CN114429023A - Sectional injection-production parameter optimization method based on plane flow unit planning - Google Patents
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Abstract
The invention relates to a water drive reservoir sectional injection and production parameter optimization method, in particular to a sectional injection and production parameter optimization method defined on the basis of a plane flow unit for a water drive reservoir. The method comprises the following steps: step 1, collecting geological and production data; step 2, establishing a water drive reservoir production dynamic initial description model; step 3, carrying out automatic history fitting on reservoir parameters, correcting the description model, and establishing a water drive reservoir production dynamic accurate description model; and 4, developing a dynamic accurate description model by using water flooding, and obtaining an optimal injection-production parameter scheme by using an optimization algorithm and aiming at optimal production. The method has simple steps and operation, can quickly realize the accurate optimization of the injection-production parameters of the flooding oil reservoir on the premise of accurately reflecting the injection-production relation, and overcomes the existing defects.
Description
Technical Field
The invention relates to a water drive reservoir sectional injection and production parameter optimization method, in particular to a sectional injection and production parameter optimization method defined on the basis of a plane flow unit for a water drive reservoir.
Background
The old water-flooding oil field represented by the victory oil field has a plurality of oil-bearing layers, along with the continuous deepening of the development, the heterogeneity of the reservoir is further intensified, the development is increasingly prominent, the yield of crude oil is in an obvious descending trend, and the development effect is worsened year by year. The main reason is that the water absorption capacity of each small layer reservoir in the layer system is different due to the difference of physical properties of the reservoirs, and most of water injected into the oil layer during water injection is absorbed by a high permeable layer with small thickness, so that the water injection and absorption profile is very uneven, the water injection utilization rate is low, and the ineffective water circulation is serious. The water drive effect can be effectively improved by reasonably adjusting the injection and production parameters, and oil and water stabilization can be realized.
Chinese patent application CN109872007A discloses a multi-objective optimization method for oil deposit injection-production parameters based on a support vector machine proxy model, which generates a certain number of injection-production schemes as samples through numerical simulation software; inputting the sample into a least square support vector machine to form a substitution model, and optimizing by adopting a particle swarm optimization to achieve convergence if the condition of non-convergence occurs in the process; generating an initial injection-production parameter population with the size of M based on a least square support vector machine, selecting a proper objective function, pareto grade and crowding distance, and then optimizing injection-production parameters by adopting a non-dominated sorting multi-objective optimization genetic algorithm NSGA-II with an elite strategy to obtain a pareto solution set; and finding out the optimized oil reservoir injection-production parameters in the pareto solution set according to the requirements of the objective function. Although the method realizes the multi-objective optimization design of the oil reservoir injection-production parameters, the efficiency is improved while the prediction precision of the agent model is ensured.
Chinese patent application CN110924908A discloses a method for determining injection and production parameters of a water-drive reservoir and a computer-readable storage medium, the method comprising: establishing a streamline simulation model: establishing a streamline simulation model of a target oil reservoir; generating injection and production parameters: randomly generating injection and production parameters under the constraint condition of the injection and production parameters; determining a flow line oil displacement capacity value: predicting the instantaneous flow field distribution of the streamline simulation model by the injection-production parameters through a streamline simulator, and extracting streamline characteristic parameters; determining a flow line oil displacement capacity value of an injection-production parameter based on the flow line characteristic parameter; and, evaluating the injection-production parameters: based on the optimization objective function, judging whether the flow line oil displacement capacity value reaches an optimization objective relative to the flow line oil displacement capacity initial value; if the optimization target is reached, determining the injection-production parameters as output injection-production parameters; and otherwise, taking the flow line oil displacement capacity value as an initial value of the flow line oil displacement capacity, and returning to the step of generating the injection-production parameters. The injection-production parameters can be quickly determined.
