A reservoir engineering method for quantitative characterization of reservoir macropore parameters

Abstract

The present invention relates to a reservoir engineering method for quantitatively characterizing reservoir macropore parameters, comprising the following steps: a. measuring the daily liquid production, daily oil production and daily water production of a multi-layer combined production well during a production period; b. analyzing the data collected in step a to determine whether the collected data can be used for quantitative analysis of reservoir macropore parameters; c. substituting the data that can be used for quantitative analysis of reservoir macropore parameters in step b into calculation to quantitatively characterize reservoir macropore parameters; d. selecting a profile adjustment and water plugging agent based on the reservoir macropore parameters quantitatively characterized in step c, so as to provide a basis for targeted water plugging operations in the next step, reduce water content and increase oil production. The method of the present invention is not only simple and feasible to operate, but also significantly reduces costs and is easy to promote and apply.

Description

Technical Field

[0001] The invention relates to an oil reservoir engineering method for quantitatively characterizing reservoir macropore parameters, belonging to the technical field of oil development. Background Art

[0002] After decades of water flooding development, most oil fields in my country are about to enter the "high water content" or even "ultra-high water content" period. Some of these oil fields have entered the middle and late stages of development. At this stage, oil well production faces many problems such as high liquid production accompanied by high water content, rapid increase in water-oil ratio, and increasing difficulty in stabilizing oil and controlling water. In water-flooding oil fields, a large amount of injected water flows rapidly from the water well end to the oil well end along the low resistance area of ​​liquid flow developed in the oil layer, resulting in the increasingly serious phenomenon of inefficient or even ineffective water injection circulation between oil and water wells. In addition, there is a lack of quantitative characterization methods for large pore parameters. The large pore parameters of reservoirs in different blocks vary greatly. If the specific parameters cannot be determined, it is difficult to provide a reliable basis for the next step of water plugging and profile control. Therefore, a method for quantitative characterization of reservoir large pore parameters is proposed to provide a theoretical basis for taking targeted water plugging and profile control measures for large pores of different levels.

[0003] At present, the main methods for quantitative characterization of large pores include logging curve inversion, coring data identification, mathematical decision theory identification, etc., but each method has great limitations in application: the logging curve inversion method can only reflect the development of large pores within a few meters near the wellbore, and the construction period is long; the coring data identification method has high operation costs, and multiple operations are required to obtain a more comprehensive understanding, and the overall application well number is small; the mathematical decision method introduces many human factors and has large errors. Reservoir dynamics is a comprehensive reflection of the "injection well-production well-reservoir" situation, which can reflect the development of large pores in the reservoir to a certain extent. Therefore, it is considered to use reservoir engineering methods to quantitatively characterize the parameters of large pores. According to the different development conditions of large pores, the next step is to select profile adjustment or plugging agents to alleviate interlayer contradictions, improve the plane and longitudinal sweep coefficients, and make all directions evenly affected, thereby reducing subsequent construction costs and achieving the goal of reducing costs and increasing efficiency. However, there are currently many methods for qualitative description of reservoir engineering with large pore development, but few methods for quantitative characterization of reservoir engineering. In addition, some methods have applicability issues, making it difficult to accurately characterize the quantitative parameters of large pores. Therefore, it is urgent to propose a reservoir engineering method for quantitatively characterizing large pore parameters. Summary of the invention

[0004] In response to the above-mentioned prominent problems, the present invention provides a reservoir engineering method for quantitatively characterizing the parameters of reservoir large pores. This method can calculate parameters such as the permeability and pore throat radius of the reservoir large pores. It is not only simple and feasible to operate, but also significantly reduces the cost and is easy to promote and apply.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A reservoir engineering method for quantitatively characterizing reservoir macropore parameters comprises the following steps:

[0007] a. Measure the daily liquid production, daily oil production and daily water production of multi-layer commingled production wells during a production period;

[0008] b. analyzing the data collected in step a to determine whether the collected data can be used for quantitative analysis of reservoir macropore parameters;

[0009] c. Substituting the data in step b that can be used for quantitative analysis of reservoir macropore parameters into calculation to quantitatively characterize the reservoir macropore parameters;

[0010] d. According to the reservoir macropore parameters quantitatively characterized in step c, a profile control and water plugging agent is selected to provide a basis for carrying out targeted water plugging operations in the next step, reducing water content and increasing oil production, so as to guide horizontal well development and improve the utilization of reserves.

