CN113177363A - Reservoir engineering method for quantitatively characterizing reservoir large pore channel parameters - Google Patents
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Abstract
The invention relates to an oil reservoir engineering method for quantitatively characterizing reservoir large pore channel parameters, which comprises the following steps: a, measuring daily liquid yield, daily oil yield and daily water yield of the multi-layer combined production well in a period of production; b, analyzing the data collected in the step a, and judging whether the collected data can be used for quantitative analysis of reservoir large pore channel parameters; c, substituting the data which can be used for quantitative analysis of the reservoir large pore channel parameters in the step b for calculation, and quantitatively representing the reservoir large pore channel parameters; d, selecting a profile control water plugging agent according to the reservoir large pore channel parameter quantitatively represented in the step c, and providing a basis for pertinently developing water plugging operation, reducing the water content and improving the oil yield. The method of the invention is simple and feasible to operate, remarkably reduces the cost and is convenient to popularize and apply.
Description
Technical Field
The invention relates to an oil reservoir engineering method for quantitatively characterizing reservoir large pore channel parameters, belonging to the technical field of oil development.
Background
After the majority of oil fields in China are developed by water flooding for decades, the oil fields are about to step into the period of high water content or even ultra high water content. Some of the oil fields enter the middle and later development stages, and the oil well production at the stage faces various problems of high liquid yield, high water content, severe water-oil ratio rising speed, difficult oil stabilization and water control and the like. The phenomena of inefficient and even ineffective circulation of water injection in oil and water wells is becoming more severe as a large amount of injected water flows rapidly from the well end to the well end along a low resistance area of the fluid stream developed in the oil reservoir. Moreover, the conventional method for quantitatively characterizing the large pore channel parameters is relatively deficient, the large pore channel parameters of reservoirs in different blocks have large difference, and if the specific parameters cannot be determined, reliable basis is difficult to provide for the development of the next water shutoff profile control operation. Therefore, a method for quantitatively characterizing the parameters of the large pore canal of the reservoir is provided, so as to provide a theoretical basis for taking targeted water shutoff and profile control measures aiming at the large pore canals with different levels.
At present, methods related to large pore channel quantitative characterization mainly comprise methods such as well logging curve inversion, coring data identification, mathematical decision theory identification and the like, but each method has great limitation in application: the well logging curve inversion method can only reflect the development condition of a large pore canal in a few meters in a near wellbore zone, and the construction period is long; the coring data identification method has high operation cost, can obtain more comprehensive understanding only by needing multiple operations, and has less overall application well times; more human factors are introduced into the mathematical decision method, and the error is larger. The oil reservoir dynamics is the comprehensive embodiment of the conditions of an injection well, a production well and a reservoir, and can reflect the development condition of a large pore channel of the reservoir to a certain extent, so that the quantitative characterization of the large pore channel parameters by adopting an oil reservoir engineering method is considered, according to the different development conditions of the large pore channel, a profile control or plugging agent is optimized in the next step, the interlayer contradiction is relieved, the plane and longitudinal sweep coefficients are improved, the effect is uniformly received in all directions, the subsequent construction cost is reduced, and the aims of reducing the cost and improving the effect are fulfilled. However, at present, there are many qualitative description methods for oil reservoir engineering related to development of large pore channels, few methods for quantitatively characterizing oil reservoir engineering, and some methods have problems in applicability, and it is difficult to accurately characterize large pore channel quantitative parameters, so that there is a need to provide an oil reservoir engineering method for quantitatively characterizing large pore channel parameters.
Disclosure of Invention
Aiming at the outstanding problems, the invention provides the oil reservoir engineering method for quantitatively characterizing the parameters of the large pore canal of the reservoir, the method can calculate the parameters of the large pore canal of the reservoir, such as the permeability, the pore throat radius and the like, the operation is simple and feasible, the cost is obviously reduced, and the method is convenient to popularize and apply.
In order to achieve the purpose, the invention adopts the following technical scheme:
a reservoir engineering method for quantitatively characterizing reservoir large pore channel parameters comprises the following steps:
a, measuring daily liquid yield, daily oil yield and daily water yield of the multi-layer combined production well in a period of production;
b, analyzing the data collected in the step a, and judging whether the collected data can be used for quantitative analysis of reservoir large pore channel parameters;
c, substituting the data which can be used for quantitative analysis of the reservoir large pore channel parameters in the step b for calculation, and quantitatively representing the reservoir large pore channel parameters;
d, selecting a profile control water plugging agent according to the reservoir large pore channel parameter quantitatively represented in the step c, and providing a basis for pertinently developing water plugging operation, reducing the water content and improving the oil yield in the next step so as to guide the development of a horizontal well and improve the reserve utilization degree.
