CN114330005B - Global optimization and decision-making method for three-dimensional development well pattern - Google Patents
Global optimization and decision-making method for three-dimensional development well pattern Download PDFInfo
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Abstract
The invention discloses a global optimization and decision-making method for a three-dimensional development well pattern. Aiming at the characteristics of three-dimensional well pattern development, the invention can model each development unit, calculate the optimal solution set based on the optimization method, establish a decision diagram based on given decision/optimization target parameters, realize visual evaluation of the position and optimization potential of each development unit in the whole, further give out comprehensive decision coefficients and give out quantitative parameters for whether the scheme is optimized. Further, on the level of development units of all three-dimensional well patterns, the evaluation of the optimal scheme of each development layer under the given parameter combination, the decision of the global optimal scheme and the decision diagram and the comprehensive decision coefficient are realized. The invention is an effective global decision method for three-dimensional well pattern development, which can provide support for the global optimization of the three-dimensional well pattern development and provide positive guidance for the whole development design of the three-dimensional well pattern and the correction of the follow-up scheme.
Description
Technical Field
The invention relates to a global optimization and decision-making method of a three-dimensional development well pattern, and belongs to the field of oil and gas field development engineering research.
Background
In the development process of the tight reservoir, a horizontal well three-dimensional well arrangement method is required to be developed so as to achieve the maximum and optimal development effect. But the field design and development parameters are numerous, how to integrate various geological, engineering and economic parameters, and determining the well pattern and well spacing in the development scheme to achieve the optimal parameter combination, so that the development target is optimal, is an important engineering problem. When the well pattern well spacing is designed, a series of problems such as geological conditions, engineering conditions, economic conditions and the like are required to be comprehensively optimized, and the association rule is clarified. And further, the overall benefit is taken as an optimization direction, the research of a multi-element collaborative optimization method of the three-dimensional development well pattern is developed, guidance is provided for realizing global optimization and efficient development of the three-dimensional development well pattern, and support and suggestion are provided for efficient development of oil reservoirs.
At present, in the actual design and construction process of a development site, a common method mainly refers to continuous iterative updating and optimization of adjacent block parameters, field development tests, field experience and numerical simulation. However, various parameter combination methods are very many, difficult to traverse completely, long in time consumption and high in field practice economic cost, and it is difficult to find the globally optimal well pattern well spacing and parameter combination to achieve the optimal development target. Therefore, a set of well spacing global decision-making method for three-dimensional well pattern development is required to be provided, and guidance is provided for subsequent design and development.
Disclosure of Invention
The invention aims to provide a global optimization and decision-making method of a three-dimensional development well pattern, which provides guidance for subsequent development.
The global optimization and decision-making method for the three-dimensional development well pattern provided by the invention comprises the following steps:
s1, dividing development units in the longitudinal direction according to basic physical properties of a target reservoir, wherein the development units are overlapped and seepage fields are mutually independent;
S2, constructing a reasonable well spacing and target parameter calculation model, calculating a pareto optimal solution set of each development unit by using the reasonable well spacing and target parameter calculation model, selecting a solution with the minimum well spacing L well from the pareto optimal solution set as an optimal solution, taking a value of L well,IRR,R1 corresponding to the optimal solution as an optimal target value, taking the optimal target value and a value of an optimized parameter corresponding to the optimal target value as an optimization result of the development unit, and selecting a value of N uni_well_H,IRR,R1 from the optimization result to form an optimal solution set (N uni_well_H,IRR,R1);
S3, establishing a decision graph; specifically: taking N uni_well_H,IRR,R1 as the x coordinate, the y coordinate and the z coordinate of the three-dimensional rectangular coordinate system, taking the optimal solution set (N uni_well_H,IRR,R1) of each development unit as a point, and drawing in a decision diagram;
S4, establishing a comprehensive decision coefficient: specifically, a weighting method is used for carrying out weighted summation on N uni_well_H,IRR,R1 to obtain a comprehensive decision coefficient E:
Wherein w 1、w2、w3 is a weighting coefficient, the value of a specific development block is given by an expert scoring method, N uni_well_H(q),IRR(q),R1 (q) respectively represents single-well control reserves N uni_well_H in the optimal solution set of the q-th development unit in the j development units in the longitudinal direction, the average internal yield IRR of the single well and the average recovery ratio R 1 of the single well, and the larger the comprehensive decision coefficient E is, the better the reservoir development effect is;
S5, performing well spacing global decision of the three-dimensional development well pattern by using the decision diagram and the comprehensive decision coefficient:
S5 a) developing a global decision of priority for the development unit: based on the points of all the development units in the decision diagram in the step S3, performing spatial clustering calculation, dividing the development units into 3 classes, and solving the comprehensive coefficient E of all the development units in each class, wherein the development unit with the largest E value is used as the development unit for priority development, and the development is performed in priority; a development unit of a type with a medium E value, which is used for development with a second priority, and selectively develops; the type with the minimum E value is used as a low-benefit development unit to suspend development;
S5 b) decision to develop priorities for a plurality of different blocks: repeating the steps S1-S4 for each block, selecting development units which are preferentially developed and development units which are secondarily preferentially developed by using the clustering method of S5 a), merging the development units to obtain a comprehensive decision coefficient E, and suggesting preferential development for the blocks with larger E, wherein the development benefits and development potential are better;
S5 c) decision on block development situation of rolling development: along with the continuous change and update of engineering field conditions and exploration development progress, constants and optimized parameters used in the step S2 are continuously updated along with the increase of development time t, after the constants and the optimized parameters are updated, the steps S2-S5 are repeated, the optimal solution set position of a newly developed layer system is updated in a decision diagram, meanwhile, the comprehensive decision coefficient is updated, if the whole data point is farther and farther from an original point along with the continuous development, and the comprehensive decision coefficient is larger and larger, so that the exploration development process is gradually better, and the whole scheme is more optimized; otherwise, as development is continuously carried out, the whole data points are closer to the original points, and the comprehensive decision coefficients are smaller, so that the exploration and development process is gradually worsened, the whole scheme is more uneconomical, and the development scheme needs to be readjusted;
S5 d) for the condition of economic situation change, developing the good and bad decision of situation: modifying a prediction model of the international oil price P in the future year, and when setting the function of the international oil price P as a known interval [ P1, pn ] and a probability function F (P) appearing in the interval, using a probability statistical algorithm to calculate the maximum likelihood estimated value of the parameter P in the interval The maximum likelihood estimation valueTaking the value of the international oil price P as steps S1 to S4, judging and updating the model of the international oil price P, and then, changing the aggregation degree of the whole data points from the original point and the comprehensive decision coefficient: the farther the whole data point is from the original point and the larger the comprehensive decision coefficient is, the better the three-dimensional well pattern development benefit is under the condition of a new international oil price prediction model; if the whole data point is closer to the original point and the comprehensive decision coefficient is smaller, the three-dimensional well pattern development benefit is poorer under the condition of a new international oil price prediction model.
