CN115467650B - Multi-element collaborative optimization method and system for oil reservoir three-dimensional well pattern development parameters - Google Patents
Multi-element collaborative optimization method and system for oil reservoir three-dimensional well pattern development parameters Download PDFInfo
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Abstract
The invention discloses a multi-element collaborative optimization method and a system for oil reservoir three-dimensional well pattern development parameters, wherein the method comprises the following steps: constructing a full-period three-dimensional development and evaluation model, wherein the full-period three-dimensional development and evaluation model takes well pattern development parameters as input parameters and target parameters as output parameters; and solving an optimal solution of the full-period three-dimensional development and evaluation model by using a multi-element collaborative optimization algorithm to obtain optimal well pattern development parameters. According to the method, in the initial stage of three-dimensional development design, on-site oil reservoir three-dimensional development parameters, comprehensive recovery ratio, total incomes in the maximum development period and total profits are combined, under the condition that the difference between the total profits and the maximum total profits of the limit is not large, the comprehensive recovery ratio and the total incomes in the maximum development period are found out to cooperatively optimize the development parameters, advice is provided for three-dimensional well pattern design and construction parameter design, and the economic benefit of oil reservoir development is improved.
Description
Technical Field
The invention relates to the field of oil and gas field development, in particular to a multi-element collaborative optimization method and system for oil reservoir three-dimensional well pattern development parameters.
Background
With the increasing difficulty of oil and gas resource development, horizontal well development technology is gradually developed, and a large-scale volume fracturing is adopted for compact unconventional reservoirs (compact conglomerates and shale oil and gas), so that the oil and gas well has certain economic productivity.
In recent years, the exploration and development of dense conglomerate oil reservoirs represented by Xinjiang MAlake obtain great breakthrough, and the ultra-large dense conglomerate oil field with the reserve size of 10 hundred million tons is found, so that the method becomes one of important potential points of crude oil yield increase in China. In order to realize the efficient development of the dense conglomerate oil reservoir and improve the resource utilization rate and the recovery ratio, a small well spacing three-dimensional well pattern development mode is adopted for exploitation. The design of the small well spacing three-dimensional well pattern needs to be globally optimized aiming at a series of problems such as geological conditions, well spacing and hydraulic fracture joint length, well distribution mode and inter-well distribution mode, multi-well operation sequences of the well pattern of the same layer, synchronous pressure control flowback after integral fracturing, production and the like, and the association rule is complex. Therefore, the research of a multi-element collaborative optimization method of the three-dimensional development well pattern is carried out by taking the overall benefit as an optimization direction, 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 a dense conglomerate oil reservoir.
The dense conglomerate oil reservoir has strong heterogeneity, belongs to low-pore low-permeability and ultra-low-permeability reservoirs, and is usually developed by adopting a multi-layer spatial three-dimensional well distribution mode, namely three-dimensional well pattern development. The three-dimensional development test of the compact oil in the MAlake in China is started from 2018, horizontal well three-dimensional well patterns with well spacing of 100m and 150m are designed and implemented in hundred sections and hundred three sections of Baikouquan groups, good effects are shown, single well oil production in a test area exceeds 1.5 times of the average of blocks, and small well spacing three-dimensional well pattern development can obtain better interwell utilization and overall recovery ratio and has potential to become one of important development modes of future unconventional oil and gas reservoir development.
However, in the process of using the geological engineering integration concept to develop the small well spacing three-dimensional well pattern, the complex system engineering of global optimization is needed to be realized by multi-element cooperative optimization aiming at specific targets, the complex system engineering comprises the combination of multidisciplinary theory and multi-field technology, the optimization of the technical scheme, the management flow and the whole life cycle of key nodes, and the complex optimization steps are large-scale system engineering.
At present, the optimization method of the well pattern mainly comprises a seepage theory analysis method, an engineering experience method, an indoor experiment method, an oil reservoir numerical simulation method and the like. However, these methods only consider the recovery ratio, and the treatment of economic benefits is still imperfect. Xie Erka Qiao Fu formula and the like establish a relation model aiming at the relation between the recovery ratio and the well pattern, and are widely applied. The model considers economic benefits, but the economic benefits are only regarded as limit constraint conditions, and optimal economic benefits are not sought. Engineering experience methods also consider maximization of economic targets, but multiple targets are not optimized at the same time, and most of the cases of the step-by-step optimization method obtain local optimal values, so that it is difficult to find a globally optimal solution. Therefore, a method and a system for multi-element collaborative optimization of oil reservoir three-dimensional well pattern development parameters are needed to be provided.
Disclosure of Invention
One of the objects of the present invention is: the multi-element collaborative optimization method for the oil reservoir three-dimensional well pattern development parameters is provided.
In order to achieve the above object, the present invention provides the following technical solutions:
a multi-element collaborative optimization method for oil reservoir three-dimensional well pattern development parameters comprises the following steps:
constructing a full-period three-dimensional development and evaluation model, wherein the full-period three-dimensional development and evaluation model takes well pattern development parameters as input parameters and target parameters as output parameters;
and solving an optimal solution for the full-period three-dimensional development and evaluation model by using a multi-element collaborative optimization algorithm to obtain optimal well pattern development parameters.
