CN107766978B - Intelligent optimization method for irregular well pattern - Google Patents
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Abstract
The invention discloses an intelligent optimization method of an irregular well pattern, and belongs to the technical field of oil exploitation. The invention comprises the following steps: extracting the geometric boundary of the block; generating initial well pattern parameters to be optimized: row spacing, well spacing and well row direction; randomly selecting one point as an original point for generating an initial well pattern, generating a basic well row, and moving the basic well row to generate the initial well pattern; performing numerical reservoir simulation under the initial well pattern to obtain daily oil production, daily water production and daily gas production curves of the whole reservoir; optimizing the initial well pattern parameters by adopting an intelligent optimization method by taking the recovery ratio or the net present value as a target function to obtain an optimal parameter combination; and generating an optimal well pattern according to the obtained optimal parameter combination. The invention fully considers the factors of oil reservoir boundary and fault position, reservoir anisotropy, heterogeneity, crack development area and the like, can improve the oil reservoir recovery ratio or net present value, and can be directly used for the design of oil and gas reservoir development schemes and drilling construction.
Description
Technical Field
The invention relates to the technical field of oil exploitation, in particular to an intelligent optimization method for an irregular well pattern.
Background
The complex fault block oil reservoir has small size and different shapes due to the fault block segmentation function, and each fault block is an independent development unit. During development, because the area of the fault block is small, well arrangement and production are difficult to perform by using a well pattern with a perfect conventional area, so that the small fault block oil reservoir can be developed by adopting an imperfect well pattern.
In the well location deployment process, the aim of improving the recovery ratio (or maximizing the net present value) is achieved by designing and adjusting a well pattern structure. Aiming at small fault block oil reservoirs, currently, the method mainly comprises artificial formulation and an automatic well position optimization method based on an optimization theory in China, and optimization is carried out on the basis, and at present, two methods are mainly used for well pattern optimization of small fault blocks:
1) according to the geological condition of the oil reservoir, a plurality of well pattern schemes are artificially made, an oil reservoir numerical simulation method is adopted for simulation calculation, and calculation results of the schemes are compared, so that the optimal scheme is determined. The method is simple and easy to implement, but depends too much on human experience, and because the oil reservoir geological uncertainty factors are many, the number of artificially made schemes is limited, and the optimal solution is not easy to obtain.
2) An automatic well position optimization method based on an optimization theory is adopted. The method is mainly characterized in that a target function (net present value or recovery ratio and the like) is set according to needs, and then a numerical simulation software is called to obtain a solution enabling the target function to be optimal by means of a certain intelligent optimization algorithm (particle swarm, genetic algorithm, simulated annealing and the like). The method can greatly reduce the manual workload.
Compared with the first method, the second well pattern optimization method based on the intelligent optimization algorithm is more general, so that the future application prospect is wider.
The main differences of the existing intelligent well pattern optimization methods are mainly focused on different optimization algorithms adopted in programs, and the optimization algorithms comprise a genetic algorithm, a particle swarm algorithm, an ant colony algorithm, a simulated annealing algorithm, a predation search algorithm and the like. The main differences between the different algorithms are the convergence speed and the amount of computation. As long as the computing resources and the computing time are sufficient, the results of different optimization algorithms have no obvious difference. Thus, the nature and key points of the well pattern optimization problem are not the intelligent optimization algorithm within it, but rather the target to be optimized itself. The objectives of current well pattern optimization studies can be divided into the following two categories:
a) directly taking the single well position as an optimization target. The method optimizes the well position of each well independently, the final well position is scattered, and a well pattern structure cannot be formed, so that the method cannot be used for optimizing a well pattern for area water injection and gas injection, and meanwhile, due to the interference problem among different wells, the optimization result is not an optimal well arrangement scheme generally.
b) After determining the well pattern types (five points, seven points, nine points and the like), optimizing the well spacing, the row spacing and the parallel angle of the well pattern, and obtaining the optimal well spacing, row spacing and parallel angle parameters through repeated numerical simulation and iterative evolution. The method has the defects that the optimized well pattern can only be a strict regular well pattern, but the trapping range of the actual oil and gas reservoir is irregular, the geological structure of most oil reservoirs is complex, and oil layers are distributed in various ways, so that the maximum production benefit cannot be obtained by adopting the strict regular well pattern (namely, the well spacing and the row spacing are always kept consistent).