In the global optimization and decision method, a reasonable well spacing and target parameter calculation model is obtained according to the following steps:
a) Calculating the total cost of a single well;
the total cost of a single well is the sum of the well construction cost and the maintenance cost;
the well construction cost comprises ground engineering, well drilling completion and fracturing cost;
the maintenance cost comprises all costs of normal operation and reconstruction construction of the maintenance well after well construction and before abandonment;
b) Calculating single well control reserves N uni_well_H of unit stratum thickness;
c) Calculating the effective seam height H uni_well of the single well,
D) Determining a reasonable well distance boundary under constraint based on cost back calculation;
e) Judging whether a control reserve quantity judging coefficient beta 1 is more than or equal to 1 and a well spacing feasibility coefficient beta 2 is more than or equal to 1, if not, indicating that the existing fracturing main process cannot offset development cost, and if so, the scheme is feasible;
f) When the scheme is feasible, the well spacing in the single layer system is obtained according to the following formula;
Wherein L well represents well spacing, N uni_well_H represents single well control reserves, H uni_well represents single well effective seam height, R represents planned recovery, S H represents reserve abundance of a single layer system, L H represents designed average horizontal well length, and H layer represents small layer thickness;
the average recovery per well was calculated according to the following:
The average internal yield IRR for a single well is calculated according to:
Wherein D 0 is the single well comprehensive reduction rate, Q 0 is the average single well planned first year yield, and T is the estimated production period; p (t) is the average oil price in the t year, C uni_well_d is the cost of single well construction, and C uni_well_m is the cost of single well maintenance; the IRR calculation method comprises the following steps: taking IRR as a variable and other parameters as constants, solving a unitary T-th order equation to obtain T solutions (including a real solution and an imaginary solution) of the equation; selecting the minimum value of all real solutions meeting the value range condition 0-IRR-1 from all solutions as the value of the final average internal yield IRR of a single well; the expression of sgn (x) indicates that the variable x is subjected to taking an absolute value after the symbol function operation; [ x ] represents a rounding operation on x;
g) Establishing boundary conditions and constraint conditions of the multi-objective optimization model and solving, wherein the constants comprise H layer,SH,P(t),D0 and constants used for calculating C uni_well_d,Cuni_well_m Nuni_well_H,Huni_well; the optimized parameters comprise R, L H,Q0 and T, and the optimized parameters used in the calculation of C uni_well_d,Cuni_well_m Nuni_well_H,Huni_well, and each optimized parameter is taken as an independent variable; determining constant values and reasonable value ranges of optimized parameters based on geophysical prospecting data, engineering practice data and well logging experience, taking the upper limit and the lower limit of the reasonable value ranges of the optimized parameters as boundary conditions, and taking the limiting conditions of the step e) as independent variable constraint conditions or constraint equations; taking the expression of L well,IRR,R1 in the step f) as an objective function, solving a minimum problem (some preferred solving algorithms are NSGA2, NSGA3, chebyshev algorithm, MOEAD algorithm and the like) of multi-objective optimization aiming at an optimization objective parameter L well,1/IRR,1/R1, and obtaining a pareto optimal solution set formed by the optimization objective parameter L well,1/IRR,1/R1;
in the global optimization and decision method, the well construction cost is obtained according to the following formula:
Cuni_well_d＝α1(CvLv+CHLH+CHfLHf)
Wherein C v represents the cost of drilling and completing a straight well per kilometer in the longitudinal direction, C H represents the cost of drilling and completing a horizontal well per kilometer, and C Hf represents the total cost of construction per kilometer of a fracturing section; l v,LH,LHf is the length of the designed average vertical well, the length of the designed average horizontal well and the accumulated length of the fracturing reconstruction section of the single horizontal well along the well path direction, wherein L H＞LHf;α1 is the coefficient generated by reducing the cost caused by intensive operation, and is related to the number of wells and the process improvement of the three-dimensional development well pattern;
The maintenance cost is obtained according to the following formula:
Cuni_well_m＝α2Cuni_well_d
where α 2 represents a maintenance cost factor, the value of which is empirically derived from adjacent wells or adjacent platforms or historical wells.