Chinese patent application CN110439515A discloses an injection-production parameter optimization method, comprising: the method comprises the following steps that firstly, well-to-well dynamic communication coefficients between block injection wells and production wells in blocks are obtained based on production dynamic data of the block injection wells and the production wells in the blocks; constructing an injection-production parameter optimization model with the maximum oil production amount of the production wells in the block as an objective function; and step three, bringing the water content of the production wells in the block and the dynamic connectivity coefficient among the wells into an injection-production parameter optimization model to obtain the optimal injection allocation amount of the injection wells in the block. According to the invention, by constructing an injection-production parameter optimization model based on the inter-well dynamic communication coefficient, the monthly water injection rate of each water injection well in the oil field is optimized by taking the maximum oil production of the oil reservoir in the current block as an optimization target, so that a dynamic optimization regulation and control method is formed, the maximization of the water injection efficiency is realized, the balanced displacement of the injected water can be realized, and the final recovery ratio can be improved.
The problem of reservoir production parameter optimization belongs to a large-scale optimization problem, and in recent years, a plurality of scholars adopt an optimization algorithm to be coupled with a reservoir numerical simulator, so that the reservoir problem is optimized. Because the method needs to be combined with an oil reservoir numerical simulator in the solving process, the solving of a large matrix is involved, the solving is very complicated, and the accuracy of the solving result cannot be ensured. Meanwhile, the method has low practicability for engineering technicians, and the main reason is that not all oil reservoirs are established with oil reservoir numerical models; and secondly, a large amount of time and energy are occupied by the history fitting work in the early stage of numerical reservoir simulation, and the requirements on the numerical reservoir simulation speciality and experience of operators are high.
Disclosure of Invention
The invention mainly aims to provide a segmented injection-production parameter optimization method based on plane flow unit planning, which has simple steps and operation, can quickly realize accurate optimization of injection-production parameters of a flooding oil reservoir on the premise of accurately reflecting injection-production relations, and overcomes the existing defects.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a segmented injection-production parameter optimization method based on plane flow unit planning, which comprises the following steps:
step 1, collecting geological and production data;
step 2, establishing a water drive reservoir production dynamic initial description model;
step 3, carrying out automatic history fitting on reservoir parameters, correcting the description model, and establishing a water drive reservoir production dynamic accurate description model;
and 4, developing a dynamic accurate description model by using water flooding, and obtaining an optimal injection-production parameter scheme by using an optimization algorithm and aiming at optimal production.
Preferably, the geological data comprises reservoir physical parameters, and the reservoir physical parameters comprise porosity, permeability, reservoir thickness, saturation, pressure, oil-water density, oil-water viscosity, a facies permeability curve and a reservoir rock compressibility.
Preferably, the production data comprises oil-water well positions, longitudinal layering conditions and historical production data.
Preferably, in step 2, the oil deposit is longitudinally and planarly doubly defined according to the separated mining and separate injection condition of the target oil deposit, the well position relation, geological development parameters and the like to obtain a series of single-layer inter-well circulation flow units, and the production dynamics in the units is calculated by utilizing a two-phase oil displacement theory, so that a water drive development dynamic initial description model is established.
Further preferably, the step of performing double delineation of the longitudinal and plane target reservoirs further comprises a step of performing two loop iterations of longitudinal delineation and plane delineation.
Preferably, the working system of each well on each layer is determined and obtained according to geological development parameters such as the allocation/injection amount of each layer section of each well, the seepage resistance of each layer and the like in the longitudinal direction; on the plane, according to the well position relation and seepage resistance in the well group, different inter-well communication flow units are defined on the plane of each layer, and the production dynamics in the units are calculated by utilizing the two-phase oil displacement theory.
Further preferably, the planar flow cell delineation for a single interval is described using flow cell area a, well spacing d, and delineation angle θ; in an injection and production well group, m oil wells are arranged by taking a water well as a center, and the defined angle corresponding to the flow unit of the ith oil well is as follows:
θi＝2παi
in the formula of alphaiAnd (3) setting coefficients for the ith oil well, wherein the solving formula is as follows:
in the formula, pi is the pressure difference between the water injection well and the ith oil well, and is MPa; m is the total number of production wells; ri is the production rate in the ith well direction.