In addition, in the actual production process, after the deployment of the regular well pattern is completed, fine adjustment is needed according to factors such as oil reservoir boundaries, fault positions, reservoir anisotropy, heterogeneity, fracture development areas and the like, so that the oil and gas recovery rate or the maximum net present value is improved as much as possible.
Disclosure of Invention
The invention provides an intelligent optimization method of an irregular well pattern, which fully considers factors such as oil reservoir boundaries, fault positions, reservoir anisotropy, heterogeneity, fracture development areas and the like, can improve the oil reservoir recovery ratio or net present value, and can be directly used for designing oil and gas reservoir development schemes and drilling construction.
In order to solve the technical problems, the invention provides the following technical scheme:
a method of intelligent optimization of an irregular well pattern, comprising:
step 1: extracting the geometric boundary of the block;
step 2: generating initial well pattern parameters to be optimized: row spacing, well spacing and well row direction;
and step 3: randomly selecting one point as an original point for generating an initial well pattern, generating a basic well row, and moving the basic well row to generate the initial well pattern;
and 4, step 4: performing numerical reservoir simulation under the initial well pattern to obtain daily oil production, daily water production and daily gas production curves of the whole reservoir;
and 5: optimizing the initial well pattern parameters by adopting an intelligent optimization method by taking the recovery ratio or the net present value as a target function to obtain an optimal parameter combination;
step 6: and generating an optimal well pattern according to the obtained optimal parameter combination.
Further, the dead mesh is removed when extracting the geometric boundary.
Further, the step 2 comprises:
step 21: setting a representative point set P in the plane of the space where the oil deposit is located, wherein the representative points are uniformly distributed in the oil deposit plane, and recording the coordinate of a representative point pi as (al)p，blp)，lpIs the distance between adjacent representative points, a and b are arbitrary natural numbers, and determines the l of the representative points in the blockpAn initial value of (1);
step 22: determining three to-be-optimized parameter row spacing c of any representative point piiWell spacing diAnd well row direction thetaiThe reasonable distribution range of the components;
step 23: for the representative point piThree parameters of row spacing ciWell spacing diAnd well row direction thetaiAnd assigning values to obtain initial well pattern parameters.
Further, in step 21, the distance between two representative points is lp200 to 400 m.
Further, in step 22, the pitch ciWell spacing diAnd well row direction thetaiThe reasonable distribution range is as follows: c is more than or equal to 100mi≤400m，100m≤di≤400m，0°≤θi≤90°。
Further, step 3 comprises:
step 31: at the origin W11Generating a basic well row by using a two-dimensional plane difference method as a starting point;
step 32: moving the basic well row to generate a basic well pattern;
step 33: removing wells outside the block;
step 34: designating each well of the basic well pattern within the block as either an injection well or a production well;
step 35: an initial well pattern is generated from the base well pattern map.
Further, in step 34, an injection well or a production well is specified according to the well pattern types, wherein the well pattern types are a depleted production well pattern, a five-point well pattern, a nine-point well pattern, a reverse nine-point well pattern, a seven-point well pattern and a reverse seven-point well pattern.
Further, all wells in the depleted production well pattern are production wells;
the five-point well pattern comprises a row of spaced extraction wells and injection wells, and an adjacent row of spaced injection wells and extraction wells;
the nine-point well pattern is a row of extraction wells, and an adjacent row of spaced extraction wells and injection wells;
the inverted nine-point well pattern is a row of injection wells, an adjacent row of interval injection wells and an adjacent row of interval extraction wells;
the seven-point well pattern comprises a row of two extraction wells and one injection well at intervals, and an adjacent row of one injection well and two extraction wells at intervals;
the reverse seven-point well pattern comprises a row of one extraction well and two injection wells at intervals, and an adjacent row of two injection wells and one extraction well at intervals.
Further, in the step 5, the intelligent optimization method includes a genetic algorithm, a particle swarm algorithm, an ant colony algorithm, a simulated annealing algorithm, and a predation search algorithm.