In the reservoir engineering method, preferably, the reservoir large pore channel parameters in the step c include large pore channel permeability, large pore channel radius and large pore channel volume.
Preferably, the oil reservoir engineering method in step c includes the following specific steps:
c1, respectively measuring oil-water phase permeability curves of different development periods to determine water-phase relative permeability of different periods, and further calculating water-phase permeability of different periods, wherein the specific formula is as follows:
Kwi＝KgKrwi (1)
wherein, KwiIs the water phase permeability; k is reservoir permeability; krwiRelative permeability of water phase;
c2 calculation of theoretical Water yield:
wherein q issiTheoretical water yield; kwiIs the water phase permeability; h is the formation thickness; delta p is injection-production pressure difference; mu.swIs the viscosity of the water phase; r iseThe distance between injection wells and production wells; r iswIs the well radius;
c3 calculation of macroscopic ineffective water injection:
qsdh＝qact-qsi (3)
wherein q issdhFor the macroscopic ineffective water injection；qactThe actual water injection amount is obtained; q. q.ssiTheoretical water yield;
c4 calculating the micro ineffective water injection amount;
wherein q issdwThe micro ineffective water injection quantity is obtained; r isdLarge pore radius; kdIs the large pore permeability; mu.sdIs viscosity; dp/dx is the pressure gradient between injection wells and production wells; n is a seepage index, n is greater than or equal to 1/2 and less than or equal to 1;
c5 carrying out multilayer yield splitting on each longitudinal layer by using a mutation theory method;
calculation of the macropore permeability of c 6:
wherein, KdiThe large pore path permeability of the ith layer; q. q.ssdiThe macroscopic ineffective water injection quantity from the splitting to the ith layer;
porosity of the ith layer; mu.sdIs viscosity; τ is tortuosity; l is the distance between injection wells and production wells; delta p is injection-production pressure difference;
c7 calculation of Large pore radius:
wherein r isdiIs the calculated large pore path radius, q, based on the macroscopic ineffective water injection quantity from splitting to the ith layersdiThe macroscopic ineffective water injection quantity from the splitting to the ith layer; mu.sdIs viscosity;
porosity of the ith layer; τ is tortuosity; l is the distance between injection wells and production wells; delta p is injection-production pressure difference；
calculation of large pore volume c 8:
wherein, VdiLarge pore volume of the i-th layer in m3(ii) a L is the distance between injection wells and production wells, and the unit is m; r isdIs the large pore radius in μm.
Preferably, the oil reservoir engineering method in step c3 includes the following steps:
c31, collecting, sorting and analyzing basic data, wherein the basic data comprises static parameters, reservoir fluid parameters, rock fluid parameters and production dynamic parameters required by quantitative characterization of reservoir large pore parameters;
c32 calculation of macroscopic ineffective water injection:
qsd＝qact-qsi (8)
wherein q issdThe macroscopic ineffective water injection quantity is obtained; q. q.sactThe actual water injection amount is obtained; q. q.ssiIs the theoretical water yield.
In the reservoir engineering method, preferably, in step c31, the static parameters include layered data and small-scale data, and the small-scale data includes sand thickness, top and bottom depth, effective thickness, porosity and permeability; the oil deposit fluid parameters and the rock fluid parameters comprise oil-water gas density, volume coefficient, viscosity, crude oil high-pressure physical parameters and rock compression coefficient; the production dynamic parameters comprise daily oil production, daily liquid production, wellhead pressure, bottom hole flowing pressure and measure reports.
Preferably, in the oil reservoir engineering method, in the step c5, the step of performing multilayer yield splitting on each longitudinal layer by using a mutation theory method includes:
c51 selecting representative dynamic and static evaluation indexes according to the actual condition of a research target, and selecting a proper mutation model, wherein the mutation models commonly used in engineering mainly comprise cusp, dovetail and butterfly mutation models;
c52 was normalized for different mutation models and expressed as follows:
cusp mutation:
dovetail mutation:
butterfly mutation:
c53 finding the target value W of each zonei：
Wherein, when n is 2, the model is a cusp mutation model, when n is 3, the model is a dovetail mutation model, and when n is 4, the model is a butterfly mutation model;
c54 finding the splitting coefficient A of each stratumi：
Wherein, WiIs the target value of the ith layer; w' is the target value of the relative mutation surface; n is the number of small layers.