In the global optimization and decision method, the single well control reserves N uni_well_H are obtained according to the following formula:
Nuni_well_H＝2n1α3LfLLHfSH
Where α 3 denotes a drain radius influence coefficient, defined as α 3＝RfL/LfL, where R fL denotes a fracture average half-seam length, L fL denotes a fracture average effective support half-seam length, n 1 denotes an effective remodel coefficient, defined as a ratio of an effective remodel length to a remodel length along a horizontal segment length, S H denotes a reserve abundance of a single layer, and L Hf denotes a remodel length of a single horizontal well along a well path.
In the global optimization and decision method, the effective seam height H uni_well of the single well is obtained according to the following formula:
Huni_well＝min(α4HfH,Hlayer)
Wherein α 4 represents an effective seam height influence coefficient, which is defined as a ratio of seam height to effective support seam height, H fH represents effective support seam height, H layer represents small layer thickness, and operator min (a, B) represents a minimum value of variables a and B.
In the well spacing global decision method, a reasonable well spacing boundary under constraint is determined according to the following steps:
1) Obtaining single well control reserves N uni_well1 under the bottom line cost according to the following steps;
wherein R represents the planned recovery ratio, and P represents the cost price of the bottom line of crude oil;
2) Obtaining a control reserve quantity discrimination coefficient beta 1 according to the following formula;
Where N uni_well_H represents single well control reserves, N uni_well1 represents single well control reserves at the base line cost;
3) Obtaining a limit well distance L d_well under the cost back calculation condition according to the following formula;
Wherein n 1 represents an effective remodel coefficient defined as a ratio of an effective accumulated length of the remodel section to an accumulated length of the remodel section in a length direction along the horizontal section, S H represents a reserve abundance of the single layer system;
4) Obtaining a limit oil drainage radius L d_well1 according to the following formula;
Ld_well1＝2α3LfL
Wherein, alpha 3 represents the influence coefficient of the oil drainage radius, which is defined as alpha 3＝RfL/LfL, wherein R fL represents the average half-seam length of the crack, and L fL represents the average effective supporting half-seam length of the crack;
5) Obtaining a well spacing feasibility coefficient beta 2 according to the following formula;
β2＝Ld_well1/Ld_well。
In the global optimization and decision method described above, the constants include: alpha 3,α4,n1,HfH,SH,Hlayer, the value and the change relation of the value along with time t are determined by a pre-geological model, logging data and field experience values; the value of R and P is determined by the expected value of the development scheme; alpha 1 and alpha 2 are stratum and development constants, and the value and the change relation of the value along with time t are obtained by experience of an adjacent well or an adjacent platform or a historical well;
the optimized parameters include: l Hf,LfL,Cv,CH,CHf,Lv,LH,LHf, the value range of which is obtained by the maximum and minimum values of the field engineering geophysical prospecting data, the engineering practice data and the variation range of the clinical experience parameters.
The global optimization method for the three-dimensional development well pattern provided by the invention is essentially aimed at the multi-objective optimization problem under the condition of multi-parameter influence. The conventional multi-objective optimization is to find multi-objective optimization extreme points (optimal solutions) of a single model, and find pareto fronts between the ranges of several extreme points (optimal solutions), wherein the solutions on the pareto fronts are the optimal solution sets (systems) of the model. However, the conventional pareto front solving method is not fully suitable for the problem of the invention, and needs to be developed and improved based on the actual conditions of the three-dimensional development of the oil reservoir, specifically, firstly, the three-dimensional development design and optimization of the oil reservoir is an overall global process, and a plurality of development layers are longitudinally divided, each development layer has various variable parameters, and some of the variable parameters are local variables affecting a single development unit, such as horizontal section length, fracturing transformation section length, reserve abundance, small layer thickness and the like, and some of the variable parameters are global variables affecting all development layers, such as crude oil bottom line cost price, straight well drilling completion cost, horizontal well drilling completion cost and the like. When parameters are changed, the influence ranges of local variables and global variables are different, and the conditions that some development layers are better and other development layers are worse may exist.
In addition, in the iterative loop optimization process, in order to enable engineering personnel to intuitively detect the effect after each optimization, a decision diagram is established, the position of the optimal solution of each development unit in the decision diagram is drawn, the position and the optimization potential of each development unit in the whole are conveniently and intuitively observed, parameters are further adjusted, and the overall decision of well spacing of a plurality of types of three-dimensional development well patterns is conveniently realized:
for example, a global decision of priority is developed for a development unit: based on the points of all the development units in the decision diagram in the step S3, performing spatial clustering calculation, dividing the development units into 3 classes, and solving the comprehensive coefficient E of all the development units in each class, wherein the development unit with the largest E value is used as the development unit for priority development, and the development is performed in priority; a development unit of a type with a medium E value, which is used for development with a second priority, and selectively develops; the type with the minimum E value is used as a low-benefit development unit to suspend development;
decision to develop priorities for multiple different blocks: repeating the steps S1-S4 for each block, selecting development units which are preferentially developed and development units which are secondarily preferentially developed by using the clustering method of S5 a), merging the development units to obtain a comprehensive decision coefficient E, and suggesting preferential development for the blocks with larger E, wherein the development benefits and development potential are better;
Decision on how good or bad a block development situation is for rolling development: along with the continuous change and update of engineering field conditions and exploration and development progress, constants and optimized parameters used in the step S2 are continuously updated, after the constants and the optimized parameters are updated, the steps S2-S5 are repeated, the optimal solution set position of a newly developed layer system is updated in a decision diagram, meanwhile, the comprehensive decision coefficient is updated, if the whole data point is farther from an original point along with the continuous development, and the comprehensive decision coefficient is larger, the exploration and development process is gradually better, and the scheme is more optimized as a whole; otherwise, as development is continuously carried out, the whole data points are closer to the original points, and the comprehensive decision coefficients are smaller, so that the exploration and development process is gradually worsened, the whole scheme is more uneconomical, and the development scheme needs to be readjusted;
For the condition of economic situation change, the good and bad decision of the development situation is: modifying a prediction model of the international oil price P in the future year, and when setting the function of the international oil price P as a known interval [ P1, pn ] and a probability function F (P) appearing in the interval, using a probability statistical algorithm to calculate the maximum likelihood estimated value of the parameter P in the interval -Estimating the maximum likelihood estimate/>Taking the value of the international oil price P as steps S1 to S4, judging and updating the model of the international oil price P, and then, changing the aggregation degree of the whole data points from the original point and the comprehensive decision coefficient: the farther the whole data point is from the original point and the larger the comprehensive decision coefficient is, the better the three-dimensional well pattern development benefit is under the condition of a new international oil price prediction model; if the whole data point is closer to the original point and the comprehensive decision coefficient is smaller, the three-dimensional well pattern development benefit is poorer under the condition of a new international oil price prediction model.