Preferably, the target parameter includes D 1 ，D 2 And M; the well pattern development parameters include L 1 ，L 2 ，m 1 T, C and r;
wherein D is 1 Is to comprehensively collectYield D 2 For total revenue over development years, M is total profit; l (L) 1 For well spacing, L 2 For average horizontal segment length, m 1 The number of layers of the three-dimensional well pattern development layer is calculated, T is the estimated production age, C is the average total cost of single well drilling, construction and maintenance every year, and r is the discount rate.
Preferably, the full-cycle stereoscopic development and evaluation model comprises a reservoir and engineering sub-model, a management sub-model and a profit calculation sub-model.
Preferably, the reservoir and engineering submodel are constructed with reference to the following parameters: length X, width Y, well spacing L of target development area 1 Average horizontal segment length L 2 Layer number m of three-dimensional well pattern development layer system 1 Estimated production period T, single well comprehensive decline rate D 0 First year predicted production Q of single well per kilometer horizontal segment length 0 The development area predicts geological reserves N and comprehensively recovers D 1 At time t, selling oil price P (t) of each ton of crude oil at the end of the t year;
wherein D is 1 For the output parameters of the model, L 1 ，L 2 ，m 1 T is a variable parameter input by the model, X, Y, N, D 0 ，Q 0 P (t) is a known constant of the model input;
the relation between the variable parameters and the output parameters input by the reservoir and the engineering submodel is as follows:
wherein square brackets are whole symbols, [ a ] represents a maximum integer not exceeding a; min (x 1, x 2) is a minimum symbol, representing the minimum of the parameters x1 and x 2.
Preferably, the management submodel is constructed by the following parameters: length X, width Y, well spacing L of target development zone 1 Average horizontal segment length L 2 Layer number m of three-dimensional well pattern development layer system 1 Estimated production period T, single well comprehensive decline rate D 0 First year predicted production Q of single well per kilometer horizontal segment length 0 Total income D within the development period 2 At time t, selling oil price P (t) of each ton of crude oil at the end of the t year;
wherein D is 2 For the output parameters of the model, L 1 ，L 2 ，m 1 T is a variable parameter input by the model, X, Y and D 0 ，Q 0 P (t) is a known constant of the model input;
the relation between the variable parameters and the output parameters input by the management sub-model is as follows:
wherein square brackets are whole symbols, [ a ] represents a maximum integer not exceeding a; min (x 1, x 2) is a minimum symbol, representing the minimum of the parameters x1 and x 2.
Preferably, the profit calculation sub-model is constructed by the following parameters: length X, width Y, well spacing L of target development zone 1 Average horizontal segment length L 2 Layer number m of three-dimensional well pattern development layer system 1 Estimated production period T, single well comprehensive decline rate D 0 First year predicted production Q of single well per kilometer horizontal segment length 0 Total income D within the development period 2 At time t, selling oil price P (t) of each ton of crude oil at the end of the t year, discount rate r, average total cost C of single well drilling, construction and maintenance each year, and total profit M;
wherein M is the output parameter of the model, L 1 ，L 2 ，m 1 T, C, r are the variable parameters input by the model, X, Y, D 0 ，Q 0 P (t) is a known constant of the model input;
the relation between the variable parameters and the output parameters input by the profit calculation sub-model is as follows:
wherein square brackets are whole symbols, [ a ] represents a maximum integer not exceeding a; min (x 1, x 2) is a minimum symbol, representing the minimum of the parameters x1 and x 2.
Preferably, the optimal solution is obtained for the full-period three-dimensional development and evaluation model by using a multi-element collaborative optimization algorithm, and the specific process for obtaining the optimal well pattern development parameters is as follows:
(1) Constructing a database of input parameters and output parameters of a full-period three-dimensional development and evaluation model by L 1 ，L 2 ，m 1 T, C, r are input parameters, and are input into a full-period three-dimensional development and evaluation model for simulation calculation, so that an output parameter D is obtained 1 ，D 2 Simulation results of M; each group of input parameters are provided with an output parameter corresponding to each other, and the data combination of the input parameters is in one-to-one correspondence with the output parameters to form one row of data in the database;
(2) Build with D 1 For the x-axis, in D 2 Taking M as a coordinate space of a z axis as a y axis, taking each row of data in the database as a point, and putting the point into the coordinate space;
(3) Finding the maximum value of M in all simulation results, and finding the input parameters and the output parameters of the calculation example;
selecting left and right adjacent values of the input parameter as respective definition domains, and interpolating the respective definition domains of each variable to obtain an expanded input parameter;
input parameters into a full-period three-dimensional development and evaluation model to perform simulation calculation to obtain output parameters D 1 ，D 2 Simulation results of M; adding the input parameters and the output parameters into a database, and adding points corresponding to the data record in the coordinate space of the step (2);
(4) Continuing to find the maximum value of M in the simulation result of the database, and then repeating the operation of the step (3) until the point in the space meets the criterion, and ending the iteration;
(5) Confirming the coordinates of an ending point V when iteration is ended;
d at the termination point V 1 And D 2 D of points than the rest 1 And D 2 When all are large, the input of the scheme represented by the point VThe parameters are optimal well pattern development parameters;
when there is at least 1 point to the end point V D 1 Or D 2 When the distance between each point and the origin is large, all points of M meeting the condition of the limiting range in the space are taken, and the distance between each point and the origin is calculatedAnd taking the input parameters of the scheme represented by the point with the maximum R as the optimal well pattern development parameters.