[0011] In the reservoir engineering method, preferably, the reservoir macropore parameters in step c include macropore permeability, macropore radius, and macropore volume.

[0012] In the reservoir engineering method, preferably, step c comprises the following specific steps:

[0013] c1 respectively measures the oil-water relative permeability curves at different development periods to determine the relative permeability of the water phase at different periods, and then calculates the water phase permeability at different periods. The specific formula is as follows:

[0014] K wi =KgK rwi (1)

[0015] Among them, K wi is the water phase permeability; K is the reservoir permeability; K rwi is the relative permeability of water phase;

[0016] Calculation of c2 theoretical water production:

[0017]

[0018] Among them, q si is the theoretical water production; K wi is the water phase permeability; h is the formation thickness; △p is the injection-production pressure difference; μ w is the viscosity of the water phase; r e is the distance between injection and production wells; r w is the well radius;

[0019] Calculation of macro invalid water injection volume:

[0020] q sdh =q act -q si (3)

[0021] Among them, q sdh is the macroscopic ineffective water injection volume; q act is the actual water injection volume; q si is the theoretical water production;

[0022] c4 Calculation of microscopic invalid water injection volume;

[0023]

[0024] Among them, q sdw is the microscopic invalid water injection volume; r d is the radius of the large pore; K d is the macropore permeability; μ d is viscosity; dp / dx is the pressure gradient between injection and production wells; n is the seepage index, 1 / 2≤n≤1;

[0025] c5 uses the mutation theory method to split the multi-layer yield of each layer in the vertical direction;

[0026] Calculation of C6 macropore permeability:

[0027]

[0028] Among them, K di is the macropore permeability of the i-th layer; q sdi is the macroscopic ineffective water injection volume split to the i-th layer; is the porosity of the i-th layer; μ d is viscosity; τ is tortuosity; L is the distance between injection and production wells; △p is the injection-production pressure difference;

[0029] Calculation of c7 large channel radius:

[0030]

[0031] Among them, r di is the radius of the large pore calculated based on the macroscopic ineffective water injection volume split to the i-th layer, q sdi is the macroscopic ineffective water injection amount split to the i-th layer; μ d is viscosity; is the porosity of the i-th layer; τ is the tortuosity; L is the distance between the injection and production wells; △p is the injection and production pressure difference;

[0032] Calculation of c8 macropore volume:

[0033]

[0034] Among them, V di is the volume of the macropores in the i-th layer, in m 3 ; L is the distance between injection and production wells, in m; r d is the radius of the large pore, in μm.

[0035] In the reservoir engineering method, preferably, step c3 comprises the following specific steps:

[0036] c31 Collection, collation and analysis of basic data, including static parameters, reservoir fluid parameters, rock fluid parameters and production dynamic parameters required for quantitative characterization of reservoir macropore parameters;

[0037] Calculation of macro invalid water injection volume of C32:

[0038] q sd =q act -q si (8)

[0039] Among them, q sd is the macroscopic ineffective water injection volume; q act is the actual water injection volume; q si The theoretical water production.

[0040] In the reservoir engineering method, preferably, in step c31, the static parameters include stratification data and sub-layer data, and the sub-layer data include sand layer thickness, top and bottom surface depth, effective thickness, porosity and permeability; the reservoir fluid parameters and the rock fluid parameters include oil-water-gas density, volume coefficient, viscosity, crude oil high-pressure physical properties and rock compression coefficient; the production dynamic parameters include daily oil production, daily liquid production, wellhead pressure, bottom hole flow pressure and measures report.

[0041] In the reservoir engineering method, preferably, in step c5, the step of performing multi-layer production splitting on each vertical layer using the catastrophe theory method comprises:

[0042] c51 According to the actual situation of the research objectives, select representative dynamic and static evaluation indicators and appropriate mutation models. The commonly used mutation models in engineering are mainly cusp, swallowtail and butterfly mutation models;

[0043] C52 is normalized for different mutation models and expressed as follows:

[0044] Cusp mutation:

[0045]

[0046] Swallowtail mutation:

[0047]

[0048] Butterfly mutation:

[0049]

[0050] c53 Obtain the target value W of each production layer i :

[0051]

[0052] Among them, when n=2, it is a cusp mutation model, when n=3, it is a swallowtail mutation model, and when n=4, it is a butterfly mutation model;

[0053] c54 calculate the splitting coefficient A of each production layer i :

[0054]

[0055] Among them, W i is the target value of the i-th layer; W′ is the target value of the relative mutation surface; n is the number of small layers.