In the reservoir engineering method, preferably, in the step c6, the calculation of the large pore path permeability includes the following steps:
c61, collecting, sorting and analyzing basic data, wherein the basic data comprises static parameters, reservoir fluid parameters, rock fluid parameters and production dynamic parameters required by quantitative characterization of reservoir large pore parameters;
calculation of the macropore permeability of c 62:
wherein, KdiThe large pore path permeability of the ith layer; q. q.ssdiThe ineffective water injection amount from splitting to the ith layer;
porosity of the ith layer; mu.sdIs viscosity; τ is tortuosity; l is the distance between injection wells and production wells; and delta p is the injection-production pressure difference.
In the reservoir engineering method, preferably, in step c61, the static parameters include layered data and small-scale data, and the small-scale data includes sand thickness, top and bottom depth, effective thickness, porosity and permeability; the oil deposit fluid parameters and the rock fluid parameters comprise oil-water gas density, volume coefficient, viscosity, crude oil high-pressure physical parameters and rock compression coefficient; the production dynamic parameters comprise daily oil production, daily liquid production, wellhead pressure, bottom hole flowing pressure and measure reports.
Preferably, in the reservoir engineering method, in the step c62, the formula of Carman-Kozeny is as follows:
wherein r isdLarge pore radius; kdIs the large pore permeability; τ is tortuosity;
is porosity.
Preferably, the reservoir engineering method in step c8 calculates the large pore volume based on the calculation result in step c7 and the tubular model.
Due to the adoption of the technical scheme, the invention has the following advantages:
1. the reservoir large pore passage quantitative characterization oil reservoir engineering method provided by the invention can calculate parameters such as permeability, pore throat radius and the like of the reservoir large pore passage, is simple and feasible to operate, remarkably reduces the cost and is convenient to popularize and apply.
2. The method directly utilizes the dynamic data to quantitatively represent the parameters of the large pore channels of the reservoir, is simple, convenient and convenient, and is convenient to implement, and the calculation result provides great convenience for selecting targeted agents for the large pore channels with different magnitudes in the next step and developing the operation of profile control and water shutoff, so that the aims of enlarging swept volume and increasing yield are fulfilled.
3. The invention provides a multi-layer yield (injection amount) splitting method based on a mutation theory method aiming at the problems of low distribution accuracy and no consideration of dynamic factors in the splitting process of the multi-layer yield (injection amount) of a multi-layer commingled production well in a heterogeneous stratum, and the reliability and the accuracy of splitting are improved.
4. The invention does not need to carry out additional field operation and test, reduces the construction cost, reduces the operation period and further achieves the purposes of cost reduction and efficiency improvement. The method can be widely applied to the research of the quantitative characterization of the reservoir large pore channel parameters.
Drawings
FIG. 1 is a schematic flow chart of a reservoir engineering method for quantitatively characterizing reservoir large pore channel parameters according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a multi-layer splitting effect of the mutation theory method provided in this embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention are described clearly and completely below, and it is obvious that the described embodiments are some, not all embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
As shown in FIG. 1, the invention provides a reservoir engineering method for quantitatively characterizing reservoir large pore channel parameters, which comprises the following steps:
a, measuring daily liquid yield, daily oil yield and daily water yield of the multi-layer combined production well in a production period (generally 3-5 days);
b, analyzing the data acquired in the step a, and judging whether the acquired data can be used for quantitative analysis of reservoir large pore channel parameters (normal production is needed during data acquisition, and a working system is not changed);
c, substituting the data which can be used for quantitative analysis of the reservoir large pore channel parameters in the step b for calculation, and quantitatively representing the reservoir large pore channel parameters;
d, selecting a profile control water plugging agent according to the reservoir large pore channel parameter quantitatively represented in the step c, and providing a basis for pertinently developing water plugging operation, reducing the water content and improving the oil yield in the next step so as to guide the development of a horizontal well and improve the reserve utilization degree.
In a preferred embodiment of the present invention, the reservoir large pore channel parameters in step c include large pore channel permeability, large pore channel radius and large pore channel volume.