The decision diagram in the invention selects 3 target parameters of N uni_well_H,IRR,R1. Similarly, it is possible to combine other target parameters (e.g., single well control reserves, recovery, profitability, internal profitability, net present, etc.) with each other, and even to select one, two, or more of the most engineering-focused decision/optimization target parameters to construct a decision set. However, in the process of constructing the decision diagram, when the decision targets are only single, the decision diagram is a line, two decision targets are a plane, three decision targets are a space, more than three decision targets can be selected from a two-to-two relation or a three-to-three relation, and the decision targets are described by a plurality of rectangular coordinates on the plane or are described by a plurality of space coordinates on the space. Although more than three parameters are difficult to visually display in space, the comprehensive decision coefficient provided by the invention is not limited by the dimension of the target parameter, and any multiple target parameters can be used for obtaining the comprehensive decision coefficient to judge the merits of the scheme.
Under the influence of global variables, input variables need to be updated continuously in the decision process. ① When the global variable is updated, the optimal solution set of each development unit may be changed, so that the position of the projection of the development unit on the decision chart and the comprehensive decision coefficient E are changed. ② When the decision is needed to consider the results caused by more development units, the optimal well spacing and the corresponding target parameters of the added development units are only needed to be calculated, the calculation process is updated, and the projection of the added development units is added into the total decision diagram, so that the situation and the optimizing potential of the newly added development units in the whole can be reflected in real time, and whether development is necessary or not. ③ In addition, when the parameter of a certain development unit is independently changed (for example, the reserve abundance is corrected after geophysical prospecting), the in-layer optimal value of the small layer needs to be updated first, whether the variable affects the global variables of other development units or not is judged, if the variable affects the global variables of other development units, the global variables are returned to ① for updating, if the variable affects the global variables, the position of the unit on a decision graph and the comprehensive decision coefficient are directly corrected, and finally, the updated decision graph and the comprehensive decision coefficient can still be obtained in real time for reference of a decision maker.
Aiming at the characteristics of three-dimensional well pattern development, the invention can model each development unit, calculate the optimal solution set based on the optimization method, establish a decision diagram based on given decision/optimization target parameters, realize visual evaluation of the position and optimization potential of each development unit in the whole, further give out comprehensive decision coefficients and give out quantitative parameters for whether the scheme is optimized. Further, on the level of the development units of all three-dimensional well patterns, the evaluation of the optimal scheme of each development layer, the decision of the global optimal scheme and the decision diagram and the comprehensive decision coefficients are realized under the given parameter combination, and when various parameters are updated, the decision diagram and the comprehensive decision coefficients can be recalculated by monitoring the updating of the input parameters, and meanwhile, a decision conclusion is provided for whether the updating attempt is more favorable or not by visual perception of whether each point in the decision diagram is farther from the original point and whether the comprehensive decision coefficients are increased or not. The method is simple, the principle is easy to understand, and the operability is strong. The invention is an effective global decision method for three-dimensional well pattern development, which can provide support for the global optimization of the three-dimensional well pattern development and provide positive guidance for the whole development design of the three-dimensional well pattern and the correction of the follow-up scheme.
Detailed Description
The experimental methods used in the following examples are conventional methods unless otherwise specified.
Materials, reagents and the like used in the examples described below are commercially available unless otherwise specified.