[0056] In the reservoir engineering method, preferably, in step c6, the calculation of macropore permeability comprises the following steps:

[0057] c61 Collection, collation and analysis of basic data, including static parameters, reservoir fluid parameters, rock fluid parameters and production dynamic parameters required for quantitative characterization of reservoir macropore parameters;

[0058] Calculation of C62 macropore permeability:

[0059]

[0060] Among them, K di is the macropore permeability of the i-th layer; q sdi is the ineffective water injection amount split to the i-th layer; is the porosity of the i-th layer; μ d is viscosity; τ is tortuosity; L is the distance between injection and production wells; △p is the injection-production pressure difference.

[0061] In the reservoir engineering method, preferably, in step c61, the static parameters include stratification data and sub-layer data, and the sub-layer data include sand layer thickness, top and bottom surface depth, effective thickness, porosity and permeability; the reservoir fluid parameters and the rock fluid parameters include oil-water-gas density, volume coefficient, viscosity, crude oil high-pressure physical properties and rock compression coefficient; the production dynamic parameters include daily oil production, daily liquid production, wellhead pressure, bottom hole flow pressure and measures report.

[0062] In the reservoir engineering method, preferably, in step c62, the Carman-Kozeny formula is:

[0063]

[0064] Among them, r d is the radius of the large pore; K d is the macropore permeability; τ is the tortuosity; is the porosity.

[0065] In the reservoir engineering method, preferably, step c8 calculates the large pore volume based on the calculation result of step c7 and the tubular model.

[0066] The present invention adopts the above technical solution, which has the following advantages:

[0067] 1. The reservoir engineering method for quantitative characterization of reservoir macropores provided by the present invention can calculate parameters such as permeability and pore throat radius of reservoir macropores. It is not only simple and feasible to operate, but also significantly reduces costs and is easy to promote and apply.

[0068] 2. The present invention directly uses dynamic data to quantitatively characterize the parameters of reservoir macropores, which is simple, convenient and easy to implement. The calculation results provide great convenience for the next step of selecting targeted agents for macropores of different magnitudes and carrying out profile adjustment and water plugging operations, thereby achieving the purpose of expanding the swept volume and increasing production.

[0069] 3. In order to solve the problems of low distribution accuracy and failure to consider dynamic factors in the splitting process of multi-layer production (injection volume) of multi-layer combined production wells in heterogeneous formations, the present invention proposes a multi-layer production (injection volume) splitting method based on the mutation theory method, thereby improving the reliability and accuracy of splitting.

[0070] 4. The present invention does not require additional on-site operations and tests, thus reducing construction costs and operation cycles, thereby achieving the purpose of reducing costs and increasing efficiency. The method of the present invention can be widely used in the study of quantitative characterization of reservoir macropore parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 It is a schematic flow chart of a reservoir engineering method for quantitatively characterizing reservoir macropore parameters provided by one embodiment of the present invention;

[0072] Figure 2 Schematic diagram of the multi-layer splitting effect of the catastrophe theory method provided in this embodiment of the present invention. DETAILED DESCRIPTION

[0073] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention is described clearly and completely below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work are within the scope of protection of the present invention.

[0074] like Figure 1 As shown, the present invention provides a reservoir engineering method for quantitatively characterizing reservoir macropore parameters, comprising the following steps:

[0075] a. Measure the daily liquid production, daily oil production and daily water production of multi-layer commingled production wells within a production period (usually 3-5 days);

[0076] b) analyzing the data collected in step a to determine whether the collected data can be used for quantitative analysis of reservoir macropore parameters (normal production is required during data collection, and the working system is not changed);

[0077] c. Substituting the data in step b that can be used for quantitative analysis of reservoir macropore parameters into calculation to quantitatively characterize the reservoir macropore parameters;

[0078] d. According to the reservoir macropore parameters quantitatively characterized in step c, a profile control and water plugging agent is selected to provide a basis for carrying out targeted water plugging operations in the next step, reducing water content and increasing oil production, so as to guide horizontal well development and improve the utilization of reserves.

[0079] In a preferred embodiment of the present invention, the reservoir macropore parameters in step c include macropore permeability, macropore radius, and macropore volume.