Further, in step 5, the objective function is a recovery factor, and the number of wells in the optimized well pattern needs to be set to an upper well placement limit.
The invention has the following beneficial effects:
1) the invention is an intelligent, automatic optimization method of the development well pattern of oil and gas reservoir, the optimization method of the invention is the intelligent optimization based on numerical simulation result of the geological model, factors such as the reservoir anisotropy, heterogeneity, crack development area of the block can be reflected in the geological model, so the intelligent optimization method of the irregular well pattern of the invention is to fully consider the factors such as the reservoir anisotropy, heterogeneity, crack development area, etc.;
2) according to the method, an initial well pattern is generated through initial well pattern parameters, then numerical simulation is carried out, the initial well pattern parameters are optimized by adopting an intelligent optimization method, an optimal parameter combination is obtained, and a well pattern structure which is most suitable for the block is further generated;
3) the invention overcomes the defects that the traditional well pattern optimization method by manual comparison excessively depends on human experience and has large manual workload; meanwhile, the defect that a well pattern obtained by a traditional regular well pattern optimization method cannot be well adapted to a specific oil reservoir is overcome, and the well pattern obtained by the method can be directly applied to design of an oil-gas reservoir development scheme and drilling construction.
Drawings
FIG. 1 is a flow chart of a method of intelligent optimization of an irregular well pattern of the present invention;
FIG. 2 is a geological model and permeability distribution of a block in example 1 of the present invention;
FIG. 3 is a geometric boundary extracted according to a geological model in embodiment 1 of the present invention;
FIG. 4 is an initial well pattern parameter to be optimized in example 1 of the present invention;
FIG. 5 is a basic well row in example 1 of the present invention;
FIG. 6 is an initial well pattern generated by moving a base well row in example 1 of the present invention;
FIG. 7 is a diagram of the well pattern remaining after removing the wells other than the block in example 1 of the present invention;
FIG. 8 is a well pattern after designation of an injection well or a production well for each well of the base well pattern within a block in example 1 of the present invention;
FIG. 9(a) is the 1 st generation optimized using particle swarm optimization in example 1 of the present invention;
FIG. 9(b) is the 5 th generation optimized using particle swarm optimization in example 1 of the present invention;
FIG. 9(c) shows the generation 9 optimized by particle swarm optimization in example 1 of the present invention;
fig. 9(d) shows the 17 th generation optimized by the particle swarm optimization in example 1 of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
The invention needs to establish a complete parameter system and a corresponding method for constructing a well pattern, and requires that:
1) the physical meanings of the parameters are clear and independent;
2) using this set of parameters, a unique injection and production well pattern can be constructed.
The two requirements are met, the set of parameters can be optimized through any intelligent optimization method, and the parameters and the well pattern are in one-to-one correspondence, so that the optimization of the well pattern is realized.
The invention provides an intelligent optimization method of an irregular well pattern, which comprises the following steps of:
step 1: extracting the geometric boundary of a block, specifically, extracting the geometric boundary outside an oil reservoir from an oil reservoir geological model obtained by geological modeling, wherein factors such as reservoir anisotropy, heterogeneity, fracture development area and the like of the block need to be considered during geological modeling, and the geological model is established according to a conventional geological modeling process and is not repeated here; the geometric boundary is marked as L, the geometric boundary L can be a closed boundary formed by faults or a manually drawn geometric boundary, and after the geometric boundary L is determined, future well layout work is only carried out in the L; it should be noted that when extracting the geometric boundary, a dead grid (ACTNUN ═ 0 grid) needs to be planed out, because well placement in the dead grid is also ineffective;
step 2: generating initial well pattern parameters to be optimized: row spacing ciWell spacing diAnd well row direction thetaiSpecifically, the method comprises the following steps:
step 21: setting a representative point set P in the plane of the space where the oil deposit is located, uniformly distributing the representative points in the plane of the oil deposit, and recording the representative points PiHas the coordinates of (al)p，blp)，lpIs the distance (fixed value) between adjacent representative points, a, b are arbitrary natural numbers, such piThere are infinite, only those in the block range are reserved, and the number of the representative points finally reserved is recorded as npThe distance l between the representative pointspThe smaller the representative point, the more the representative point, the finer the optimization result, but the larger the calculation amount of the optimization, and therefore, lpThe value of (c) is determined according to the actual geological conditions (such as reservoir anisotropy, heterogeneity, etc.) of the reservoir, the stronger the reservoir heterogeneity, the stronger the value of lpThe smaller should be, and the larger may be the other way around; in general, |pBetween 200m and 400 m;