Further preferably, for a multi-layer reservoir, due to the heterogeneity, the liquid volume of each interval also needs to be defined, and each layer liquid volume is defined according to the following formula:
where Ri is the defining coefficient of the i-th layer:
qi is the liquid volume of the ith interval, Ki is the permeability of the ith interval, Ai is the sectional area of the ith interval, Δ p is the injection-production differential pressure, Gi is the starting pressure of the ith interval, a is the unit conversion coefficient, d is the well spacing, λ ro is the oil phase fluidity, and λ rw is the water phase fluidity.
Preferably, in step 3, N sets of reservoir parameters are generated by using a CMA-ES algorithm, history fitting is performed by using a water-drive reservoir production dynamic initial description model, model reservoir parameters are continuously corrected and updated to obtain optimal reservoir parameters, and a water-drive reservoir production dynamic accurate description model is further obtained.
Preferably, in step 4, an MCS algorithm is used for generating N sets of injection-production schemes, and a water-drive oil reservoir production dynamic accurate description model is used for predicting a production dynamic result to obtain an optimal injection-production scheme.
Compared with the prior art, the invention has the following advantages:
the invention provides a simple and rapid water drive reservoir injection and production parameter optimization method, which is characterized in that a history fitting problem is converted into an optimal solution problem by establishing a water drive reservoir production dynamic initial model, an automatic history fitting technology is formed, a dynamic description model can be rapidly and accurately corrected and developed, and the injection and production parameters are rapidly and accurately optimized by taking production optimization as a target through an optimization algorithm.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
Fig. 1 is a flowchart of a segmented injection-production parameter optimization method based on planar flow unit planning according to an embodiment of the present invention;
FIG. 2 is a schematic illustration of a flow cell definition according to an embodiment of the present invention;
FIG. 3 is a flow chart of an automatic history fitting process according to an embodiment of the present invention;
fig. 4 is a flow chart of injection-production parameter optimization according to an embodiment of the present invention;
FIG. 5 is a graph of predicted production rate curves and actual production rate curves prior to history matching in accordance with an embodiment of the present invention;
FIG. 6 is a graph of predicted oil production rate curves and actual oil production rate curves after history matching according to an embodiment of the present invention;
fig. 7 is a comparison diagram of the effects of the optimal injection-production parameters according to an embodiment of the present invention, where a: optimizing the change condition of each oil well; b: and optimizing the change conditions of each water well before and after.
Detailed Description
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of the stated features, steps, operations, and/or combinations thereof, unless the context clearly indicates otherwise.
Embodiment 1 segmented injection-production parameter optimization method based on plane flow unit demarcation
As shown in fig. 1, the method comprises the steps of:
step 1, collecting geological and production data:
the reservoir physical property parameters comprise porosity, permeability, reservoir thickness, saturation, pressure, oil-water density, oil-water viscosity, a phase permeability curve and reservoir rock compression coefficient.
The production data comprises oil-water well positions, longitudinal layering conditions and historical production data.
Step 2, establishing a water drive reservoir production dynamic initial description model:
and performing longitudinal and plane double delineation on the oil reservoir according to the separate mining and separate injection condition, the well position relation, geological development parameters and the like of the target oil reservoir to obtain a series of single-layer inter-well circulation flow units, and calculating the production dynamics in the units by using a two-phase oil displacement theory so as to establish a water drive development dynamic initial description model.
In the step of double longitudinal and plane delineation of the target oil reservoir, the method further comprises two loop iteration links of longitudinal delineation and plane delineation: in the longitudinal direction, the working system of each well on each layer is determined according to geological development parameters such as the production allocation/injection allocation amount of each layer of each well, the seepage resistance of each layer and the like; on the plane, according to the well position relation and seepage resistance in the well group, different inter-well communication flow units are defined on the plane of each layer, and the production dynamics in the units are calculated by utilizing the two-phase oil displacement theory.