The global optimization and decision-making method for the three-dimensional development well pattern provided by the invention comprises the following steps:
s1, dividing development units in the longitudinal direction according to basic physical properties of a target reservoir, wherein the development units are overlapped and seepage fields are mutually independent;
S2, constructing a reasonable well spacing and target parameter calculation model, calculating a pareto optimal solution set of each development unit by using the reasonable well spacing and target parameter calculation model, selecting a solution with the minimum well spacing L well from the pareto optimal solution set as an optimal solution, taking a value of L well,IRR,R1 corresponding to the optimal solution as an optimal target value, taking the optimal target value and a value of an optimized parameter corresponding to the optimal target value as an optimization result of the development unit, and selecting a value of N uni_well_H,IRR,R1 from the optimization result to form an optimal solution set (N uni_well_H,IRR,R1);
S3, establishing a decision graph; specifically: taking N uni_well_H,IRR,R1 as the x coordinate, the y coordinate and the z coordinate of the three-dimensional rectangular coordinate system, taking the optimal solution set (N uni_well_H,IRR,R1) of each development unit as a point, and drawing in a decision diagram;
S4, establishing a comprehensive decision coefficient: specifically, a weighting method is used for carrying out weighted summation on N uni_well_H,IRR,R1 to obtain a comprehensive decision coefficient E:
Wherein w 1、w2、w3 is a weighting coefficient, the value of a specific development block is given by an expert scoring method, N uni_well_H(q),IRR(q),R1 (q) respectively represents single-well control reserves N uni_well_H in the optimal solution set of the q-th development unit in the j development units in the longitudinal direction, the average internal yield IRR of the single well and the average recovery ratio R 1 of the single well, and the larger the comprehensive decision coefficient E is, the better the reservoir development effect is;
S5, performing well spacing global decision of the three-dimensional development well pattern by using the decision diagram and the comprehensive decision coefficient:
S5 a) developing a global decision of priority for the development unit: based on the points of all the development units in the decision diagram in the step S3, performing spatial clustering calculation, dividing the development units into 3 classes, and solving the comprehensive coefficient E of all the development units in each class, wherein the development unit with the largest E value is used as the development unit for priority development, and the development is performed in priority; a development unit of a type with a medium E value, which is used for development with a second priority, and selectively develops; the type with the minimum E value is used as a low-benefit development unit to suspend development;
S5 b) decision to develop priorities for a plurality of different blocks: repeating the steps S1-S4 for each block, selecting development units which are preferentially developed and development units which are secondarily preferentially developed by using the clustering method of S5 a), merging the development units to obtain a comprehensive decision coefficient E, and suggesting preferential development for the blocks with larger E, wherein the development benefits and development potential are better;
S5 c) decision on block development situation of rolling development: along with the continuous change and update of engineering field conditions and exploration and development progress, constants and optimized parameters used in the step S2 are continuously updated, after the constants and the optimized parameters are updated, the steps S2-S5 are repeated, the optimal solution set position of a newly developed layer system is updated in a decision diagram, meanwhile, the comprehensive decision coefficient is updated, if the whole data point is farther from an original point along with the continuous development, and the comprehensive decision coefficient is larger, the exploration and development process is gradually better, and the scheme is more optimized as a whole; otherwise, as development is continuously carried out, the whole data points are closer to the original points, and the comprehensive decision coefficients are smaller, so that the exploration and development process is gradually worsened, the whole scheme is more uneconomical, and the development scheme needs to be readjusted;
S5 d) for the condition of economic situation change, developing the good and bad decision of situation: modifying a prediction model of the international oil price P in the future year, and when setting the function of the international oil price P as a known interval [ P1, pn ] and a probability function F (P) appearing in the interval, using a probability statistical algorithm to calculate the maximum likelihood estimated value of the parameter P in the interval The maximum likelihood estimation valueTaking the value of the international oil price P as steps S1 to S4, judging and updating the model of the international oil price P, and then, changing the aggregation degree of the whole data points from the original point and the comprehensive decision coefficient: the farther the whole data point is from the original point and the larger the comprehensive decision coefficient is, the better the three-dimensional well pattern development benefit is under the condition of a new international oil price prediction model; if the whole data point is closer to the original point and the comprehensive decision coefficient is smaller, the three-dimensional well pattern development benefit is poorer under the condition of a new international oil price prediction model.
Preferably, a reasonable well spacing and target parameter calculation model is obtained according to the following steps:
a) Calculating the total cost of a single well;
the total cost of a single well is the sum of the well construction cost and the maintenance cost;
the well construction cost comprises ground engineering, well drilling completion and fracturing cost;
the maintenance cost comprises all costs of normal operation and reconstruction construction of the maintenance well after well construction and before abandonment;
b) Calculating single well control reserves N uni_well_H of unit stratum thickness;
c) Calculating the effective seam height H uni_well of the single well,
D) Determining a reasonable well distance boundary under constraint based on cost back calculation;
e) Judging whether a control reserve quantity judging coefficient beta 1 is more than or equal to 1 and a well spacing feasibility coefficient beta 2 is more than or equal to 1, if not, indicating that the existing fracturing main process cannot offset development cost, and if so, the scheme is feasible;
f) When the scheme is feasible, the well spacing in the single layer system is obtained according to the following formula;
Wherein L well represents well spacing, N uni_well_H represents single well control reserves, H uni_well represents single well effective seam height, R represents planned recovery, S H represents reserve abundance of a single layer system, L H represents designed average horizontal well length, and H layer represents small layer thickness;
the average recovery per well was calculated according to the following:
The average internal yield IRR for a single well is calculated according to:
Wherein D 0 is the single well comprehensive reduction rate, Q 0 is the average single well planned first year yield, and T is the estimated production period; p (t) is the average oil price in the t year, C uni_well_d is the cost of single well construction, and C uni_well_m is the cost of single well maintenance; the IRR calculation method comprises the following steps: taking IRR as a variable and other parameters as constants, solving a unitary T-th order equation to obtain T solutions (including a real solution and an imaginary solution) of the equation; selecting the minimum value of all real solutions meeting the value range condition 0-IRR-1 from all solutions as the value of the final average internal yield IRR of a single well; the expression of sgn (x) indicates that the variable x is subjected to taking an absolute value after the symbol function operation; [ x ] represents a rounding operation on x;
g) Establishing boundary conditions and constraint conditions of the multi-objective optimization model and solving, wherein the constants comprise H layer,SH,P(t),D0 and constants used for calculating C uni_well_d,Cuni_well_m Nuni_well_H,Huni_well; the optimized parameters comprise R, L H,Q0 and T, and the optimized parameters used in the calculation of C uni_well_d,Cuni_well_m Nuni_well_H,Huni_well, and each optimized parameter is taken as an independent variable; determining constant values and reasonable value ranges of optimized parameters based on geophysical prospecting data, engineering practice data and well logging experience, taking the upper limit and the lower limit of the reasonable value ranges of the optimized parameters as boundary conditions, and taking the limiting conditions of the step e) as independent variable constraint conditions or constraint equations; taking the expression of L well,IRR,R1 in the step f) as an objective function, solving a minimum problem (some preferred solving algorithms are NSGA2, NSGA3, chebyshev algorithm, MOEAD algorithm and the like) of multi-objective optimization aiming at an optimization objective parameter L well,1/IRR,1/R1, and obtaining a pareto optimal solution set formed by the optimization objective parameter L well,1/IRR,1/R1;
Preferably, the well construction costs are obtained according to the following formula:
Cuni_well_d＝α1(CvLv+CHLH+CHfLHf)
Wherein C v represents the cost of drilling and completing a straight well per kilometer in the longitudinal direction, C H represents the cost of drilling and completing a horizontal well per kilometer, and C Hf represents the total cost of construction per kilometer of a fracturing section; l v,LH,LHf is the length of the designed average vertical well, the length of the designed average horizontal well and the accumulated length of the fracturing reconstruction section of the single horizontal well along the well path direction, wherein L H＞LHf;α1 is the coefficient generated by reducing the cost caused by intensive operation, and is related to the number of wells and the process improvement of the three-dimensional development well pattern;
The maintenance cost is obtained according to the following formula:
Cuni_well_m＝α2Cuni_well_d
where α 2 represents a maintenance cost factor, the value of which is empirically derived from adjacent wells or adjacent platforms or historical wells.
Preferably, the single well control reserves N uni_well_H are obtained as follows:
Nuni_well_H＝2n1α3LfLLHfSH
Where α 3 denotes a drain radius influence coefficient, defined as α 3＝RfL/LfL, where R fL denotes a fracture average half-seam length, L fL denotes a fracture average effective support half-seam length, n 1 denotes an effective remodel coefficient, defined as a ratio of an effective remodel length to a remodel length along a horizontal segment length, S H denotes a reserve abundance of a single layer, and L Hf denotes a remodel length of a single horizontal well along a well path.
Preferably, the single well effective slot height H uni_well is obtained according to the following formula:
Huni_well＝min(α4HfH,Hlayer)
Wherein α 4 represents an effective seam height influence coefficient, which is defined as a ratio of seam height to effective support seam height, H fH represents effective support seam height, H layer represents small layer thickness, and operator min (a, B) represents a minimum value of variables a and B.
Preferably, the reasonable well spacing boundaries under constraint are determined as follows:
1) Obtaining single well control reserves N uni_well1 under the bottom line cost according to the following steps;
wherein R represents the planned recovery ratio, and P represents the cost price of the bottom line of crude oil;
2) Obtaining a control reserve quantity discrimination coefficient beta 1 according to the following formula;
Where N uni_well_H represents single well control reserves, N uni_well1 represents single well control reserves at the base line cost;
3) Obtaining a limit well distance L d_well under the cost back calculation condition according to the following formula;
Wherein n 1 represents an effective remodel coefficient defined as a ratio of an effective accumulated length of the remodel section to an accumulated length of the remodel section in a length direction along the horizontal section, S H represents a reserve abundance of the single layer system;
4) Obtaining a limit oil drainage radius L d_well1 according to the following formula;
Ld_well1＝2α3LfL
Wherein, alpha 3 represents the influence coefficient of the oil drainage radius, which is defined as alpha 3＝RfL/LfL, wherein R fL represents the average half-seam length of the crack, and L fL represents the average effective supporting half-seam length of the crack;
5) Obtaining a well spacing feasibility coefficient beta 2 according to the following formula;
β2＝Ld_well1/Ld_well。
Preferably, the constant comprises: alpha 3,α4,n1,HfH,SH,Hlayer, the value of which is determined by a pre-geological model, logging data and field experience values; the value of R and P is determined by the expected value of the development scheme; alpha 1 and alpha 2 are stratum and development constants, and values of the stratum and the development constants are obtained through experience of adjacent wells or adjacent platforms or historical wells;
the optimized parameters include: l Hf,LfL,Cv,CH,CHf,Lv,LH,LHf, the value range of which is obtained by the maximum and minimum values of the field engineering geophysical prospecting data, the engineering practice data and the variation range of the clinical experience parameters.
The decision diagram in the invention selects 3 target parameters of N uni_well_H,IRR,R1. Similarly, it is possible to combine other target parameters (e.g., single well control reserves, recovery, profitability, internal profitability, net present, etc.) with each other, and even to select one, two, or more of the most engineering-focused decision/optimization target parameters to construct a decision set. However, in the process of constructing the decision diagram, when the decision targets are only single, the decision diagram is a line, two decision targets are a plane, three decision targets are a space, more than three decision targets can be selected from a two-to-two relation or a three-to-three relation, and the decision targets are described by a plurality of rectangular coordinates on the plane or are described by a plurality of space coordinates on the space. Although more than three parameters are difficult to visually display in space, the comprehensive decision coefficient provided by the invention is not limited by the dimension of the target parameter, and any multiple target parameters can be used for obtaining the comprehensive decision coefficient to judge the merits of the scheme.