[0080] In a preferred embodiment of the present invention, the step c comprises the following specific steps:

[0081] c1 respectively measures the oil-water relative permeability curves at different development periods to determine the relative permeability of the water phase at different periods, and then calculates the water phase permeability at different periods. The specific formula is as follows:

[0082] K wi =KgK rwi (1)

[0083] Among them, K wi is the water phase permeability, in um 2 ; K is the reservoir permeability, unit is um 2 ; K rwi is the relative permeability of water phase, dimensionless;

[0084] Calculation of c2 theoretical water production:

[0085]

[0086] Among them, q si is the theoretical water production, in m 3 / d;K wi is the water phase permeability, in um 2 ; h is the formation thickness, in m; △p is the injection-production pressure difference, in MPa; μ w is the viscosity of the water phase, in cp; r e is the distance between injection and production wells, in m; r w is the well radius, in m;

[0087] Calculation of macro invalid water injection volume:

[0088] q sdh =q act -q si (3)

[0089] Among them, q sdh is the macro invalid water injection volume, in m 3 / d;q act is the actual water injection volume, in m 3 / d;q si is the theoretical water production, in m 3 / d;

[0090] c4 Calculation of microscopic invalid water injection volume;

[0091]

[0092] Among them, q sdw is the microscopic invalid water injection volume, in m 3 / d;r d is the radius of the large pore, in μm; K d is the macropore permeability, in μm 2 ;μ d is viscosity, unit is cp; dp / dx is pressure gradient between injection and production wells, unit is MPa / m; n is seepage index, 1 / 2≤n≤1;

[0093] c5 uses the mutation theory method to split the multi-layer yield of each layer in the vertical direction;

[0094] Calculation of C6 macropore permeability:

[0095]

[0096] Among them, K di is the macropore permeability of the i-th layer, in um 2 ;q sdiis the macroscopic ineffective water injection volume split to the i-th layer, in m 3 / d; is the porosity of the i-th layer; μ d is viscosity, unit is cp; τ is tortuosity; L is the distance between injection and production wells, unit is m; △p is injection-production pressure difference, unit is MPa;

[0097] Calculation of c7 large channel radius:

[0098]

[0099] Among them, r di is the radius of the macro-pore calculated based on the macroscopic ineffective water injection volume split to the i-th layer, in μm; q sdi is the macroscopic ineffective water injection volume split to the i-th layer, in m 3 / d;μ d is viscosity, unit is cp; is the porosity of the i-th layer; τ is the tortuosity; L is the distance between the injection and production wells, in m; △p is the injection-production pressure difference, in MPa;

[0100] Calculation of c8 macropore volume:

[0101]

[0102] Among them, V di is the volume of the macropores in the i-th layer, in m 3 ; L is the distance between injection and production wells, in m; r d is the radius of the large pore, in μm.

[0103] In a preferred embodiment of the present invention, step c3 comprises the following specific steps:

[0104] c31 Collection, collation and analysis of basic data, including static parameters, reservoir fluid parameters, rock fluid parameters and production dynamic parameters required for quantitative characterization of reservoir macropore parameters;

[0105] Calculation of macro invalid water injection volume of C32:

[0106] q sd =q act -q si (3)

[0107] Among them, q sd is the macroscopic ineffective water injection volume; q act is the actual water injection volume; q si The theoretical water production.

[0108] In a preferred embodiment of the present invention, in step c31, the static parameters include stratification data and sub-layer data, and the sub-layer data include sand layer thickness, top and bottom surface depth, effective thickness, porosity and permeability; the reservoir fluid parameters and the rock fluid parameters include oil-water-gas density, volume coefficient, viscosity, crude oil high-pressure physical properties and rock compression coefficient; the production dynamic parameters include daily oil production, daily liquid production, wellhead pressure, bottom hole flow pressure and measure report.

[0109] In a preferred embodiment of the present invention, in step c2, the theoretical water production is calculated based on the production formula of plane radial seepage (the production formula is the formula (2) in the above step c2).

[0110] In a preferred embodiment of the present invention, in step c4, the microscopic ineffective water injection volume is calculated based on the capillary bundle physical model by Darcy's formula (Darcy's formula is formula (4) in the above step c4).