Wherein the planar flow cell delineation for a single interval is described using flow cell area a, well spacing d, and delineation angle θ; in an injection and production well group, m oil wells are arranged by taking a water well as a center, and the defined angle corresponding to the flow unit of the ith oil well is as follows:
θi＝2παi
in the formula of alphaiAnd (3) setting coefficients for the ith oil well, wherein the solving formula is as follows:
in the formula piThe pressure difference between the water injection well and the ith oil well is MPa; m is the total number of production wells; riThe production degree of the ith oil well direction.
The flow condition in each plane defining unit can be calculated by using Buckley-Leverett oil-water two-phase oil displacement theory.
For a multi-layer reservoir, due to the heterogeneity, the liquid amount of each layer needs to be defined, and the liquid amount of each layer is defined according to the following formula:
where Ri is the defining coefficient of the i-th layer:
qi is the liquid volume of the ith interval, Ki is the permeability of the ith interval, Ai is the sectional area of the ith interval, Δ p is the injection-production differential pressure, Gi is the starting pressure of the ith interval, a is the unit conversion coefficient, d is the well spacing, λ ro is the oil phase fluidity, and λ rw is the water phase fluidity.
Step 3, carrying out automatic history fitting on reservoir parameters, correcting the description model, and establishing a water drive reservoir production dynamic accurate description model:
and generating N sets of oil reservoir parameters by using a CMA-ES algorithm, performing history fitting by using a water-drive oil reservoir production dynamic initial description model, continuously correcting and updating model reservoir parameters to obtain optimal oil reservoir parameters, and further obtaining a water-drive oil reservoir production dynamic accurate description model.
And 4, step 4: developing a dynamic accurate description model by utilizing water flooding, and obtaining an optimal injection-production parameter scheme by taking production optimization as a target by virtue of an optimization algorithm:
and generating N sets of injection and production schemes by using an MCS algorithm, and predicting a production dynamic result by using a water-drive oil reservoir production dynamic accurate description model to obtain an optimal injection and production scheme.
Embodiment 2 segmented injection-production parameter optimization method based on plane flow unit demarcation
As shown in fig. 1, the method comprises the steps of:
step 1, collecting geological and production data:
the reservoir physical property parameters comprise porosity, permeability, reservoir thickness, saturation, pressure, oil-water density, oil-water viscosity, a phase permeability curve and reservoir rock compression coefficient.
The production data comprises oil-water well positions, longitudinal layering conditions and historical production data.
Step 2, establishing a water drive reservoir production dynamic initial description model:
and performing longitudinal and plane double delineation on the oil reservoir according to the separate mining and separate injection condition, the well position relation, geological development parameters and the like of the target oil reservoir to obtain a series of single-layer inter-well circulation flow units, and calculating the production dynamics in the units by using a two-phase oil displacement theory so as to establish a water drive development dynamic initial description model.
In the step of double longitudinal and plane delineation of the target oil reservoir, the method further comprises two loop iteration links of longitudinal delineation and plane delineation: in the longitudinal direction, the working system of each well on each layer is determined according to geological development parameters such as the production allocation/injection allocation amount of each layer of each well, the seepage resistance of each layer and the like; on the plane, according to the well position relation and seepage resistance in the well group, different inter-well communication flow units are defined on the plane of each layer, and the production dynamics in the units are calculated by utilizing the two-phase oil displacement theory.
As shown in fig. 2, the planar flow cell delineation for a single interval is described using flow cell area a, well spacing d, and delineation angle θ; in an injection and production well group, m oil wells are arranged by taking a water well as a center, and the defined angle corresponding to the flow unit of the ith oil well is as follows:
θi＝2παi
in the formula of alphaiAnd (3) setting coefficients for the ith oil well, wherein the solving formula is as follows:
in the formulapiThe pressure difference between the water injection well and the ith oil well is MPa; m is the total number of production wells; riThe production degree of the ith oil well direction.