Preferably, the criterion in step (4) is as follows: maximum value M of M in previous interpolation calculation result i M is the maximum value of M in the interpolation calculation result i+1 Percent difference of (2)Stopping calculation when the accuracy value is smaller than the accuracy fixed value; the fixed value of the precision is 0.1, namely when epsilon<Stopping calculation when 0.1 is reached;
the limiting range condition in the step (5) is that M meets the conditions of [ M-eta, M+eta ], and the value of eta is 0.1.
The second object of the present invention is: the multi-element collaborative optimization system for the oil reservoir three-dimensional well pattern development parameters is provided.
In order to achieve the above object, the present invention provides the following technical solutions:
a multi-element collaborative optimization system for reservoir three-dimensional well pattern development parameters, comprising:
the construction unit: the method is used for constructing a full-period three-dimensional development and evaluation model, wherein the full-period three-dimensional development and evaluation model takes well pattern development parameters as input parameters and target parameters as output parameters;
a calculation unit: and the method is used for solving the optimal solution of the full-period three-dimensional development and evaluation model by using a multi-element collaborative optimization algorithm to obtain the optimal well pattern development parameters.
Preferably, the calculating unit calculates an optimal well pattern development parameter, which specifically includes:
(1) Constructing a database of input parameters and output parameters of a full-period three-dimensional development and evaluation model by L 1 ，L 2 ，m 1 T, C, r are input parameters, and are input into a full-period three-dimensional development and evaluation model for simulation calculation, so that an output parameter D is obtained 1 ，D 2 Simulation results of M; each group of input parameters has an output parameter corresponding to each other, and the data combination of the input parameters corresponds to the output parameters one by one to form a row of data in the database, wherein D 1 For comprehensive recovery, D 2 For total revenue over development years, M is total profit; l (L) 1 For well spacing, L 2 For average horizontal segment length, m 1 The number of layers of the three-dimensional well pattern development layer is T is the estimated exploitation years, C is the average total cost of single well drilling, construction and maintenance every year, and r is the discount rate;
(2) Build with D 1 For the x-axis, in D 2 Taking M as a coordinate space of a z axis as a y axis, taking each row of data in the database as a point, and putting the point into the coordinate space;
(3) Finding the maximum value of M in all simulation results, and finding the input parameters and the output parameters of the calculation example;
selecting left and right adjacent values of the input parameter as respective definition domains, and interpolating the respective definition domains of each variable to obtain an expanded input parameter;
input parameters into a full-period three-dimensional development and evaluation model to perform simulation calculation to obtain output parameters D 1 ，D 2 Simulation results of M; adding the input parameters and the output parameters into a database, and adding points corresponding to the data record in the coordinate space of the step (2);
(4) Continuing to find the maximum value of M in the simulation result of the database, and then repeating the operation of the step (3) until the point in the space meets the criterion, and ending the iteration;
(5) Confirming the coordinates of an ending point V when iteration is ended;
d at the termination point V 1 And D 2 D of points than the rest 1 And D 2 When the parameters are all large, taking the input parameters of the scheme represented by the point V as the optimal well pattern development parameters;
when there is at least 1 point to the end point V D 1 Or D 2 When the space is large, taking all points of M meeting the condition of the limited range in the space, and calculating each pointAnd taking the input parameters of the scheme represented by the point with the maximum R as the optimal well pattern development parameters.
The invention has the beneficial effects that:
according to the three-dimensional development multi-element collaborative optimization method and technology, in the initial stage of three-dimensional development design, on-site oil deposit three-dimensional development design parameters, comprehensive recovery ratio, total incomes in the maximum development period and total profits are combined, under the condition that the difference between the total profits and the maximum total profits is not large, the comprehensive recovery ratio and the total incomes in the maximum development period are found through the multi-element collaborative optimization method, the optimal development parameters are cooperated, and finally suggestions are provided for three-dimensional well pattern design and construction parameter design, so that the economic benefit of compact conglomerate oil deposit development is improved, and the method has good practicability.
Meanwhile, the scheme provided by the invention is simple and easy to implement, can be conveniently understood by field engineering personnel, and can intuitively and rapidly find the optimal development scheme on the premise of collaborative optimization of comprehensive recovery ratio and total income within the maximum development period. The coordinate space drawn by the invention is shallow and understandable, and the projection is visual and clear, so that a novel tool is provided for rapidly judging the advantages and disadvantages of the development scheme for field engineering personnel.
Comprehensive recovery ratio D defined by the invention 1 And total revenue D within the maximum development years 2 The definition of the two parameters is simple and feasible, and easy to understand and implement, thereby laying a foundation for the collaborative optimization of the technical scheme.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and drawings.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of the method of the present invention;
fig. 2 is a schematic diagram of spatial coordinates in the present embodiment.
FIG. 3 is a diagram showing the ratio of V to D at the remaining points in the present embodiment 1 And D 2 And under the condition of large size, the projection diagram on the O-D1-D2 plane is shown.