Claims (7)
1. A global optimization and decision method for a three-dimensional development well pattern is characterized in that: the method comprises the following steps:
s1, dividing development units in the longitudinal direction according to basic physical properties of a target reservoir, wherein the development units are overlapped and seepage fields are mutually independent;
S2, constructing a reasonable well spacing and target parameter calculation model, calculating a pareto optimal solution set of each development unit by using the reasonable well spacing and target parameter calculation model, selecting a solution with the minimum well spacing L well from the pareto optimal solution set as an optimal solution, taking a value of L well,IRR,R1 corresponding to the optimal solution as an optimal target value, taking the optimal target value and a value of an optimized parameter corresponding to the optimal target value as an optimization result of the development unit, and selecting a value of N uni_well_H,IRR,R1 from the optimization result to form an optimal solution set (N uni_well_H,IRR,R1);
S3, establishing a decision graph; specifically: taking N uni_well_H,IRR,R1 as the x coordinate, the y coordinate and the z coordinate of the three-dimensional rectangular coordinate system, taking the optimal solution set (N uni_well_H,IRR,R1) of each development unit as a point, and drawing in a decision diagram;
S4, establishing a comprehensive decision coefficient: specifically, a weighting method is used for carrying out weighted summation on N uni_well_H,IRR,R1 to obtain a comprehensive decision coefficient E:
Wherein w 1、w2、w3 is a weighting coefficient, the value of a specific development block is given by an expert scoring method, N uni_well_H(q),IRR(q),R1 (q) respectively represents single-well control reserves N uni_well_H in the optimal solution set of the q-th development unit in the j development units in the longitudinal direction, the average internal yield IRR of the single well and the average recovery ratio R 1 of the single well, and the larger the comprehensive decision coefficient E is, the better the reservoir development effect is;
S5, performing well spacing global decision of the three-dimensional development well pattern by using the decision diagram and the comprehensive decision coefficient:
S5 a) developing a global decision of priority for the development unit: based on the points of all the development units in the decision diagram in the step S3, performing spatial clustering calculation, dividing the development units into 3 classes, and solving the comprehensive coefficient E of all the development units in each class, wherein the development unit with the largest E value is used as the development unit for priority development, and the development is performed in priority; a development unit of a type with a medium E value, which is used for development with a second priority, and selectively develops; the type with the minimum E value is used as a low-benefit development unit to suspend development;
S5 b) decision to develop priorities for a plurality of different blocks: repeating the steps S1-S4 for each block, selecting development units which are preferentially developed and development units which are secondarily preferentially developed by using the clustering method of S5 a), merging the development units to obtain a comprehensive decision coefficient E, and suggesting preferential development for the blocks with larger E, wherein the development benefits and development potential are better;
S5 c) decision on block development situation of rolling development: along with the continuous change and update of engineering field conditions and exploration development progress, constants and optimized parameters used in the step S2 are continuously updated along with the increase of development time t, after the constants and the optimized parameters are updated, the steps S2-S5 are repeated, the optimal solution set position of a newly developed layer system is updated in a decision diagram, meanwhile, the comprehensive decision coefficient is updated, if the whole data point is farther and farther from an original point along with the continuous development, and the comprehensive decision coefficient is larger and larger, so that the exploration development process is gradually better, and the whole scheme is more optimized; otherwise, as development is continuously carried out, the whole data points are closer to the original points, and the comprehensive decision coefficients are smaller, so that the exploration and development process is gradually worsened, the whole scheme is more uneconomical, and the development scheme needs to be readjusted;
S5 d) for the condition of economic situation change, developing the good and bad decision of situation: modifying a prediction model of the international oil price P in the future year, and when setting the function of the international oil price P as a known interval [ P1, pn ] and a probability function F (P) appearing in the interval, using a probability statistical algorithm to calculate the maximum likelihood estimated value of the parameter P in the interval -Estimating the maximum likelihood estimate/>Taking the value of the international oil price P as steps S1 to S4, judging and updating the model of the international oil price P, and then, changing the aggregation degree of the whole data points from the original point and the comprehensive decision coefficient: the farther the whole data point is from the original point and the larger the comprehensive decision coefficient is, the better the three-dimensional well pattern development benefit is under the condition of a new international oil price prediction model; if the whole data point is closer to the original point and the comprehensive decision coefficient is smaller, the three-dimensional well pattern development benefit is poorer under the condition of a new international oil price prediction model.