step 22: determining any representative point piThree row spacing c of parameters to be optimizediWell spacing diAnd well row direction thetaiIs reasonableThe distribution range; the step needs to be analyzed and determined by combining specific reservoir characteristics, and generally, three parameters to be optimized can be set as follows: c is more than or equal to 100mi≤400m，100m≤di≤400m，0°≤θiNot more than 90 degrees; wherein, the range of the three parameters to be optimized covers the value possibility of most of the reservoir displacement, well spacing and well displacement directions, but if the reservoir permeability is low (for example, the reservoir permeability is low)<1mD), c can bei、diThe upper limit of the value of (a) is enlarged to 600 m;
step 23: for the representative point piThree parameters of row spacing ciWell spacing diAnd well row direction thetaiAssigning values to obtain initial well pattern parameters; when assigning, assigning randomly, but ensuring that the random value is within a reasonable range in the step 22;
and step 3: randomly selecting one point as an original point for generating an initial well pattern, generating a basic well row, and moving the basic well row to generate the initial well pattern; specifically, the method comprises the following steps:
step 31: randomly selecting one point in the oil reservoir as an origin W for generating a basic well pattern11I.e. the first well of the first well row, at the origin W11Generating a basic well row by using a two-dimensional plane difference method for a starting point, specifically, firstly using the two-dimensional plane difference method to generate a basic well row according to the well spacing d on the representative point set PiThe data field is constructed, and W is calculated11Well spacing d at points11According to the well row direction theta on the representative point set PiThe data field is constructed, and W is calculated11Well row direction on point theta11According to d11、θ11The second well W on the same well bank can be calculated12(x12，y12) Is calculated by the formula x12＝x11+d11cosθ11，y12＝y11+d11sinθ11Repeating the operation circularly to generate all wells on the well row;
step 32: moving the basic well pattern to form a basic well pattern, in particular, the wells W on the basic well pattern1iIn the direction of well row theta1iMovement c1iA distance of (i) thatForming a well W in a second well bank2iWherein c is1iIs W1iThe row spacing of the dots is W1iPosition of (2), pitch c on the representative point set PiThe operation is repeated circularly after the difference values in the formed data field are obtained, and finally a basic well pattern W covering all block sides can be generated;
step 33: removing wells outside the block: for each well W in WijJudging whether it is in the block range L, if WijThe well is retained inside L, otherwise, the well is deleted from the set W;
step 34: designating each well of the basic well pattern within the block as either an injection well or a production well; this step needs to be performed according to the selected pattern type, and the selectable pattern types are: the method comprises the following steps of failing to produce a well pattern, a five-point well pattern, a nine-point well pattern, a reverse nine-point well pattern, a seven-point well pattern and a reverse seven-point well pattern, wherein the method specifically comprises the following steps of:
a. failure of the production well pattern: all wells are production wells;
b. five-point well pattern: a row of spaced production wells and injection wells, and an adjacent row of spaced injection wells and production wells;
c. nine-point well pattern: a row of production wells, an adjacent row of spaced production wells and an adjacent row of spaced injection wells;
d. and (3) reversing a nine-point well pattern: a row of injection wells, an adjacent row of interval injection wells and an adjacent row of interval extraction wells;
e. a seven-point well pattern: a row of spaced production wells (2) and injection wells (1), and an adjacent row of spaced injection wells (1) and production wells (2);
f. reverse seven-point well pattern: one row of spaced extraction wells (one opening) and one injection well (2 openings), and one adjacent row of spaced injection wells (2 openings) and one extraction well (1 opening);
step 35: generating an initial well pattern according to the basic well pattern mapping;
and 4, step 4: performing numerical reservoir simulation under the obtained initial well pattern to obtain daily oil production, daily water production and daily gas production curves of the whole reservoir, and calculating the recovery ratio or net present value on the basis of the daily oil production, the daily water production and the daily gas production curves; the oil deposit numerical simulation is a conventional technical means for calculating the flowing process of oil, gas and water in a stratum by using a computer, can give the oil-gas-water distribution at any moment and predict the oil deposit dynamic, firstly, the oil deposit numerical simulation needs to establish an accurate geological model (a grid model and an attribute model), on the basis, fluid high-pressure physical property data and phase permeability curve data need to be prepared, and then, the fluid flow in all grids is calculated according to a mass conservation equation;
