CN106703776B - Fracturing parameter optimization method - Google Patents
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Abstract
The invention discloses a fracturing parameter optimization method, and belongs to the field of exploration and development of oil and gas reservoirs. The method comprises the following steps: collecting reservoir parameters, fracturing parameters, yield increase effect data and reservoir parameters of a well to be fractured of a plurality of groups of fractured wells; performing correlation analysis on the collected reservoir parameters and yield increasing effect data of the fractured wells and the collected fracturing parameters and yield increasing effect data to obtain a mathematical statistic result; applying an orthogonal experimental scheme design and simultaneously combining the fracturing parameters of the fractured well and the reservoir parameters of the well to be fractured to form a plurality of groups of fracturing parameter alternative schemes of the well to be fractured; and converting the fuzzy comprehensive evaluation matrix of the alternative scheme into a fuzzy comprehensive evaluation score, and determining a final optimal scheme from high to low of the fuzzy comprehensive evaluation score and combining the construction risk from low to high according to a maximum membership principle. The invention improves the optimization rationality and effectiveness of fracturing parameters and yield increasing effect.
Description
Technical Field
The invention relates to the field of exploration and development of oil and gas reservoirs, in particular to a fracturing parameter optimization method.
Background
Hydraulic fracturing is a key technology for exploration and development of low-permeability and compact oil and gas reservoirs. The yield increasing effect after fracturing of the oil and gas well is influenced by reservoir parameters (effective thickness, porosity, gas saturation, compensating neutrons and compensating density), and the optimization of fracturing parameters (construction discharge capacity, average sand ratio, pad fluid ratio and sand adding strength) is also a key factor for determining the yield increasing effect.
The currently common fracturing construction parameter optimization method is to establish mathematical expressions among reservoir parameters, fracturing parameters and yield increasing effects by regression analysis or a neural network method by utilizing the reservoir parameters, the fracturing parameters and the yield increasing effects of a large number of fractured wells in an oil field block. And then for the well with the optimized fracturing construction parameters, because the reservoir parameters of the well are determined, the fracturing well stimulation effects under different parameter combinations are calculated by changing different fracturing parameter combinations and utilizing the established mathematical expressions, and the parameter combination with the best stimulation effect is selected as the optimized fracturing parameters.
In the process of implementing the invention, the inventor finds that the prior art has at least the following problems:
the numerical value of each reservoir parameter is taken from deterministic numerical values such as limit values, average values and the like of each parameter, and the uncertainty or errors of experimental instruments, experimental methods, calculation analysis and the like of each reservoir parameter are not fully considered, so that the parameters do not necessarily reflect the true values of the parameters under the reservoir condition, and the characteristic that the parameters have certain ambiguity is ignored. Secondly, when a mathematical relation is established by regression analysis or a neural network method by using reservoir parameters, fracturing parameters and a yield increase effect of the fracturing well, when the reservoir parameters, the fracturing parameters and the yield increase effect do not have a certain mathematical relation, the deviation of the optimized fracturing parameters is increased. Meanwhile, the optimal schemes of the selected fracturing parameters are too few, and the finally optimized fracturing parameters can only be equivalent to the local optimal fracturing parameters rather than the global optimal fracturing parameters in the true sense.
Disclosure of Invention
In order to solve the problems in the prior art, the embodiment of the invention provides a fracturing parameter optimization method. The technical scheme is as follows:
a fracturing parameter optimization method comprises the following steps:
s100, collecting reservoir parameters, fracturing parameters, yield increase effect data and reservoir parameters of a well to be fractured of a plurality of groups of fractured wells;
s200, performing correlation analysis on the collected reservoir parameters and yield increasing effect data of the fractured well and the collected fracturing parameters and yield increasing effect data to obtain a mathematical statistic result;
s300, establishing a membership function relationship and a single-factor fuzzy evaluation matrix of the reservoir parameters and the yield increasing effect data and the fracturing parameters and the yield increasing effect data based on the mathematical statistics result;
s400, based on the mathematical statistics result, establishing a weight matrix of reservoir parameters in yield increasing effect data and fracturing parameters in yield increasing effect data by using a grey correlation theory;
s500, applying an orthogonal experimental scheme design, and simultaneously combining the fracturing parameters of the fractured well and the reservoir parameters of the well to be fractured to form a plurality of groups of fracturing parameter alternative schemes of the well to be fractured;
s600, based on the fracturing parameter alternative scheme of the well to be fractured, applying the membership function relationship established by the fracturing well and the single-factor fuzzy evaluation matrix, and compounding the single-factor fuzzy evaluation matrix and the weight matrix to obtain a fuzzy comprehensive evaluation matrix of the alternative scheme;
s700, converting the fuzzy comprehensive evaluation matrix of the alternative scheme into a fuzzy comprehensive evaluation score, and determining a final optimal scheme from high to low according to the maximum membership principle and combining the construction risk from low to high.