2. The global optimization and decision-making method according to claim 1, characterized in that: obtaining a reasonable well spacing and target parameter calculation model according to the following steps:
a) Calculating the total cost of a single well;
the total cost of a single well is the sum of the well construction cost and the maintenance cost;
the well construction cost comprises ground engineering, well drilling completion and fracturing cost;
the maintenance cost comprises all costs of normal operation and reconstruction construction of the maintenance well after well construction and before abandonment;
b) Calculating single well control reserves N uni_well_H of unit stratum thickness;
c) Calculating the effective seam height H uni_well of the single well,
D) Determining a reasonable well distance boundary under constraint based on cost back calculation;
e) Judging whether a control reserve quantity judging coefficient beta 1 is more than or equal to 1 and a well spacing feasibility coefficient beta 2 is more than or equal to 1, if not, indicating that the existing fracturing main process cannot offset development cost, and if so, the scheme is feasible;
f) When the scheme is feasible, the well spacing in the single layer system is obtained according to the following formula;
Wherein L well represents well spacing, N uni_well_H represents single well control reserves, H uni_well represents single well effective seam height, R represents planned recovery, S H represents reserve abundance of a single layer system, L H represents designed average horizontal well length, and H layer represents small layer thickness;
the average recovery per well was calculated according to the following:
The average internal yield IRR for a single well is calculated according to:
Wherein D 0 is the single well comprehensive reduction rate, Q 0 is the average single well planned first year yield, and T is the estimated production period; p (t) is the average oil price in the t year, C uni_well_d is the cost of single well construction, and C uni_well_m is the cost of single well maintenance; the IRR calculation method comprises the following steps: taking IRR as a variable and other parameters as constants, solving a unitary T-th equation to obtain T solutions of the equation; selecting the minimum value of all real solutions meeting the value range condition 0-IRR-1 from all solutions as the value of the final average internal yield IRR of a single well; the expression of sgn (x) indicates that the variable x is subjected to taking an absolute value after the symbol function operation; [ x ] represents a rounding operation on x;
g) Establishing boundary conditions and constraint conditions of the multi-objective optimization model and solving, wherein the constants comprise H layer,SH,P(t),D0 and constants used for calculating C uni_well_d,Cuni_well_m Nuni_well_H,Huni_well; the optimized parameters comprise R, L H,Q0 and T, and the optimized parameters used in the calculation of C uni_well_d,Cuni_well_m Nuni_well_H,Huni_well, and each optimized parameter is taken as an independent variable; determining constant values and reasonable value ranges of optimized parameters based on geophysical prospecting data, engineering practice data and well logging experience, taking the upper limit and the lower limit of the reasonable value ranges of the optimized parameters as boundary conditions, and taking the limiting conditions of the step e) as independent variable constraint conditions or constraint equations; taking the expression of L well,IRR,R1 in the step f) as an objective function, solving a minimum value problem of multi-objective optimization aiming at an optimization objective parameter L well,1/IRR,1/R1, and obtaining a pareto optimal solution set formed by the optimization objective parameter L well,1/IRR,1/R1;
3. The global optimization and decision-making method according to claim 2, characterized in that: the well construction cost is obtained according to the following formula:
Cuni_well_d＝α1(CvLv+CHLH+CHfLHf)
Wherein C v represents the cost of drilling and completing a straight well per kilometer in the longitudinal direction, C H represents the cost of drilling and completing a horizontal well per kilometer, and C Hf represents the total cost of construction per kilometer of a fracturing section; l v,LH,LHf is the length of the designed average vertical well, the length of the designed average horizontal well and the accumulated length of the fracturing reconstruction section of the single horizontal well along the well path direction, wherein L H＞LHf;α1 is the coefficient generated by reducing the cost caused by intensive operation, and is related to the number of wells and the process improvement of the three-dimensional development well pattern;
The maintenance cost is obtained according to the following formula:
Cuni_well_m＝α2Cuni_well_d
where α 2 represents a maintenance cost factor, the value of which is empirically derived from adjacent wells or adjacent platforms or historical wells.
4. A global optimization and decision-making method in accordance with claim 3, characterized by: the single well control reserves N uni_well_H were obtained as follows:
Nuni_well_H＝2n1α3LfLLHfSH
Where α 3 denotes a drain radius influence coefficient, defined as α 3＝RfL/LfL, where R fL denotes a fracture average half-seam length, L fL denotes a fracture average effective support half-seam length, n 1 denotes an effective remodel coefficient, defined as a ratio of an effective remodel length to a remodel length along a horizontal segment length, S H denotes a reserve abundance of a single layer, and L Hf denotes a remodel length of a single horizontal well along a well path.
5. The global optimization and decision-making method according to claim 4, wherein: the effective seam height H uni_well of the single well is obtained according to the following steps:
Huni_well＝min(α4HfH,Hlayer)
Wherein α 4 represents an effective seam height influence coefficient, which is defined as a ratio of seam height to effective support seam height, H fH represents effective support seam height, H layer represents small layer thickness, and operator min (a, B) represents a minimum value of variables a and B.
6. The global optimization and decision-making method according to claim 5, wherein: determining a reasonable well spacing boundary under constraint according to the following steps:
1) Obtaining single well control reserves N uni_well1 under the bottom line cost according to the following steps;
wherein R represents the planned recovery ratio, and P represents the cost price of the bottom line of crude oil;
2) Obtaining a control reserve quantity discrimination coefficient beta 1 according to the following formula;
Where N uni_well_H represents single well control reserves, N uni_well1 represents single well control reserves at the base line cost;
3) Obtaining a limit well distance L d_well under the cost back calculation condition according to the following formula;
Wherein n 1 represents an effective remodel coefficient defined as a ratio of an effective accumulated length of the remodel section to an accumulated length of the remodel section in a length direction along the horizontal section, S H represents a reserve abundance of the single layer system;
4) Obtaining a limit oil drainage radius L d_well1 according to the following formula;
Ld_well1＝2α3LfL
Wherein, alpha 3 represents the influence coefficient of the oil drainage radius, which is defined as alpha 3＝RfL/LfL, wherein R fL represents the average half-seam length of the crack, and L fL represents the average effective supporting half-seam length of the crack;
5) Obtaining a well spacing feasibility coefficient beta 2 according to the following formula;
β2＝Ld_well1/Ld_well。
7. The global optimization and decision-making method according to claim 6, wherein: the constants include: alpha 3,α4,n1,HfH,SH,Hlayer, the value and the change relation of the value along with time t are determined by a pre-geological model, logging data and field experience values; the value of R and P is determined by the expected value of the development scheme; alpha 1 and alpha 2 are stratum and development constants, and the value and the change relation of the value along with time t are obtained by experience of an adjacent well or an adjacent platform or a historical well;
the optimized parameters include: l Hf,LfL,Cv,CH,CHf,Lv,LH,LHf, the value range of which is obtained by the maximum and minimum values of the field engineering geophysical prospecting data, the engineering practice data and the variation range of the clinical experience parameters.
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