The flow condition in each plane defining unit can be calculated by using Buckley-Leverett oil-water two-phase oil displacement theory.
For a multi-layer reservoir, due to the heterogeneity, the liquid amount of each layer needs to be defined, and the liquid amount of each layer is defined according to the following formula:
where Ri is the defining coefficient of the i-th layer:
qi is the liquid volume of the ith interval, Ki is the permeability of the ith interval, Ai is the sectional area of the ith interval, Δ p is the injection-production differential pressure, Gi is the starting pressure of the ith interval, a is the unit conversion coefficient, d is the well spacing, λ ro is the oil phase fluidity, and λ rw is the water phase fluidity.
Step 3, carrying out automatic history fitting on reservoir parameters, correcting the description model, and establishing a water drive reservoir production dynamic accurate description model:
and (3) establishing a mathematical method for the automatic history fitting problem by taking reservoir parameters as adjustment objects, optimizing the porosity, permeability, effective thickness and control area in each subdivision unit by taking the minimum difference between the calculation result of the oil reservoir production dynamic description model and the actual oil reservoir, and correcting the water drive dynamic description mathematical model.
An objective function:
constraint conditions are as follows:
as shown in the attached figure 3, by utilizing a CMA-ES self-adaptive intelligent optimization algorithm and taking the minimum value of the objective function as an optimization target, a production dynamic result is predicted by calling an oil reservoir production dynamic description model, and model reservoir parameters are continuously corrected and updated until a convergence condition is reached to obtain the optimal model reservoir parameters, so that the oil reservoir production dynamic description model capable of accurately describing the production dynamic of the water-drive oil reservoir is obtained. In iteration step k, the CMA-ES first samples the λ individuals to form populations according to the following formula
Wherein N (… ) is a random vector of a multivariate normal distribution; m iskIs an average vector; ckIs a covariance matrix; σ is the step size factor.
In the optimization process, the covariance matrix is updated according to the following formula
In the formula (I), the compound is shown in the specification,
becomes an evolutionary path to characterize the direction of optimization.
in the formula, ccIs a constant between 0 and 1;
for describing recombination.
Step size factor sigmak+1The determination formula is as follows
Wherein the content of the first and second substances,
for the conjugate search path, the following is determined
The covariance matrix is adaptively scaled by the step factor, so that a good convergence rate can be realized.
And 4, developing a dynamic accurate description model by using water flooding, and obtaining an optimal injection-production parameter scheme by using an optimization algorithm and aiming at optimal production:
on the basis of the obtained modified dynamic description model of the flooding oil reservoir production, different injection-production schemes are generated by means of the MCS multi-level intelligent optimization algorithm, the dynamic description model of the oil reservoir production is called to predict the dynamic production result, the injection-production parameters are continuously modified until the convergence condition is reached, and the injection-production scheme with the optimal production is obtained.
An objective function: the accumulated oil production is maximum in the future time from t0 to t1
Optimizing variables: production parameter Q for each interval of each well. Wherein q ism nRepresenting the production/injection allocation of the mth layer of the nth well.
Constraint conditions are as follows:
production allocation and injection allocation quantity q of each layer section of each wellijCannot exceed the set upper limit qij maxAnd a lower limit qij min：
Bottom hole pressure p corresponding to production allocation and injection allocation of each interval of each wellijCannot exceed a set upper limit pij maxAnd a lower limit pij min：
Total liquid production from each well was constant:
the total water injection amount of each water well is constant:
and (3) calling an oil reservoir production dynamic description model by utilizing an MCS (modulation and coding scheme) multi-level coordination search algorithm to predict production dynamic results under different injection-production parameter combinations, continuously correcting and updating injection-production parameters until convergence conditions are reached to obtain injection-production parameters corresponding to the optimal yield, wherein the solving process is shown as the attached figure 4.