[0111] In a preferred embodiment of the present invention, in step c5, the step of performing multi-layer yield splitting on each longitudinal layer by using the catastrophe theory method comprises:

[0112] c51 According to the actual situation of the research objectives, select representative dynamic and static evaluation indicators and appropriate mutation models. The commonly used mutation models in engineering are mainly cusp, swallowtail and butterfly mutation models;

[0113] C52 is normalized for different mutation models and expressed as follows:

[0114] Cusp mutation:

[0115]

[0116] Swallowtail mutation:

[0117]

[0118] Butterfly mutation:

[0119]

[0120] c53 Obtain the target value W of each production layer i :

[0121]

[0122] Among them, when n=2, it is a cusp mutation model, when n=3, it is a swallowtail mutation model, and when n=4, it is a butterfly mutation model;

[0123] c54 calculate the splitting coefficient A of each production layer i :

[0124]

[0125] Among them, W i is the target value of the i-th layer; W′ is the target value of the relative mutation surface; n is the number of small layers.

[0126] In a preferred embodiment of the present invention, in step c6, the calculation of macropore permeability comprises the following steps:

[0127] c61 Collection, collation and analysis of basic data, including static parameters, reservoir fluid parameters, rock fluid parameters and production dynamic parameters required for quantitative characterization of reservoir macropore parameters;

[0128] Calculation of C62 macropore permeability:

[0129]

[0130] Among them, K di is the macropore permeability of the i-th layer; q sdi is the ineffective water injection amount split to the i-th layer; is the porosity of the i-th layer; μ d is viscosity; τ is tortuosity; L is the distance between injection and production wells; △p is the injection-production pressure difference.

[0131] In a preferred embodiment of the present invention, in step c61, the static parameters include stratification data and sub-layer data, and the sub-layer data include sand layer thickness, top and bottom surface depth, effective thickness, porosity and permeability; the reservoir fluid parameters and the rock fluid parameters include oil-water-gas density, volume coefficient, viscosity, crude oil high-pressure physical properties and rock compression coefficient; the production dynamic parameters include daily oil production, daily liquid production, wellhead pressure, bottom hole flow pressure and measures report.

[0132] In a preferred embodiment of the present invention, in step c62, the Carman-Kozeny formula is:

[0133]

[0134] Among them, r d is the radius of the large pore; K d is the macropore permeability; τ is the tortuosity; is the porosity.

[0135] In a preferred embodiment of the present invention, in step c7, based on the calculation process of step c62, the large channel radius is calculated by solving the Carman-Kozeny formula.

[0136] In a preferred embodiment of the present invention, step c8 calculates the volume of the macropores based on the calculation results of step c7 and the tubular model.

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A reservoir engineering method for quantitative characterization of reservoir macropore parameters, characterized in that: The steps include: a. Measure the daily liquid production, daily oil production and daily water production of multi-layer commingled production wells during a production period; b. analyzing the data collected in step a to determine whether the collected data can be used for quantitative analysis of reservoir macropore parameters; c. Substituting the data in step b that can be used for quantitative analysis of reservoir macropore parameters into calculation to quantitatively characterize the reservoir macropore parameters; d. According to the reservoir macropore parameters quantitatively characterized in step c, a profile control and water plugging agent is selected to provide a basis for carrying out targeted water plugging operations in the next step, reduce water content, and increase oil production, so as to guide horizontal well development and improve the degree of reserve utilization; The step c comprises the following specific steps: c1 respectively measures the oil-water relative permeability curves at different development periods to determine the relative permeability of the water phase at different periods, and then calculates the water phase permeability at different periods. The specific formula is as follows: K wi =K·K rwi (1) Among them, K wi is the water phase permeability; K is the reservoir permeability; K rwi is the relative permeability of water phase; Calculation of c2 theoretical water production: Among them, q si is the theoretical water production; K wi is the water phase permeability; h is the formation thickness; △p is the injection-production pressure difference; μ w is the viscosity of the water phase; r e is the distance between injection and production wells; r w is the well radius; Calculation of macro invalid water injection volume: q sdh =q act -q si (3) Among them, q sdh is the macroscopic ineffective water injection volume; q act is the actual water injection volume; q si is the theoretical water production; c4 Calculation of microscopic invalid water injection volume; Among them, q sdw is the microscopic invalid water injection volume; r d is the radius of the large pore; K d is the macropore permeability; μ d is viscosity; dp / dx is the pressure gradient between injection and production wells; n is the seepage index, 1 / 2≤n≤1; c5 uses the mutation theory method to split the multi-layer yield of each layer in the vertical direction; Calculation of C6 macropore permeability: Among them, K di is the macropore permeability of the i-th layer; q sdi is the macroscopic ineffective water injection volume split to the i-th layer; is the porosity of the i-th layer; μ d is viscosity; τ is tortuosity; L is the distance between injection and production wells; △p is the injection-production pressure difference; Calculation of c7 large channel radius: Among them, r di is the radius of the large pore calculated based on the macroscopic ineffective water injection volume split to the i-th layer, q sdi is the macroscopic ineffective water injection volume split to the i-th layer; μ d is viscosity; is the porosity of the i-th layer; τ is the tortuosity; L is the distance between the injection and production wells; △p is the injection and production pressure difference; Calculation of c8 macropore volume: Among them, V di is the volume of the macropores in the i-th layer; L is the distance between the injection and production wells; r d is the maximum hole radius.