Optionally, the reservoir parameters, the fracturing parameters and the stimulation effect data in the step S100 include reservoir effective thickness, porosity, gas saturation, compensation neutrons, compensation density, construction displacement, pad fluid proportion, average sand ratio, sand adding strength and stimulation effect data.
Optionally, the step S200 specifically includes:
and performing single-factor linear regression analysis on the collected reservoir parameters and yield increasing effect data of the fractured wells and the collected fracturing parameters and yield increasing effect data to determine whether the reservoir parameters, the fracturing parameters and the yield increasing effect of the fractured wells belong to a positive correlation or a negative correlation, and performing 4-class equal evaluation grades between the minimum value and the maximum value of the parameters on the parameters according to the positive correlation and the negative correlation.
Optionally, step S300 specifically includes:
establishing membership functions of all parameters according to the mathematical statistics result of the single factor values among the reservoir parameters, the fracturing parameters and the yield increasing effect of the fractured wells, selecting approximate normal distribution membership functions,
in the formula: u. ofvMembership functions, dimensionless; d-judging the specific value of the parameter without dimension; v is evaluation grade, which is totally 4 evaluation grades of I, II, III and IV and has no dimension; a, b-evaluation of the respective gradesThe index parameters of (1) are dimensionless;
the membership function for the same rating level is determined as,
in the formula: u. ofvj(di)—diAt an evaluation level vjMembership degree on the surface, and no dimension; di-specific values of the ith evaluation parameter, dimensionless;
as shown in the formula (2): when d isiWhen being a, uvj(a) Since 1 indicates that the degree of membership of the evaluation parameter belonging to one evaluation level is the largest, a is a mathematical expectation of a certain level, that is, a
a＝(d1+d2)/2 (3)
Points with equal membership degrees of adjacent evaluation grades are called transition points, the membership degree of the transition points is taken as 0.5, namely,
in the formula: d1、d2Upper and lower limit values of j-th evaluation level interval, d of each evaluation level of each evaluation parameter1、d2The value is determined by the mathematical statistic result, and the known membership function relationship can be determined after the values a and b are determined.
The single-factor fuzzy evaluation matrix corresponding to each parameter can be obtained by the calculation of the formulas (1) to (5), if m evaluation grades and n evaluation parameters are shared, a single-factor fuzzy evaluation matrix R of n multiplied by m orders can be obtained,
optionally, the step S500 specifically includes:
according to the fracturing parameters of the fractured well, counting the fracturing parameter range of the fractured well, determining three 3-level factors between the minimum value and the maximum value of each fracturing parameter, and forming a scheme of construction displacement, a pad fluid ratio, an average sand ratio and sand adding strength of the fracturing parameters of the fractured well, wherein the scheme is an experimental scheme of three 3-level factors with four factors in total, and an optimal orthogonal experiment optimal table is established;
and matching the collected effective thickness, porosity, gas saturation, compensation neutrons and compensation density parameters of the reservoir of the well to be fractured, and combining an orthogonal experimental table to form an alternative scheme of the fracturing parameters of the well to be fractured.