The MCS specifically includes the following steps: 1) and (5) initializing. In the initialization step, the MCS splits the initial search space into a series of subspaces (called "boxes") by a preset initialization list. 2) And (6) scanning. And sequencing all the boxes according to the magnitude of the stage number, listing the boxes in a scanning list, and labeling the box with the minimum objective function value in each stage. The marked box enters the next splitting step for further splitting, and a new box generated by splitting is added into the scanning list and the scanning list is updated. 3) And (4) splitting. And for the box needing splitting, judging whether the box has been split for enough times, if so, further splitting along the direction with the least splitting times of the box by using a sequencing splitting method. Otherwise, using the expectation splitting method, the box is first estimated to obtain a more resolvable expectation of splitting in all directions, followed by splitting in the direction where the expectation is greatest. 4) A shopping basket. After the scanning is finished, all base points of the box reaching the maximum level are put into the shopping basket according to the size of the objective function value. And judging whether the point in the shopping basket is in a new local minimum area or not, if so, marking the point as an initial point of the next local search, otherwise, marking the point and the existing point in the shopping basket in the same local minimum area, and selecting the point with the better objective function value from the two points for reservation. 5) And (6) local searching. And taking the updated point in the shopping basket as an initial point to start local search. The local search combines the ideas of coordinated search and sequence quadratic programming, firstly a local quadratic form is constructed through a series of coordinated searches, then the search direction and the search step length are determined according to the sequence quadratic programming, the quadratic form is updated after the search is completed, and iteration is carried out in this way.
Example 3
Taking a certain block of the victory oil field as an example, the injection-production parameter optimization is carried out by adopting the method described in the embodiment 2.
The data of 9 wells in total in a certain area of the victory oil field are collected, and the comparison between the oil production speed variation curve of the predicted oil well 1 and the actual oil production speed variation curve of the oil well is shown in figure 5. It can be seen that the current predicted pay-per-oil velocity profile is significantly different from the actual pay-per-oil velocity profile, requiring automatic history fitting of reservoir parameters.
History fitting is carried out by referring to the automatic history fitting method for reservoir parameters described in the above embodiment 2, and a reservoir parameter combination corresponding to the most consistent actual reservoir development dynamic curve is found by multi-layer reservoir development dynamic prediction under 9000 different reservoir parameter combinations. The comparison of the change in production rate of well 1 with the actual change in production rate of that well for the combination of reservoir parameters is shown in FIG. 6. It can be seen that the currently predicted oil production speed curve and the actual oil production speed curve are well matched, and at the moment, the set of reservoir parameter combination can reflect the actual oil reservoir and can accurately predict future dynamics.
And (3) carrying out injection-production parameter optimization on the basis of automatic history fitting of reservoir parameters, and optimizing an injection-production parameter scheme by taking the maximum accumulated oil yield in the next 5 years as a target. The constraint condition is set to be in the range of 0-60m of the liquid quantity of the production well3D, the liquid amount of the water injection well ranges from 0 to 80m3And d. The total liquid amount is consistent with the original scheme. Through dynamic prediction of multi-layer oil reservoir development under different injection-production parameter combinations of more than 6000 times, an injection-production parameter scheme with the maximum accumulated oil production in the next 5 years is found, and the ratio of the injection-production parameter scheme to the original scheme is shown in fig. 7. Therefore, the optimal injection-production parameter scheme of the block is obtained.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (10)
1. The segmented injection-production parameter optimization method based on the plane flow unit is characterized by comprising the following steps of:
step 1, collecting geological and production data;
step 2, establishing a water drive reservoir production dynamic initial description model;
step 3, carrying out automatic history fitting on reservoir parameters, correcting the description model, and establishing a water drive reservoir production dynamic accurate description model;
and 4, developing a dynamic accurate description model by using water flooding, and automatically optimizing and solving injection-production parameters by using an optimization algorithm and taking production optimization as a target.
2. The method of claim 1, wherein the geological data comprises reservoir physical parameters including porosity, permeability, reservoir thickness, saturation, pressure, oil and water density, oil and water viscosity, facies permeability curves, and reservoir rock compressibility.