FIG. 4 shows a graph of D with at least one point compared with V 1 Or D 2 Under large conditions, the projection on the O-D1-D2 plane is schematic.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
When the three-dimensional well pattern development scheme is designed, the conventional trial calculation cannot obtain the optimal parameter set due to the plurality of parameters, and the recovery ratio and the total income are optimized on the premise of highest total profit, so that the development under the full period optimization of the three-dimensional well pattern is realized, and therefore, the collaborative optimization is needed.
The embodiment discloses a multi-element collaborative optimization method for oil reservoir three-dimensional well pattern development parameters, which is shown in fig. 1 and comprises the following steps:
s1, constructing a full-period three-dimensional development and evaluation model, wherein the full-period three-dimensional development and evaluation model takes well pattern development parameters as input parameters and targetsThe parameters are output parameters; the target parameter includes D 1 、D 2 M; the well pattern development parameters include L 1 ，L 2 ，m 1 T, C, r; wherein D is 1 For comprehensive recovery, D 2 For total revenue over development years, M is total profit; l (L) 1 For well spacing, L 2 For average horizontal segment length, m 1 The number of layers of the three-dimensional well pattern development layer is calculated, T is the estimated production age, C is the average total cost of single well drilling, construction and maintenance every year, and r is the discount rate.
S2, solving an optimal solution for the full-period three-dimensional development and evaluation model by using a multi-element collaborative optimization algorithm to obtain optimal well pattern development parameters.
Preferably, the full-cycle stereoscopic development and evaluation model includes a reservoir and engineering sub-model, a management sub-model, and a profit calculation sub-model.
The reservoir and engineering submodel are constructed by the following parameters: length X, width Y, well spacing L of target development zone 1 Average horizontal segment length L 2 Layer number m of three-dimensional well pattern development layer system 1 Estimated production period T, single well comprehensive decline rate D 0 First year predicted production Q of single well per kilometer horizontal segment length 0 The development area predicts geological reserves N and comprehensively recovers D 1 ；
Wherein D is 1 For the output parameters of the model, L 1 ，L 2 ，m 1 T is a variable parameter input by the model, X, Y, N, D 0 ,Q 0 A known constant input for the model;
the relation between the variable parameters and the output parameters input by the reservoir and the engineering submodel is as follows:
wherein square brackets are whole symbols, [ a ] represents a maximum integer not exceeding a; min (x 1, x 2) is a minimum symbol, representing the minimum of the parameters x1 and x 2.
The management sub-moduleModel construction involves the following parameters: length X, width Y, well spacing L of target development zone 1 Average horizontal segment length L 2 Layer number m of three-dimensional well pattern development layer system 1 Estimated production period T, single well comprehensive decline rate D 0 First year predicted production Q of single well per kilometer horizontal segment length 0 Total income D within the development period 2 At time t, selling oil price P of each ton of crude oil at the end of the t year;
wherein D is 2 For the output parameters of the model, L 1 ，L 2 ，m 1 T is a variable parameter input by the model, X, Y, D 0 ,Q 0 P is a known constant of the model input;
the relation between the variable parameters and the output parameters input by the management sub-model is as follows:
wherein square brackets are whole symbols, [ a ] represents a maximum integer not exceeding a; min (x 1, x 2) is a minimum symbol, representing the minimum of the parameters x1 and x 2.
The profit calculation sub-model is constructed by the following parameters: length X, width Y, well spacing L of target development zone 1 Average horizontal segment length L 2 Layer number m of three-dimensional well pattern development layer system 1 Estimated production period T, single well comprehensive decline rate D 0 First year predicted production Q of single well per kilometer horizontal segment length 0 Total income D within the development period 2 At time t, selling oil price P (t) of each ton of crude oil at the end of the t year, discount rate r, average total cost C of single well drilling, construction and maintenance each year, and total profit M;
wherein M is the output parameter of the model, L 1 ，L 2 ，m 1 T, C, r are the variable parameters of the model input, X, Y, D 0 ,Q 0 P is a known constant of the model input;
the relation between the variable parameters and the output parameters input by the profit calculation sub-model is as follows:
wherein square brackets are whole symbols, [ a ] represents a maximum integer not exceeding a; min (x 1, x 2) is a minimum symbol, representing the minimum of the parameters x1 and x 2.
Wherein the constants X, Y, N are determined by the underlying geophysical prospecting and geological data of the development scheme, D 0 Taking the average value of the single well comprehensive reduction rate of the adjacent block development well of the block, Q 0 Taking the average value of the first year predicted production of a single well of a block adjacent to the block development well per kilometer horizontal segment, wherein the data can be determined by known parameters or well-facing empirical values, and the determination method is a technology known to a person skilled in the art; the selling oil price P is a function changing along with the development years t and is expressed by P (t); the values were obtained by the disclosed oil price prediction model.