and 5: taking the recovery ratio or the net present value as an objective function, adopting an intelligent optimization method, repeatedly carrying out well arrangement and numerical simulation (namely, repeating the step 3 and the step 4), and carrying out optimization on the parameters (the row spacing c) to be optimized on all the representative pointsiWell spacing diAnd well row direction thetai) Optimization is carried out, and it is required to point out that:
a. any intelligent optimization algorithm (i.e. heuristic optimization algorithm) can be used for the optimization of the step, including but not limited to genetic algorithm, particle swarm algorithm, ant colony algorithm, simulated annealing algorithm, predation search algorithm, etc.; taking the particle swarm algorithm as an example, the optimization process is as follows: the method comprises the steps that a group of random particles (parameter random combination) is initially adopted, in each iteration, an objective function value corresponding to the particles is calculated through numerical simulation, the particles update the positions (parameter setting) of the particles by tracking two extreme values, the first parameter combination is the parameter combination when the particles find the maximum objective function, and the solution is called an individual extreme value; the other extreme value is the parameter combination when the maximum objective function is found in the whole population at present, the extreme value is a global extreme value, and the optimal solution can be found by repeatedly repeating the process;
b. the recovery factor or the net present value can be used as an objective function of optimization, and specifically which parameter is used needs to be determined according to the overall planning and long-term objective of oil reservoir development (when the recovery factor is used as the optimization objective, the upper limit W of well placement generally needs to be setmaxI.e. the number of wells in the optimized well pattern is required to be less than or equal to WmaxAt this time, the problem is converted into a simple constrained optimization problem, and a penalty function or a tabu search strategy can be considered in the intelligent optimization algorithm for solving);
step 6: after the optimization is completed, the optimal parameter combination (the row on the representative point set P) is obtainedDistance ciWell spacing diAnd well row direction thetai) And (5) repeating the step (3) to generate an optimal well pattern.
Example 1:
taking F oil field B block as an example, the intelligent optimization method adopts a particle swarm algorithm, namely PSO algorithm, and the fitting target is the maximum net present value:
1. reservoir boundaries (i.e., reservoir boundaries) for the extraction tiles:
extracting an outer boundary (as shown in figure 3) of a reservoir (as shown in figure 2) from a reservoir geological model (as shown in figure 2) obtained by geological modeling, and removing dead grids when attention is paid to extracting the boundary, wherein figure 2 is a geological model and permeability distribution of a block;
2. distributing representative points and assigning initial values to three parameters (well spacing, row spacing and well row direction) of each reference point:
uniformly arranging representative points (as shown in figure 4) on a plane where an oil reservoir block is located, and giving initial values to three parameters (well spacing, row spacing and well arrangement direction) of the representative points, wherein black solid dots are artificially arranged representative points in figure 4, each representative point is provided with three parameters, namely the well spacing (indicated by the length of a gray arrow), the row spacing (indicated by the length of a black arrow) and the well arrangement direction (indicated by the direction of a gray arrow), and a hollow dot is a well pattern generation origin W11The location of the well (which can be arbitrarily chosen);
3. generating an entire initial well pattern from the parameter values at the representative points:
firstly, starting from a well pattern generation origin, generating a basic well row (as shown in figure 5); moving the basic well row to generate a basic well pattern (as shown in figure 6); then, removing the wells outside the work area, and only keeping the effective wells (as shown in figure 7); finally, according to the selected well pattern type (taking a five-point well pattern as an example), dividing the effective wells into two groups of an injection well and a production well (such as a figure 8), and completing well pattern generation, wherein black solid dots in figures 5-7 represent wells, black dots in figure 8 represent production wells, gray dots represent injection wells, and gray continuous production parts are complete well groups of injection and production wells;
4. a development numerical simulation was performed, calculating an optimized "objective function":
on the basis of well arrangement, carrying out oil reservoir development numerical simulation on the block, and calculating an optimized 'objective function' (recovery ratio or net present value) according to a simulation result;
5. the parameters at the representative points are optimized to maximize the objective function:
and (3) optimizing three parameters (well spacing, row spacing and well row direction) on all reference points by adopting a particle swarm optimization, and as the optimization is carried out, for example, the optimized generation 1 in the graph 9(a), the optimized generation 5 in the graph 9(b), the optimized generation 9 in the graph 9(c) and the optimized generation 17 in the graph 9(d), the well pattern structure is more and more adapted to the work area of the research, and finally the optimal well pattern is obtained.