Optionally, the step S600 specifically includes:
determining a single-factor evaluation matrix R of each parameter by applying a formula (6) according to the effective thickness, porosity, gas saturation, compensation neutrons, compensation density parameters and fracturing parameter optimization orthogonal experiment optimization table combination of the well to be fractured;
according to the single-factor evaluation matrix R, a matrix product summation algorithm is adopted to compound the fuzzy weight vector A and the single-factor fuzzy evaluation matrix R, the contribution of all parameters is comprehensively considered to obtain a fuzzy comprehensive evaluation matrix B,
optionally, step S700 specifically includes:
converting the fuzzy comprehensive evaluation matrix of each alternative scheme into a fuzzy comprehensive evaluation score according to a maximum membership principle, wherein the higher the score is, the better the yield increasing effect of the alternative scheme is after implementation is shown; when fuzzy comprehensive scores of different alternatives are the same, comprehensively considering the fracturing risk and the operation cost to determine a final optimal scheme;
the calculation formula is as follows,
wherein: v. ofI、vII、vIII、vIVThe scores corresponding to the evaluation grades were 100, 75, 50, and 25, respectively.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
the fracturing parameter optimization method fully considers the problems that a plurality of factors influencing the yield increasing effect are fuzzy, the yield increasing effect and the factors do not have a definite functional relation expression, thereby overcoming the problem that only local optimization but not all optimization of the fracturing parameters can be realized in the prior art, and improving the rationality, effectiveness and yield increasing effect of the fracturing parameter optimization.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of a preferred method of fracturing parameters provided by an embodiment of the present invention;
FIG. 2a is a plot of the effect of fracturing well stimulation as a function of effective thickness provided by an embodiment of the present invention;
FIG. 2b is a plot of the fracture stimulation effect versus porosity provided by an embodiment of the present invention;
FIG. 2c is a graph of fractured well stimulation effectiveness versus gas saturation provided by an embodiment of the present invention;
FIG. 2d is a plot of the stimulation effect of a fractured well versus offset neutrons provided by an embodiment of the present invention;
FIG. 2e is a plot of the stimulation effect of a fractured well versus offset density provided by an embodiment of the present invention;
FIG. 2f is a graph of the fracturing well stimulation effect versus the construction displacement provided by an embodiment of the present invention;
FIG. 2g is a plot of the fracturing well stimulation effect versus average sand ratio provided by an embodiment of the present invention;
FIG. 2h is a graph of the fracturing well stimulation effect versus pad fluid ratio provided by an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
The embodiment of the invention provides a fracturing parameter optimization method, which is shown in figure 1 and comprises the following steps:
s100, collecting reservoir parameters, fracturing parameters, yield increase effect data and reservoir parameters of a well to be fractured of a plurality of groups of fractured wells.
S200, performing correlation analysis on the collected reservoir parameters and yield increasing effect data of the fractured well and the collected fracturing parameters and yield increasing effect data to obtain a mathematical statistic result.
S300, based on the mathematical statistics result, establishing a membership function relationship and a single-factor fuzzy evaluation matrix of the reservoir parameters and the yield increasing effect data and the fracturing parameters and the yield increasing effect data.
S400, based on the mathematical statistics result, a weight matrix of reservoir parameters in yield increasing effect data and fracturing parameters in yield increasing effect data is established by using a grey correlation theory.
S500, applying an orthogonal experimental scheme design, and simultaneously combining the fracturing parameters of the fractured well and the reservoir parameters of the well to be fractured to form a plurality of groups of fracturing parameter alternative schemes of the well to be fractured.
S600, based on the fracturing parameter alternative scheme of the well to be fractured, the membership function relation established by the fracturing well and the single-factor fuzzy evaluation matrix are applied, and the single-factor fuzzy evaluation matrix and the weight matrix are compounded to obtain a fuzzy comprehensive evaluation matrix of the alternative scheme.
S700, converting the fuzzy comprehensive evaluation matrix of the alternative scheme into a fuzzy comprehensive evaluation score, and determining a final optimal scheme from high to low according to the maximum membership principle and combining the construction risk from low to high.
In this embodiment, the reservoir parameter, the fracturing parameter, and the stimulation effect data in step S100 specifically include reservoir effective thickness, porosity, gas saturation, compensation neutrons, compensation density, construction displacement, pad fluid ratio, average sand ratio, sand adding strength, and stimulation effect data.
In this embodiment, step S200 specifically includes:
and performing single-factor linear regression analysis on the reservoir parameters, the fracturing parameters and the yield increase effect data of the fractured well, and determining whether the value of the yield increase effect is increased or decreased along with the increase of the reservoir parameters and the fracturing parameters of the fractured well.
And further determining whether the reservoir parameters, the fracturing parameters and the yield increasing effect of the fractured well belong to positive correlation or negative correlation, and performing 4-class equal evaluation grades such as 'I', 'II', 'III' and 'IV' between the minimum value and the maximum value of the parameters on the parameters according to the positive correlation and the negative correlation, wherein the 'I' grade evaluation grade of the parameters of the positive correlation indicates that the parameters are large, and the 'I' grade evaluation of the parameters of the negative correlation indicates that the parameters are small.
In this embodiment, step S300 specifically includes:
according to the mathematical statistics result of the single factor value among the reservoir parameters, the fracturing parameters and the yield increasing effect of the fractured well, the membership function of each parameter is established, the approximate normal distribution membership function is selected,
in the formula: u. ofvMembership functions, dimensionless; d-judging the specific value of the parameter without dimension; v is evaluation grade, which is totally 4 evaluation grades of I, II, III and IV and has no dimension; a, b-index parameters of each evaluation grade, and no dimension.
