservoir parameters and dynamic production data;

[0020] S2, based on the step S1, using the production data to calculate the corresponding water content, and selecting and optimizing the relative permeability calculation formula for different types of reservoirs;

[0021] S3, based on the step S2, parameterize the relative permeability, take the actual production water content as the fitting target, and establish an optimized mathematical model;

[0022] S4, based on the step S3, using a genetic algorithm to solve the optimal solution of the nonlinear function with constraints, and finding the optimal solution through multiple iterations;

[0023] S5: Calculate the relative permeability curve according to the results obtained in steps S2, S3 and S4 to determine the relative permeability curve suitable for the current oil field development form.

[0024] Furthermore, the step S1 comprises:

[0025] The main basic reservoir parameters required are summarized as follows: residual oil saturation, bound water saturation, oil viscosity, water viscosity, oil density, oil volume coefficient, water volume coefficient; the main dynamic parameters required are: recovery degree and actual water content.

[0026] Furthermore, the step S2 comprises:

[0027] Using the result of step S1, the current average water content is calculated through the current cumulative production of the oil field, and the key parameters are solved and the relative permeability is normalized using the preferred relative permeability calculation formula.

[0028] Furthermore, the step S3 comprises:

[0029] Using the result of step S2, a target optimization mathematical model is established, the relative permeability is parameterized, the actual production water content is used as the fitting target, and the optimal target function is established.

[0030] Furthermore, the step S4 comprises:

[0031] Using the result of step S3, the objective function is used to solve the nonlinear optimal problem with constraints. Traditional solutions such as iterative algorithms and direct search methods have certain limitations. This time, a genetic algorithm is used to find the global optimal solution through multiple iterations.

[0032] Furthermore, the step S5 comprises:

[0033] According to the conclusions obtained in step S2, step S3 and step S4,

[0034] Using the above method and production data, with comprehensive water content as the objective function, the genetic algorithm is used to solve the necessary parameters of relative permeability. It can be seen from the fitting curve of water content and recovery degree that the fitting effect is good.

[0035] The annual water content of a certain oil field has reached 88.6% since it was put into production. The basic parameters are shown in Table 1, and the water content data are shown in Table 2.

[0036] According to the above production data, the method in this paper takes the comprehensive water content as the objective function and uses the genetic algorithm to solve the fitting curve between water content and recovery degree as shown in the following figure: Figure 2 As shown, from Figure 2 It can be seen from the figure that the fitting effect is good, and the calculated relative permeability curve is as follows: Figure 3 shown.

[0037] Table 1 Basic data table

[0038]

[0039] Table 2 Production data

[0040]

[0041] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solutions and concepts of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A relative permeability curve calculation method, characterized in that: The steps include: S1. Collect and organize basic reservoir parameters and dynamic production data; S2. Calculate the corresponding water content using production data, and select and optimize the relative permeability calculation formula for different types of reservoirs; S3. Parameterize the relative permeability, take the actual production water content as the fitting target, and establish the optimal mathematical model; S4, using genetic algorithm to solve the optimal solution of nonlinear function with constraints, and find the optimal solution through multiple iterations; S5. Calculate the relative permeability curve according to the results obtained in steps S2, S3 and S4 to determine a relative permeability curve suitable for the current oil field development form.

2. The relative permeability curve calculation method according to claim 1, characterized in that: In step S1, the basic reservoir parameters required are summarized as follows: residual oil saturation, irreducible water saturation, oil viscosity, water viscosity, oil density, oil volume coefficient, water volume coefficient; the main dynamic parameters required are: recovery degree, actual water content.

3. The relative permeability curve calculation method according to claim 2, characterized in that: In step S2, the result of step S1 is used to calculate the current average water content through the current cumulative production of the oil field.

4. The relative permeability curve calculation method according to claim 3, characterized in that: The relative permeability calculation formula is used to solve the key parameters and normalize the relative permeability.

5. The relative permeability curve calculation method according to claim 4, characterized in that: In step S3, the result of step S2 is used to establish a target optimization mathematical model, parameterize the relative permeability, take the actual production water content as the fitting target, and establish the optimal target function.

6. The relative permeability curve calculation method according to claim 5, characterized in that: In step S4, the result of step S3 is used to solve the nonlinear optimization problem with constraints using the objective function.

7. The relative permeability curve calculation method according to claim 6, characterized in that: A genetic algorithm is used to find the global optimal solution through multiple iterations.

8. The relative permeability curve calculation method according to claim 7, characterized in that: In step S5, according to the conclusions obtained in steps S2, S3 and S4, according to production data, with comprehensive water content as the objective function, a genetic algorithm is used to solve and obtain the necessary parameters of relative permeability, and the effect of the fitting can be seen from the fitting curve of water content and recovery degree.

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