In conclusion, the invention has the following beneficial effects:
1) the invention is an intelligent, automatic optimization method of the development well pattern of oil and gas reservoir, the optimization method of the invention is the intelligent optimization based on numerical simulation result of the geological model, factors such as the reservoir anisotropy, heterogeneity, crack development area of the block can be reflected in the geological model, so the intelligent optimization method of the irregular well pattern of the invention is to fully consider the factors such as the reservoir anisotropy, heterogeneity, crack development area, etc.;
2) according to the method, an initial well pattern is generated through initial well pattern parameters, then numerical simulation is carried out, the initial well pattern parameters are optimized by adopting an intelligent optimization method, an optimal parameter combination is obtained, and a well pattern structure which is most suitable for the block is further generated;
3) the invention overcomes the defects that the traditional well pattern optimization method by manual comparison excessively depends on human experience and has large manual workload; meanwhile, the well pattern obtained by the traditional regular well pattern optimization method can not be well adapted to a specific oil reservoir, can be directly applied to the design of an oil-gas reservoir development scheme and drilling construction, has good application prospect, and is particularly applicable to strong heterogeneous oil reservoirs such as compact sandstone reservoirs, fracture development reservoirs and the like.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (8)
1. An intelligent optimization method for an irregular well pattern, comprising:
step 1: extracting the geometric boundary of the block;
step 2: generating initial well pattern parameters to be optimized: row spacing, well spacing and well row direction;
and step 3: randomly selecting one point as an original point for generating an initial well pattern, generating a basic well row, and moving the basic well row to generate the initial well pattern;
and 4, step 4: performing numerical reservoir simulation under the initial well pattern to obtain daily oil production, daily water production and daily gas production curves of the whole reservoir;
and 5: optimizing the initial well pattern parameters by adopting an intelligent optimization method by taking the recovery ratio or the net present value as a target function to obtain an optimal parameter combination;
step 6: generating an optimal well pattern according to the obtained optimal parameter combination;
the step 2 comprises the following steps:
step 21: setting a representative point set P in the plane of the space where the oil deposit is located, wherein the representative points are uniformly distributed in the oil deposit plane, and recording the coordinate of a representative point pi as (al)p，blp)，lpIs the distance between adjacent representative points, a and b are arbitrary natural numbers, and determines the l of the representative points in the blockpAn initial value of (1);
step 22: determining three to-be-optimized parameter row spacing c of any representative point piiWell spacing diAnd well row direction thetaiThe reasonable distribution range of the components;
step 23: for three parameters of row spacing c representing point piiWell spacing diAnd well row direction thetaiAssigning values to obtain initial well pattern parameters;
the step 3 comprises the following steps:
step 31: at the origin W11Generating a basic well row by using a two-dimensional plane difference method as a starting point;
step 32: moving the basic well row to generate a basic well pattern;
step 33: removing wells outside the block;
step 34: designating each well of the basic well pattern within the block as either an injection well or a production well;
step 35: an initial well pattern is generated from the base well pattern map.
2. The intelligent optimization method for irregular well patterns according to claim 1, characterized in that in step 1, dead meshes are removed when extracting geometric boundaries.