In a preferred embodiment of the present invention, said step c comprises the following specific steps:
c1, respectively measuring oil-water phase permeability curves of different development periods to determine water-phase relative permeability of different periods, and further calculating water-phase permeability of different periods, wherein the specific formula is as follows:
Kwi＝KgKrwi (1)
wherein, KwiIs water phase permeability in um2(ii) a K is the reservoir permeability in um2；KrwiIs the relative permeability of the water phase and is dimensionless;
c2 calculation of theoretical Water yield:
wherein q issiAs theoretical water yield, in m3/d；KwiIs water phase permeability in um2(ii) a h is the thickness of the stratum in m; delta p is injection-production differential pressure, and the unit is MPa; mu.swIs the aqueous phase viscosity in cp; r iseThe distance between injection wells and production wells is m; r iswIs the well radius in m;
c3 calculation of macroscopic ineffective water injection:
qsdh＝qact-qsi (3)
wherein q issdhFor macroscopic ineffective water injection, the unit is m3/d；qactFor the actual water injection, the unit is m3/d；qsiAs theoretical water yield, in m3/d；
c4 calculating the micro ineffective water injection amount;
wherein q issdwThe unit of the microscopic ineffective water injection is m3/d；rdLarge pore radius in μm; kdIs the large pore permeability in μm2；μdIs viscosity, in cp; dp/dx is the pressure gradient between injection wells and production wells, and the unit is MPa/m; n is a seepage index, n is greater than or equal to 1/2 and less than or equal to 1;
c5 carrying out multilayer yield splitting on each longitudinal layer by using a mutation theory method;
calculation of the macropore permeability of c 6:
wherein, KdiIs the large pore path permeability of the ith layer in um2；qsdiThe macroscopic ineffective water injection quantity for splitting to the ith layer is m3/d；
Porosity of the ith layer; mu.sdIs viscosity, in cp; τ is tortuosity; l is distance between injection wells and production wellsIn the unit of m; delta p is injection-production differential pressure, and the unit is MPa;
c7 calculation of Large pore radius:
wherein r isdiThe unit is the radius of a large pore path obtained by calculating the macroscopic invalid water injection amount from the splitting to the ith layer, and the radius is mum; q. q.ssdiThe macroscopic ineffective water injection quantity for splitting to the ith layer is m3/d；μdIs viscosity, in cp;
porosity of the ith layer; τ is tortuosity; l is the distance between injection wells and production wells, and the unit is m; delta p is injection-production differential pressure, and the unit is MPa;
calculation of large pore volume c 8:
wherein, VdiLarge pore volume of the i-th layer in m3(ii) a L is the distance between injection wells and production wells, and the unit is m; r isdIs the large pore radius in μm.
In a preferred embodiment of the present invention, the step c3 comprises the following specific steps:
c31, collecting, sorting and analyzing basic data, wherein the basic data comprises static parameters, reservoir fluid parameters, rock fluid parameters and production dynamic parameters required by quantitative characterization of reservoir large pore parameters;
c32 calculation of macroscopic ineffective water injection:
qsd＝qact-qsi (3)
wherein q issdThe macroscopic ineffective water injection quantity is obtained; q. q.sactThe actual water injection amount is obtained; q. q.ssiIs the theoretical water yield.
In a preferred embodiment of the present invention, in the step c31, the static parameters include layered data and small-scale data, the small-scale data includes sand thickness, top and bottom surface depth, effective thickness, porosity and permeability; the oil deposit fluid parameters and the rock fluid parameters comprise oil-water gas density, volume coefficient, viscosity, crude oil high-pressure physical parameters and rock compression coefficient; the production dynamic parameters comprise daily oil production, daily liquid production, wellhead pressure, bottom hole flowing pressure and measure reports.
In a preferred embodiment of the present invention, in said step c2, the calculation of the theoretical water yield is performed based on the production formula of the planar radial seepage (the production formula is formula (2) in step c2 above).
In a preferred embodiment of the present invention, in the step c4, the micro ineffective water injection amount is calculated by darcy formula (the darcy formula is formula (4) in the above step c 4) based on the hair bundle physical model.
In a preferred embodiment of the present invention, in the step c5, the step of performing multi-layer yield splitting on each longitudinal layer by using a mutation theory method comprises:
c51 selecting representative dynamic and static evaluation indexes according to the actual condition of a research target, and selecting a proper mutation model, wherein the mutation models commonly used in engineering mainly comprise cusp, dovetail and butterfly mutation models;
c52 was normalized for different mutation models and expressed as follows:
cusp mutation:
dovetail mutation:
butterfly mutation:
c53 finding the target value W of each zonei：
Wherein, when n is 2, the model is a cusp mutation model, when n is 3, the model is a dovetail mutation model, and when n is 4, the model is a butterfly mutation model;
c54 finding the splitting coefficient A of each stratumi：
Wherein, WiIs the target value of the ith layer; w' is the target value of the relative mutation surface; n is the number of small layers.