The membership function for the same rating level is determined as,
in the formula: u. ofvj(di)—diAt an evaluation level vjMembership degree on the surface, and no dimension; di-specific values of the ith evaluation parameter, dimensionless.
As shown in the formula (2): when d isiWhen being a, uvj(a) Since 1 indicates that the degree of membership of the evaluation parameter belonging to one evaluation level is the largest, a is a mathematical expectation of a certain level, that is, a
a＝(d1+d2)/2 (3)
And (3) points with equal membership degrees of adjacent evaluation grades are called transition points, and the membership degree of the transition points is taken as 0.5, namely:
in the formula: d1、d2Upper and lower limit values of j-th evaluation level interval, d of each evaluation level of each evaluation parameter1、d2The value is determined by the mathematical statistic result, and the known membership function relationship can be determined after the values a and b are determined.
The single-factor fuzzy evaluation matrix corresponding to each parameter can be obtained by the calculation of the formulas (1) to (5), if m evaluation grades and n evaluation parameters are shared, a single-factor fuzzy evaluation matrix R of n multiplied by m orders can be obtained,
in this embodiment, step S500 specifically includes:
according to the fracturing parameters of the fractured well, the range of the fracturing parameters of the fractured well is counted, three 3-level factors are determined between the minimum value and the maximum value of each fracturing parameter, and the construction discharge capacity, the pre-liquid ratio, the average sand ratio and the sand adding strength of the fracturing parameters of the fractured well are formed.
TABLE 1 optimized orthogonal experiment table for optimized fracturing parameters of well to be fractured
And matching the collected effective thickness, porosity, gas saturation, compensation neutrons and compensation density parameters of the reservoir of the well to be fractured, and combining the orthogonal experiment optimization table to form an alternative scheme of the fracturing parameters of the well to be fractured.
In this embodiment, step S600 specifically includes:
and (3) determining a single-factor evaluation matrix R of each parameter by applying a formula (6) according to the effective thickness, porosity, gas saturation, compensation neutrons, compensation density parameters and fracturing parameter optimization orthogonal experiment optimization table combination of the well to be fractured.
According to the single-factor evaluation matrix R, a matrix product summation algorithm is adopted to compound the fuzzy weight vector A and the single-factor fuzzy evaluation matrix R, the contribution of all parameters is comprehensively considered to obtain a fuzzy comprehensive evaluation matrix B,
in this embodiment, step S700 specifically includes:
converting the fuzzy comprehensive evaluation matrix of each alternative scheme into a fuzzy comprehensive evaluation score according to a maximum membership principle, wherein the higher the score is, the better the yield increasing effect of the alternative scheme is after implementation is shown; and when fuzzy comprehensive scores of different alternatives are the same, comprehensively considering the fracturing risk and the operation cost to determine a final optimal scheme.
The calculation formula is as follows
Wherein: v. ofI、vII、vIII、vIVCorresponding to evaluation levelThe scores were 100, 75, 50, 25, respectively.
By applying the steps of the method, the implementation process of the embodiment is as follows:
for a specific oil and gas reservoir, the fracturing parameters of a follow-up well to be fractured are optimized by using the basic reservoir parameters, the fracturing parameters and the yield increasing effect of a fractured well under the condition that the fracturing process, equipment, fracturing fluid and proppant of the fractured well are not changed. Among the many factors that affect the stimulation effect, the stimulation effect is sensitive to changes in some parameters and not to others. Through the fractured well data, correlation analysis is adopted, and field expert experience is combined, and finally, data of effective thickness, porosity, gas saturation, compensation neutrons, compensation density, construction discharge capacity, prepad fluid proportion, average sand ratio, sand adding strength and yield increasing effect of 15 wells are selected and determined as a research basis, and the data are shown in table 2. Wherein, the numbers 1-15 are the basic parameters and the yield increasing effect of the fractured well, and the number 16 is the basic parameters of the well to be fractured which needs the optimized construction parameters.
TABLE 2 statistics of fractured well reservoir parameters, construction parameters and production increasing effect
Referring to fig. 2, the fracturing well stimulation effect is in positive correlation with the effective thickness, porosity, gas saturation, compensation neutrons and sand adding strength, that is, as the numerical values of the parameters increase, the fracturing well stimulation effect becomes better; and the additive is in negative correlation with the compensation density, the construction discharge capacity, the average sand ratio and the prepad fluid ratio, namely, the yield increasing effect of the fracturing well is deteriorated along with the increase of the numerical values of the parameters.