3. The intelligent optimization method for irregular well patterns according to claim 1, characterized in that, in the step 21, the distance between two representative points is lp200 to 400 m.
4. The intelligent optimization method for irregular well patterns according to claim 1, wherein in step 22, the row pitch ciWell spacing diAnd well row direction thetaiThe reasonable distribution range is as follows: c is more than or equal to 100mi≤400m，100m≤di≤400m，0°≤θi≤90°。
5. The intelligent optimization method for irregular well patterns according to claim 1, wherein the injection well or the production well is designated in step 34 according to well pattern types, and the well pattern types are failure production well pattern, five-point well pattern, nine-point well pattern, seven-point well pattern and seven-point well pattern.
6. The intelligent optimization method for an irregular well pattern as defined in claim 5, wherein all wells in the depleted production well pattern are production wells;
the five-point well pattern comprises a row of spaced extraction wells and injection wells, and an adjacent row of spaced injection wells and extraction wells;
the nine-point well pattern is a row of extraction wells, and an adjacent row of spaced extraction wells and injection wells;
the inverted nine-point well pattern is a row of injection wells, an adjacent row of interval injection wells and an adjacent row of interval extraction wells;
the seven-point well pattern comprises a row of two extraction wells and one injection well at intervals, and an adjacent row of one injection well and two extraction wells at intervals;
the reverse seven-point well pattern comprises a row of one extraction well and two injection wells at intervals, and an adjacent row of two injection wells and one extraction well at intervals.
7. The intelligent optimization method for irregular well patterns according to claim 1, wherein in the step 5, the intelligent optimization method comprises genetic algorithm, particle swarm algorithm, ant colony algorithm, simulated annealing algorithm and predation search algorithm.
8. The intelligent optimization method for irregular well patterns according to claim 1, wherein in the step 5, the objective function is recovery factor, and the number of wells of the well pattern after optimization needs to set the upper well spacing limit.
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| CN112459763A (en) * | 2019-09-06 | 2021-03-09 | 中国石油天然气股份有限公司 | Optimal arrangement method and device for gas wells in gas field |
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| CN115169761B (en) * | 2022-09-08 | 2022-12-13 | 中科数智能源科技(深圳)有限公司 | GNN and LSTM based complex well pattern oil field yield prediction method |
| CN116108704B (en) * | 2023-04-12 | 2023-06-13 | 中国石油大学(华东) | Method for optimizing geothermal Tian Shiliang well pattern based on intelligent algorithm |
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5360010A (en) * | 1990-05-02 | 1994-11-01 | Board Of Regents, The University Of Texas System | Vascular entoptoscope |
| CN205532555U (en) * | 2016-03-28 | 2016-08-31 | 中国石油天然气股份有限公司 | Well pattern structure |
| CN106703779A (en) * | 2016-12-02 | 2017-05-24 | 中国石油化工股份有限公司 | Injection-production well pattern construction method suitable for carbonate fractured-cave reservoirs |
| CN106996287A (en) * | 2017-06-08 | 2017-08-01 | 成都北方石油勘探开发技术有限公司 | The flooding pattern arrangement method of sealene triangle oil reservoir |
-
2017
- 2017-10-23 CN CN201710990708.XA patent/CN107766978B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5360010A (en) * | 1990-05-02 | 1994-11-01 | Board Of Regents, The University Of Texas System | Vascular entoptoscope |
| CN205532555U (en) * | 2016-03-28 | 2016-08-31 | 中国石油天然气股份有限公司 | Well pattern structure |
| CN106703779A (en) * | 2016-12-02 | 2017-05-24 | 中国石油化工股份有限公司 | Injection-production well pattern construction method suitable for carbonate fractured-cave reservoirs |
| CN106996287A (en) * | 2017-06-08 | 2017-08-01 | 成都北方石油勘探开发技术有限公司 | The flooding pattern arrangement method of sealene triangle oil reservoir |
Non-Patent Citations (1)
| Title |
|---|
| 基于改进粒子群算法的不规则井网自动优化;丁帅伟 等;《中国海上油气》;20160229;全文 * |
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