2. The reservoir engineering method according to claim 1, characterized in that: The reservoir macropore parameters in step c include macropore permeability, macropore radius, and macropore volume.

3. The reservoir engineering method according to claim 1, characterized in that: The step c3 comprises the following specific steps: c31 Collection, collation and analysis of basic data, including static parameters, reservoir fluid parameters, rock fluid parameters and production dynamic parameters required for quantitative characterization of reservoir macropore parameters; Calculation of macro invalid water injection volume of C32: q sd =q act -q si (3) Among them, q sd is the macroscopic ineffective water injection volume; q act is the actual water injection volume; q si The theoretical water production.

4. The reservoir engineering method according to claim 3, characterized in that: In step c31, the static parameters include stratification data and small layer data, and the small layer data include sand layer thickness, top and bottom surface depth, effective thickness, porosity and permeability; the reservoir fluid parameters and the rock fluid parameters include oil-water-gas density, volume coefficient, viscosity, crude oil high-pressure physical properties and rock compression coefficient; the production dynamic parameters include daily oil production, daily liquid production, wellhead pressure, bottom hole flow pressure and measures report.

5. The reservoir engineering method according to claim 1, characterized in that: In step c5, the step of performing multi-layer yield splitting on each layer in the longitudinal direction by using the catastrophe theory method comprises: c51 According to the actual situation of the research objectives, select representative dynamic and static evaluation indicators and appropriate mutation models. Commonly used mutation models in engineering include cusp, swallowtail and butterfly mutation models; C52 is normalized for different mutation models and expressed as follows: Cusp mutation: Swallowtail mutation: Butterfly mutation: c53 Obtain the target value W of each production layer i : Among them, when n=2, it is a cusp mutation model, when n=3, it is a swallowtail mutation model, and when n=4, it is a butterfly mutation model; c54 calculate the splitting coefficient A of each production layer i : Among them, W i is the target value of the i-th layer; W′ is the target value of the relative mutation surface; n is the number of small layers.

6. The reservoir engineering method according to claim 1, characterized in that: In step c6, the calculation of macropore permeability includes the following steps: c61 Collection, collation and analysis of basic data, including static parameters, reservoir fluid parameters, rock fluid parameters and production dynamic parameters required for quantitative characterization of reservoir macropore parameters; Calculation of C62 macropore permeability: Among them, K di is the macropore permeability of the i-th layer; q sdi is the ineffective water injection amount split to the i-th layer; is the porosity of the i-th layer; μ d is viscosity; τ is tortuosity; L is the distance between injection and production wells; △p is the injection-production pressure difference.

7. The reservoir engineering method according to claim 6, characterized in that: In step c61, the static parameters include stratification data and sub-layer data, and the sub-layer data include sand layer thickness, top and bottom surface depth, effective thickness, porosity and permeability; the reservoir fluid parameters and the rock fluid parameters include oil-water-gas density, volume coefficient, viscosity, crude oil high-pressure physical properties and rock compression coefficient; the production dynamic parameters include daily oil production, daily liquid production, wellhead pressure, bottom hole flow pressure and measures report.

8. The reservoir engineering method according to claim 6, characterized in that: In step c62, the Carman-Kozeny formula is: Among them, r d is the radius of the large pore; K d is the macropore permeability; τ is the tortuosity; is the porosity.

9. The reservoir engineering method according to claim 1, characterized in that: The step c8 calculates the volume of the large pores based on the calculation results of the step c7 and the tubular model.

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