Preferably, the optimal solution is obtained for the full-period three-dimensional development and evaluation model by using a multi-element collaborative optimization algorithm, and the specific process for obtaining the optimal well pattern development parameters is as follows:
(1) Constructing a database of input parameters and output parameters of a full-period three-dimensional development and evaluation model by L 1 ，L 2 ，m 1 T, C, r are input parameters, and are input into a full-period three-dimensional development and evaluation model for simulation calculation, so that an output parameter D is obtained 1 ，D 2 Simulation results of M; each group of input parameters are provided with an output parameter corresponding to each other, and the data combination of the input parameters is in one-to-one correspondence with the output parameters to form one row of data in the database;
specifically: each input parameter actually defines a reasonable range according to the field engineering, an interpolation method is used for selecting an intermediate value, and a full life cycle optimization model is input into a data combination formed by mutually arranging and combining all the input parameters for calculation to obtain respective corresponding output results;
for example, L 1 ，L 2 ，m 1 Equidistant interpolation of S1, S2, S3, S4, S5, S6 is performed within the engineering reasonable range of T, P, C, rAnd S7 numbers, wherein each input parameter takes a value in interpolation during each calculation, and according to the arrangement and combination principle, the method finally needs to simulate S1×S2×S3×S4×S5×S6×S7 times, and each simulation can obtain corresponding D 1 ，D 2 M; finally, a database having s1×s2×s3×s4×s5×s6×s7 rows is formed.
(2) Build with D 1 For the x-axis, in D 2 Taking M as a coordinate space of a z axis as a y axis, taking each row of data in the database as a point, and putting the point into the coordinate space;
(3) Finding the maximum value of M in all simulation results, and finding the input parameters and the output parameters of the calculation example;
selecting two adjacent values of the input parameter as respective definition fields, and interpolating the respective definition fields of the variables to obtain an expanded input parameter (for example, when the M is maximum, the corresponding well distance value is L 1，i Then choose L 1,i-1 ，L 1,i+1 Well spacing L as the step 1 Further performing equidistant interpolation within the upper and lower domain limits of (2);
input parameters into a full-period three-dimensional development and evaluation model to perform simulation calculation to obtain output parameters D 1 ，D 2 Simulation results of M; adding the input parameters and the output parameters into a database, and adding points corresponding to the data record in the coordinate space of the step (2);
(4) Continuing to find the maximum value of M in the simulation result of the database, and then repeating the operation of the step (3) until the point in the space meets the criterion, and ending the iteration;
the criterion is as follows: maximum value M of M in previous interpolation calculation result i M is the maximum value of M in the interpolation calculation result i+1 Percent difference of (2)Stopping calculation when the accuracy value is smaller than the accuracy fixed value; the fixed value of the precision is 0.1, namely when epsilon<When 0.1, stopping calculation, wherein, of course, the precision fixed value is 0.1 as a preferable value, and setting according to the field requirement, the precisionWhen the requirement is high, the value is reduced, and when the accuracy requirement is low, the value is increased.
(5) The position of the point V at the time of termination of observation (its coordinates are (D 1,V ,D 2,V ,M V ) A Z-axis coordinate value M of a selected point j in a three-dimensional coordinate space j Satisfy the relationAs shown in fig. 2, the black dots satisfy the condition, and the hollow dots do not satisfy the condition; projecting the selected points onto an O-D1-D2 plane, and observing the position relation between V and the selected points:
d at point V 1 And D 2 D compared with the rest points 1 And D 2 When the total profit M is large (as in FIG. 3), the total recovery D is obtained in the maximum development period 1 And total revenue D over development years 2 Meanwhile, the maximum is reached, and the input and output parameters of the scheme represented by the point V are the optimal development scheme under the condition of multi-element collaborative optimization of the three-dimensional development of the region;
otherwise (when there is at least 1 point to the end point V D 1 Or D 2 When large), find the point closest to the origin among all points (as in fig. 4) as the optimal point, and the scheme represented by this point K is under the condition that the final total profit M is almost the same as the maximum value (i.e., at the optimal total profit M V Within ± η of (a), the value of η may preferably be 0.1), the resulting integrated recovery D 1 And total revenue D over development years 2 Are better in combination than other points, i.e. the distance from the originThe maximum, at this point, the scheme represented by point K is not as good as point V in the final total profit, but the comprehensive recovery rate D 1 Or total income D within the development years 2 In combination, better than point V, so that the input and output parameters of the scheme represented by point K are the optimal development scheme.
The invention also discloses a multi-element collaborative optimization system for the oil reservoir three-dimensional well pattern development parameters based on the multi-element collaborative optimization method for the oil reservoir three-dimensional well pattern development parameters, which comprises the following steps of
The construction unit: the method is used for constructing a full-period three-dimensional development and evaluation model, wherein the full-period three-dimensional development and evaluation model takes well pattern development parameters as input parameters and target parameters as output parameters;
a calculation unit: and the method is used for solving the optimal solution of the full-period three-dimensional development and evaluation model by using a multi-element collaborative optimization algorithm to obtain the optimal well pattern development parameters.
The functions and the implementation modes realized by the construction unit and the calculation unit in the multi-element collaborative optimization system of the oil reservoir three-dimensional well pattern development parameters are correspondingly consistent with the functions and the implementation modes of each step in the multi-element collaborative optimization method of the oil reservoir three-dimensional well pattern development parameters, so that the detailed description is omitted.
In summary, the principle of the invention is as follows:
when the three-dimensional well pattern development scheme is designed, the conventional trial calculation cannot obtain the optimal parameter set due to the plurality of parameters, and the recovery ratio and the total income are optimized on the premise of highest total profit, so that the development under the full period optimization of the three-dimensional well pattern is realized, and therefore, the collaborative optimization is needed.