In a preferred embodiment of the present invention, the calculation of the macropore permeability in said step c6 comprises the steps of:
c61, collecting, sorting and analyzing basic data, wherein the basic data comprises static parameters, reservoir fluid parameters, rock fluid parameters and production dynamic parameters required by quantitative characterization of reservoir large pore parameters;
calculation of the macropore permeability of c 62:
wherein, KdiThe large pore path permeability of the ith layer; q. q.ssdiThe ineffective water injection amount from splitting to the ith layer;
porosity of the ith layer; mu.sdIs viscosity; τ is tortuosity; l is the distance between injection wells and production wells; and delta p is the injection-production pressure difference.
In a preferred embodiment of the present invention, in the step c61, the static parameters include layered data and small-scale data, the small-scale data includes sand thickness, top and bottom surface depth, effective thickness, porosity and permeability; the oil deposit fluid parameters and the rock fluid parameters comprise oil-water gas density, volume coefficient, viscosity, crude oil high-pressure physical parameters and rock compression coefficient; the production dynamic parameters comprise daily oil production, daily liquid production, wellhead pressure, bottom hole flowing pressure and measure reports.
In a preferred embodiment of the present invention, in step c62, the formula Carman-Kozeny is:
wherein r isdLarge pore radius; kdIs the large pore permeability; τ is tortuosity;
is porosity.
In a preferred embodiment of the present invention, in step c7, the calculation of the large pore radius is performed based on the calculation process of step c62, which is solved in parallel with the Carman-Kozeny formula.
In a preferred embodiment of the present invention, said step c8 performs calculation of large pore volume based on the calculation result of said step c7 and the tubular model.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A reservoir engineering method for quantitatively characterizing reservoir large pore channel parameters is characterized by comprising the following steps:
a, measuring daily liquid yield, daily oil yield and daily water yield of the multi-layer combined production well in a period of production;
b, analyzing the data collected in the step a, and judging whether the collected data can be used for quantitative analysis of reservoir large pore channel parameters;
c, substituting the data which can be used for quantitative analysis of the reservoir large pore channel parameters in the step b for calculation, and quantitatively representing the reservoir large pore channel parameters;
d, selecting a profile control water plugging agent according to the reservoir large pore channel parameter quantitatively represented in the step c, and providing a basis for pertinently developing water plugging operation, reducing the water content and improving the oil yield in the next step so as to guide the development of a horizontal well and improve the reserve utilization degree.
2. The reservoir engineering method according to claim 1, wherein the reservoir large pore channel parameters in step c comprise large pore channel permeability, large pore channel radius and large pore channel volume.
3. The reservoir engineering method according to claim 2, wherein the step c comprises the following specific steps:
c1, respectively measuring oil-water phase permeability curves of different development periods to determine water-phase relative permeability of different periods, and further calculating water-phase permeability of different periods, wherein the specific formula is as follows:
Kwi＝KgKrwi (1)
wherein, KwiIs the water phase permeability; k is reservoir permeability; krwiRelative permeability of water phase;
c2 calculation of theoretical Water yield:
wherein q issiTheoretical water yield; kwiIs the water phase permeability; h is groundLayer thickness; delta p is injection-production pressure difference; mu.swIs the viscosity of the water phase; r iseThe distance between injection wells and production wells; r iswIs the well radius;
c3 calculation of macroscopic ineffective water injection:
qsdh＝qact-qsi (3)
wherein q issdhThe macroscopic ineffective water injection quantity is obtained; q. q.sactThe actual water injection amount is obtained; q. q.ssiTheoretical water yield;
c4 calculating the micro ineffective water injection amount;
wherein q issdwThe micro ineffective water injection quantity is obtained; r isdLarge pore radius; kdIs the large pore permeability; mu.sdIs viscosity; dp/dx is the pressure gradient between injection wells and production wells; n is a seepage index, n is greater than or equal to 1/2 and less than or equal to 1;
c5 carrying out multilayer yield splitting on each longitudinal layer by using a mutation theory method;
calculation of the macropore permeability of c 6:
wherein, KdiThe large pore path permeability of the ith layer; q. q.ssdiThe macroscopic ineffective water injection quantity from the splitting to the ith layer;
porosity of the ith layer; mu.sdIs viscosity; τ is tortuosity; l is the distance between injection wells and production wells; delta p is injection-production pressure difference;
c7 calculation of Large pore radius:
wherein r isdiIs the calculated large pore path radius, q, based on the macroscopic ineffective water injection quantity from splitting to the ith layersdiThe macroscopic ineffective water injection quantity from the splitting to the ith layer; mu.sdIs viscosity;
porosity of the ith layer; τ is tortuosity; l is the distance between injection wells and production wells; delta p is injection-production pressure difference;
calculation of large pore volume c 8:
wherein, VdiLarge pore volume of the ith layer; l is the distance between injection wells and production wells; r isdLarge pore radii.