3. The method of claim 1, wherein the production data includes well location, longitudinal stratification, and historical production data.
4. The method of claim 1, wherein in step 2, the oil deposit is longitudinally and horizontally double-defined according to the separated mining and separate injection condition of the target oil deposit, well position relation, geological development parameters and the like to obtain a series of single-layer inter-well circulation flow units, and the production dynamics in the units is calculated by using a two-phase oil displacement theory, so that a water flooding development dynamic initial description model is established.
5. The method of claim 4, wherein the step of dual delineation of the target reservoir in the longitudinal and planar directions further comprises a step of two loop iterations of longitudinal delineation and planar delineation.
6. The method according to claim 4 or 5, characterized in that the working schedule of each well on each layer is defined and obtained according to geological development parameters such as the production allocation/injection allocation amount of each layer of each well and the seepage resistance of each layer in the longitudinal direction; on the plane, according to the well position relation and seepage resistance in the well group, different inter-well communication flow units are defined on the plane of each layer, and the production dynamics in the units are calculated by utilizing the two-phase oil displacement theory.
7. The method of claim 4 or 5, wherein the planar flow cell delineation for a single interval is described using flow cell area A, well spacing d, and delineation angle θ; in an injection and production well group, m oil wells are arranged by taking a water well as a center, and the defined angle corresponding to the flow unit of the ith oil well is as follows:
θi＝2παi
in the formula of alphaiAnd (3) setting coefficients for the ith oil well, wherein the solving formula is as follows:
in the formula, pi is the pressure difference between the water injection well and the ith oil well, and is MPa; m is the total number of production wells; ri is the production rate in the ith well direction.
8. The method of claim 4 or 5, wherein for a multi-layered reservoir, due to the heterogeneity, the liquid volume of each interval is also defined, and the liquid volume of each layer is defined according to the following formula:
where Ri is the defining coefficient of the i-th layer:
qi is the liquid volume of the ith interval, Ki is the permeability of the ith interval, Ai is the sectional area of the ith interval, Δ p is the injection-production differential pressure, Gi is the starting pressure of the ith interval, a is the unit conversion coefficient, d is the well spacing, λ ro is the oil phase fluidity, and λ rw is the water phase fluidity.
9. The method of claim 1, wherein in step 3, N sets of reservoir parameters are generated by using a CMA-ES algorithm, history fitting is performed by using a water-drive reservoir production dynamic initial description model, reservoir parameters of the updated model are continuously corrected to obtain optimal reservoir parameters, and a water-drive reservoir production dynamic accurate description model is further obtained.
10. The method of claim 1, wherein in step 4, N sets of injection and production schemes are generated by using MCS algorithm, and the production dynamic result is predicted by using the water-drive reservoir production dynamic accurate description model to obtain the optimal injection and production scheme.
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| CN115324543A (en) * | 2022-09-14 | 2022-11-11 | 陕西延长石油（集团）有限责任公司 | Well group injection-production pressure difference optimization method based on synchronous water breakthrough of oil production well |
| CN116562126A (en) * | 2023-04-12 | 2023-08-08 | 西南石油大学 | Optimal design method and system for geological sequestration parameters of exhausted gas reservoir carbon dioxide |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115324543A (en) * | 2022-09-14 | 2022-11-11 | 陕西延长石油（集团）有限责任公司 | Well group injection-production pressure difference optimization method based on synchronous water breakthrough of oil production well |
| CN115324543B (en) * | 2022-09-14 | 2023-11-28 | 陕西延长石油（集团）有限责任公司 | Well group injection and production pressure difference optimization method based on synchronous water breakthrough of oil production well |
| CN116562126A (en) * | 2023-04-12 | 2023-08-08 | 西南石油大学 | Optimal design method and system for geological sequestration parameters of exhausted gas reservoir carbon dioxide |
| CN116562126B (en) * | 2023-04-12 | 2024-02-06 | 西南石油大学 | Optimal design method and system for geological sequestration parameters of exhausted gas reservoir carbon dioxide |
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