The principle of the scheme is divided into the following parts:
in the whole life cycle optimizing model part of the scheme, input parameters (well spacing L 1 Average horizontal segment length L 2 Layer number m of three-dimensional well pattern development layer system 1 Estimated production life T, average total annual single well drilling, construction and maintenance costs C, discount rate r) and total profit M over output parameter development life, integrated recovery D 1 And total revenue D over development years 2 A functional relationship between them. Each time a known input parameter combination is given, it must correspond to an output parameter combination;
but when the total profit M is maximum, D 1 And D 2 Not necessarily, but also to the maximum, but to a solid wellNetwork development is a collaborative optimization process, so that multiple collaborative optimizations are needed, so:
firstly, selecting reasonable definition domains for input parameters, then linearly interpolating in the respective definition domains (such as for well spacing L 1 In a reasonable engineering sectionThe inner equidistant is divided into S1 number, then the optional set of well spacing inputs isSelecting a value from the interpolated set for each parameter to form an input parameter combination, and substituting the input parameter combination into the model to finally obtain D under the condition of all parameter combinations in the definition domain 1 ，D 2 M, forming a record by the input parameters and the corresponding output parameters, and recording the record into a database;
then selecting the maximum M value in the database to obtain the input parameter corresponding to the maximum M value, narrowing the current definition domain of each input parameter (i.e. taking the last interpolated left and right adjacent values of the input parameter corresponding to the maximum M value as the upper and lower limits of the definition domain of the calculation), and further interpolating in the updated definition domain of each parameter (e.g. the well spacing value corresponding to the maximum M value is L) i Then the upper and lower limits of the iteration well spacing are as followsAnd then the equidistant interpolation S1 number is continued in the range), and more accurate M can be obtained by repeating the calculation steps. When the M value obtained by two adjacent iterations is very small, the iterations are considered to be stable, and the total profit M of the final scheme is the maximum value in all development schemes.
However, although the total profit M is the greatest, the recovery factor D at this time is the same 1 And total revenue D over development years 2 Not necessarily the largest. If the comprehensive recovery ratio D 1 Not optimal, the oil extraction speed is too low, which is disadvantageous to development and the total income D in the development years 2 Not large enough to negatively affect the cash flow of the oilfield and therefore not Mmax is optimal, in a scenario similar to the maximum total profit Mmax (i.e., at the maximum total profit M V Within ± η) of (a) and selecting a comprehensive recovery factor D 1 And total revenue D over development years 2 And a synergic optimal scheme.
Thus, all the profit M at maximum is found out V Within ± η (each representing a development scheme) and then projected onto the O-D1-D2 plane:
if the point of maximum profit is larger than other points, it is indicated that the scheme can obtain the maximum comprehensive recovery and the maximum total income in the development period when the maximum total profit is reached, so that the scheme represented by the point is optimal (the input parameter can be used as the optimal/optimal recommended construction parameter).
If the total profit is not the maximum point, some other points have better comprehensive recovery ratio, some development years have better total income, and the total profit corresponding to the schemes is not different from the maximum predicted total profit, a distinguishing method needs to be constructed, and the scheme with the comprehensive optimal comprehensive recovery ratio and the total income in the development years is found. The invention adopts distance function, namelyWhen a certain scheme K is farthest from the original point, the comprehensive recovery ratio and the total income within the development period can be maximized under the condition of not greatly differing from the maximum total profit, and the development scheme represented by the point farthest from the original point O can be intuitively used as the optimal development scheme (the input parameters can be used as the optimal/optimal recommended construction parameters).
Although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. A multi-element collaborative optimization method for oil reservoir three-dimensional well pattern development parameters is characterized by comprising the following steps:
constructing a full-period three-dimensional development and evaluation model, wherein the full-period three-dimensional development and evaluation model takes well pattern development parameters as input parameters and target parameters as output parameters, and the target parameters comprise D 1 ，D 2 And M; the well pattern development parameters include L 1 ，L 2 ，m 1 T, C and r; wherein D is 1 For comprehensive recovery, D 2 For total revenue over development years, M is total profit; l (L) 1 For well spacing, L 2 For average horizontal segment length, m 1 The number of layers of the three-dimensional well pattern development layer is T is the estimated exploitation years, C is the average total cost of single well drilling, construction and maintenance every year, and r is the discount rate;
and solving an optimal solution of the full-period three-dimensional development and evaluation model by using a multi-element collaborative optimization algorithm to obtain optimal well pattern development parameters, wherein the specific process is as follows:
s1, constructing a database of input parameters and output parameters of a full-period three-dimensional development and evaluation model, and using L 1 ，L 2 ，m 1 T, C, r are input parameters, and are input into a full-period three-dimensional development and evaluation model for simulation calculation, so that an output parameter D is obtained 1 ，D 2 Simulation results of M; each group of input parameters are provided with an output parameter corresponding to each other, and the data combination of the input parameters is in one-to-one correspondence with the output parameters to form one row of data in the database;
s2, build D 1 For the x-axis, in D 2 Taking M as a coordinate space of a z axis as a y axis, taking each row of data in the database as a point, and putting the point into the coordinate space;
s3, finding the maximum value of M in all simulation results, and finding the input parameters and the output parameters of the calculation example;
selecting left and right adjacent values of the input parameter as respective definition domains, and interpolating the respective definition domains of each variable to obtain an expanded input parameter;
input parameters into a full-period three-dimensional development and evaluation model to perform simulation calculation to obtain output parameters D 1 ，D 2 Simulation results of M; adding the input parameters and the output parameters into a database, and adding points corresponding to the data record in the coordinate space of S2;
s4, continuing to find the maximum value of M in the simulation result of the database, and then repeating the operation of S3 until the point in the space meets the criterion, and ending iteration;
s5, confirming the coordinates of a termination point V when iteration is terminated;
d at the termination point V 1 And D 2 D of points than the rest 1 And D 2 When the parameters are all large, taking the input parameters of the scheme represented by the point V as the optimal well pattern development parameters;
when there is at least 1 point to the end point V D 1 Or D 2 When the distance between each point and the origin is large, all points of M meeting the condition of the limiting range in the space are taken, and the distance between each point and the origin is calculatedAnd taking the input parameters of the scheme represented by the point with the maximum R as the optimal well pattern development parameters.