4. The reservoir engineering method according to claim 3, wherein the step c3 comprises the following specific steps:
c31, collecting, sorting and analyzing basic data, wherein the basic data comprises static parameters, reservoir fluid parameters, rock fluid parameters and production dynamic parameters required by quantitative characterization of reservoir large pore parameters;
c32 calculation of macroscopic ineffective water injection:
qsd＝qact-qsi (3)
wherein q issdThe macroscopic ineffective water injection quantity is obtained; q. q.sactThe actual water injection amount is obtained; q. q.ssiIs the theoretical water yield.
5. The reservoir engineering method according to claim 4, wherein in step c31, the static parameters comprise layered data and small-scale data, the small-scale data comprising sand thickness, top and bottom surface depth, effective thickness, porosity and permeability; the oil deposit fluid parameters and the rock fluid parameters comprise oil-water gas density, volume coefficient, viscosity, crude oil high-pressure physical parameters and rock compression coefficient; the production dynamic parameters comprise daily oil production, daily liquid production, wellhead pressure, bottom hole flowing pressure and measure reports.
6. The reservoir engineering method according to claim 3, wherein the step of performing multi-layer yield splitting on each longitudinal layer by using a mutation theory method in the step c5 comprises:
c51 selecting representative dynamic and static evaluation indexes according to the actual condition of a research target, and selecting a proper mutation model, wherein the mutation models commonly used in engineering mainly comprise cusp, dovetail and butterfly mutation models;
c52 was normalized for different mutation models and expressed as follows:
cusp mutation:
dovetail mutation:
butterfly mutation:
c53 finding the target value W of each zonei：
Wherein, when n is 2, the model is a cusp mutation model, when n is 3, the model is a dovetail mutation model, and when n is 4, the model is a butterfly mutation model;
c54 finding the splitting coefficient A of each stratumi：
Wherein, WiIs the target value of the ith layer; w' is the target value of the relative mutation surface; n is the number of small layers.
7. The reservoir engineering method according to claim 3, wherein the calculation of the large pore canal permeability in step c6 comprises the following steps:
c61, collecting, sorting and analyzing basic data, wherein the basic data comprises static parameters, reservoir fluid parameters, rock fluid parameters and production dynamic parameters required by quantitative characterization of reservoir large pore parameters;
calculation of the macropore permeability of c 62:
wherein, KdiThe large pore path permeability of the ith layer; q. q.ssdiThe ineffective water injection amount from splitting to the ith layer;
porosity of the ith layer; mu.sdIs viscosity; τ is tortuosity; l is the distance between injection wells and production wells; and delta p is the injection-production pressure difference.
8. The reservoir engineering method according to claim 7, wherein in step c61, the static parameters comprise layered data and small-scale data, the small-scale data comprising sand thickness, top and bottom surface depth, effective thickness, porosity and permeability; the oil deposit fluid parameters and the rock fluid parameters comprise oil-water gas density, volume coefficient, viscosity, crude oil high-pressure physical parameters and rock compression coefficient; the production dynamic parameters comprise daily oil production, daily liquid production, wellhead pressure, bottom hole flowing pressure and measure reports.
9. Reservoir engineering method according to claim 7, characterized in that in step c62, the Carman-Kozeny formula is:
wherein r isdLarge pore radius; kdIs the large pore permeability; τ is tortuosity;
is porosity.
10. The reservoir engineering method according to claim 3, wherein the step c8 is based on the calculation result of step c7 and a tubular model to calculate the large pore channel volume.
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| CN116950637A (en) * | 2023-07-11 | 2023-10-27 | 德州学院 | Single well group profile control method and system based on micro-pressure drive and readable storage medium |
| CN116950637B (en) * | 2023-07-11 | 2024-03-08 | 德州学院 | Single well group profile control method and system based on micro-pressure drive and readable storage medium |
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