2. The method for multi-element collaborative optimization of reservoir three-dimensional well pattern development parameters according to claim 1, wherein the full-cycle three-dimensional development and evaluation model comprises a reservoir and engineering sub-model, a management sub-model and a profit calculation sub-model.
3. The method for multi-element collaborative optimization of oil reservoir three-dimensional well pattern development parameters according to claim 2, wherein the reservoir and engineering sub-model are constructed by the following parameters: length X, width Y, well spacing L of target development area 1 Average horizontal segment length L 2 Layer number m of three-dimensional well pattern development layer system 1 Estimated production period T, single well comprehensive decline rate D 0 First year predicted production Q of single well per kilometer horizontal segment length 0 The development area predicts geological reserves N and comprehensively recovers D 1 At time t, selling oil price P (t) of each ton of crude oil at the end of the t year;
wherein D is 1 For the output parameters of the model, L 1 ，L 2 ，m 1 T is a variable parameter input by the model, X, Y, N, D 0 ，Q 0 P (t) is a known constant of the model input;
the relation between the variable parameters and the output parameters input by the reservoir and the engineering submodel is as follows:
wherein square brackets are whole symbols, [ a ] represents a maximum integer not exceeding a; min (x 1, x 2) is a minimum symbol, representing the minimum of the parameters x1 and x 2.
4. The method for multi-element collaborative optimization of oil reservoir three-dimensional well pattern development parameters according to claim 2, wherein the management submodel is constructed by the following parameters: length X, width Y, well spacing L of target development zone 1 Average horizontal segment length L 2 Layer number m of three-dimensional well pattern development layer system 1 Estimated production period T, single well comprehensive decline rate D 0 First year predicted production Q of single well per kilometer horizontal segment length 0 Total income D within the development period 2 At time t, selling oil price P (t) of each ton of crude oil at the end of the t year;
wherein D is 2 For the output parameters of the model, L 1 ，L 2 ，m 1 T is a variable parameter input by the model, X, Y and D 0 ，Q 0 P (t) is a known constant of the model input;
the relation between the variable parameters and the output parameters input by the management sub-model is as follows:
wherein square brackets are whole symbols, [ a ] represents a maximum integer not exceeding a; min (x 1, x 2) is a minimum symbol, representing the minimum of the parameters x1 and x 2.
5. The method for multi-element collaborative optimization of oil reservoir three-dimensional well pattern development parameters according to claim 2, wherein the profit computation sub-model is constructed by the following parameters: length X, width Y, well spacing L of target development zone 1 Average horizontal segment length L 2 Layer number m of three-dimensional well pattern development layer system 1 Estimated production period T, single well comprehensive decline rate D 0 First year predicted production Q of single well per kilometer horizontal segment length 0 Total income D within the development period 2 At time t, selling oil price P (t) of each ton of crude oil at the end of the t year, discount rate r, average total cost C of single well drilling, construction and maintenance each year, and total profit M;
wherein M is the output parameter of the model, L 1 ，L 2 ，m 1 T, C, r are the variable parameters input by the model, X, Y, D 0 ，Q 0 P (t) is a known constant of the model input;
the relation between the variable parameters and the output parameters input by the profit calculation sub-model is as follows:
wherein square brackets are whole symbols, [ a ] represents a maximum integer not exceeding a; min (x 1, x 2) is a minimum symbol, representing the minimum of the parameters x1 and x 2.
6. The method for multi-element collaborative optimization of oil reservoir three-dimensional well pattern development parameters according to claim 1, wherein the criterion in S4 is as follows: maximum value M of M in previous interpolation calculation result i M is the maximum value of M in the interpolation calculation result i+1 Percent difference of (2)Stopping calculation when the accuracy value is smaller than the accuracy fixed value; the fixed value of the precision is 0.1, namely when epsilon<Stopping calculation when 0.1 is reached;
the limiting range condition in the S5 is that M meets the conditions of [ M-eta, M+eta ], and the value of eta is 0.1.
7. The utility model provides a three-dimensional well pattern development parameter's of oil reservoir multicomponent collaborative optimization system which characterized in that includes:
the construction unit: the method is used for constructing a full-period three-dimensional development and evaluation model, wherein the full-period three-dimensional development and evaluation model takes well pattern development parameters as input parameters and target parameters as output parameters;
a calculation unit: the method comprises the steps of obtaining an optimal solution for a full-period three-dimensional development and evaluation model by using a multi-element collaborative optimization algorithm to obtain optimal well pattern development parameters; the calculation unit calculates an optimal well pattern development parameter, which specifically comprises the following steps:
s1, constructing a database of input parameters and output parameters of a full-period three-dimensional development and evaluation model, and using L 1 ，L 2 ，m 1 T, C, r are input parameters, and are input into a full-period three-dimensional development and evaluation model for simulation calculation, so that an output parameter D is obtained 1 ，D 2 Simulation results of M; each group of input parameters are provided with an output parameter corresponding to each other, and the data combination of the input parameters is in one-to-one correspondence with the output parameters to form one row of data in the database; wherein D is 1 For comprehensive recovery, D 2 For total revenue over development years, M is total profit; l (L) 1 For well spacing, L 2 For average horizontal segment length, m 1 The number of layers of the three-dimensional well pattern development layer is T is the estimated exploitation years, C is the average total cost of single well drilling, construction and maintenance every year, and r is the discount rate;
s2, build D 1 For the x-axis, in D 2 Taking M as a coordinate space of a z axis as a y axis, taking each row of data in the database as a point, and putting the point into the coordinate space;
s3, finding the maximum value of M in all simulation results, and finding the input parameters and the output parameters of the calculation example;
selecting left and right adjacent values of the input parameter as respective definition domains, and interpolating the respective definition domains of each variable to obtain an expanded input parameter;
input parameters into a full-period three-dimensional development and evaluation model to perform simulation calculation to obtain output parameters D 1 ，D 2 Simulation results of M; adding the input parameters and the output parameters into a database, and adding points corresponding to the data record in the coordinate space of S2;
s4, continuing to find the maximum value of M in the simulation result of the database, and then repeating the operation of S3 until the point in the space meets the criterion, and ending iteration;
s5, confirming the coordinates of a termination point V when iteration is terminated;
d at the termination point V 1 And D 2 D of points than the rest 1 And D 2 When the parameters are all large, taking the input parameters of the scheme represented by the point V as the optimal well pattern development parameters;
when there is at least 1 point to the end point V D 1 Or D 2 When the space is large, taking all points of M meeting the condition of the limited range in the space, and calculating each pointAnd taking the input parameters of the scheme represented by the point with the maximum R as the optimal well pattern development parameters.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104331537A (en) * | 2014-09-28 | 2015-02-04 | 长江大学 | Well placement optimization design method based on reservoir static factors |
| CN107829719A (en) * | 2017-02-21 | 2018-03-23 | 中国石油化工股份有限公司 | The Optimization Design of marine oil reservoir new district economic optimum well pattern |
| CN109386272A (en) * | 2017-08-07 | 2019-02-26 | 中国石油化工股份有限公司 | Ultra deep reef flat facies gas reservoir rational spacing between wells Multipurpose Optimal Method |
| RU2692369C1 (en) * | 2018-12-26 | 2019-06-24 | Публичное акционерное общество "Газпром нефть" | Method of selecting deposit development system |
| CN110306968A (en) * | 2018-03-27 | 2019-10-08 | 中国石油化工股份有限公司 | Irregular well pattern optimization method and its computer readable storage medium |
| CN111160669A (en) * | 2020-01-03 | 2020-05-15 | 中国石油化工股份有限公司 | Multilayer stress sensitive oil reservoir fracturing water-drive well pattern and production system optimization method |
| CN111222243A (en) * | 2020-01-06 | 2020-06-02 | 长江大学 | Method, medium, terminal and device for optimizing well pattern distribution of fractured horizontal well |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200080406A1 (en) * | 2018-09-06 | 2020-03-12 | American University Of Beirut | Black hole particle swarm optimization for optimal well placement in field development planning and methods of use |
-
2021
- 2021-12-31 CN CN202111673365.7A patent/CN115467650B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104331537A (en) * | 2014-09-28 | 2015-02-04 | 长江大学 | Well placement optimization design method based on reservoir static factors |
| CN107829719A (en) * | 2017-02-21 | 2018-03-23 | 中国石油化工股份有限公司 | The Optimization Design of marine oil reservoir new district economic optimum well pattern |
| CN109386272A (en) * | 2017-08-07 | 2019-02-26 | 中国石油化工股份有限公司 | Ultra deep reef flat facies gas reservoir rational spacing between wells Multipurpose Optimal Method |
| CN110306968A (en) * | 2018-03-27 | 2019-10-08 | 中国石油化工股份有限公司 | Irregular well pattern optimization method and its computer readable storage medium |
| RU2692369C1 (en) * | 2018-12-26 | 2019-06-24 | Публичное акционерное общество "Газпром нефть" | Method of selecting deposit development system |
| CN111160669A (en) * | 2020-01-03 | 2020-05-15 | 中国石油化工股份有限公司 | Multilayer stress sensitive oil reservoir fracturing water-drive well pattern and production system optimization method |
| CN111222243A (en) * | 2020-01-06 | 2020-06-02 | 长江大学 | Method, medium, terminal and device for optimizing well pattern distribution of fractured horizontal well |
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