According to the statistical result of the correlation between each parameter and the yield increasing effect, each parameter is divided into 4 evaluation grades, as shown in table 3.
TABLE 3 evaluation parameters evaluation grade between divisions
The fractured well reservoir parameters, the fracturing parameters and the yield increase effect data are utilized, the weight of the parameters to the yield increase effect is determined by using a grey correlation theory, and a weight matrix A occupied by each parameter is obtained, as shown in Table 4.
TABLE 4 weight of each evaluation factor to yield increasing effect
A＝[0.1329 0.1284 0.1274 0.1248 0.0906 0.0879 0.0881 0.0850 0.1344](9)
Selecting an orthogonal experiment table and designing orthogonal experiment parameters and schemes by applying orthogonal experiment scheme design and combining the range of fracturing parameters of a fractured well; and simultaneously integrating reservoir parameters of the well to be fractured and the orthogonal experimental scheme to form a fracturing parameter alternative scheme of the well to be fractured, as shown in table 5.
TABLE 5 statistics of fractured well reservoir parameters, construction parameters and production increasing effect
According to each parameter of the fracturing parameter alternative scheme of the well to be fractured, evaluating the single-factor evaluation function of each parameter of the alternative scheme of the well to be fractured by applying each parameter membership function established by the fracturing well, further compounding each single-factor evaluation function with the weight matrix to obtain the fuzzy comprehensive evaluation matrix of each alternative scheme, taking each basic parameter in the scheme number 1 as an example, calculating the single-factor evaluation matrix of the scheme 1 according to the formulas (1) - (5) by combining the data of the tables 3 and 4,
the single-factor evaluation matrix only reflects the membership degree of each factor in each interval and cannot reflect the comprehensive influence result of each parameter on the yield increasing effect. Taking the effective thickness as an example, the membership degree of the evaluation result belonging to the "I" class of the effective thickness in the scheme 1 is reflected to be 0.5000, the membership degree of the evaluation result belonging to the "II", "III" or "IV" class is reflected to be 0, and the effective thickness is subordinate to the "I" class of the evaluation result according to the maximum membership degree principle.
And compounding the weight and the single-factor evaluation matrix to obtain a multi-factor comprehensive evaluation matrix.
And (3) according to a maximum membership principle, converting the fuzzy evaluation matrix into an evaluation score of 83 points according to a formula (8), wherein the comprehensive evaluation result of the construction scheme I belongs to an I-type evaluation result.
And repeating the steps to complete the comprehensive evaluation results of the schemes 2-9 in the alternative scheme of the well to be fractured, and calculating a fuzzy score as shown in the table 6.
TABLE 6 comprehensive evaluation results of different protocols
According to the comprehensive scoring results of the 9 schemes in the table 6, the fuzzy comprehensive evaluation scores of the schemes 3, 4, 5 and 8 are all 84 scores, which shows that the 4 schemes can obtain the same best results after construction. Further comprehensively considering the fracturing risk and the operation cost to determine a final preferable scheme, the sand adding strength used in the scheme 5 is the lowest and is only 1.2m3And/m is beneficial to reducing the cost and improving the construction safety, and finally, the scheme 5 is preferably selected as a construction scheme.
In conclusion, the embodiment of the invention fully considers the problems that a plurality of factors influencing the yield increasing effect are ambiguous, the obtained parameters have ambiguity, and the yield increasing effect and the factors do not have a definite functional relation expression, thereby overcoming the problem that only local optimization but not complete optimization of the fracturing parameters can be realized in the prior art, and improving the rationality, effectiveness and yield increasing effect of the optimal fracturing parameters.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (4)
1. A method for optimizing fracturing parameters is characterized by comprising the following steps:
s100, collecting reservoir parameters, fracturing parameters, yield increase effect data and reservoir parameters of a well to be fractured of a plurality of groups of fractured wells;
the reservoir parameter, the fracturing parameter and the yield increase effect data in the step S100 comprise reservoir effective thickness, porosity, gas saturation, compensation neutron, compensation density, construction discharge capacity, pad fluid proportion, average sand ratio, sand adding strength and yield increase effect data;
s200, carrying out correlation analysis on the collected reservoir parameters and yield increase effect data of the fractured well and the collected fracturing parameters and yield increase effect data to obtain a mathematical statistic result;
the step S200 is specifically:
performing single-factor linear regression analysis on the collected reservoir parameters and yield-increasing effect data of the fractured wells and the collected fracturing parameters and yield-increasing effect data to determine whether the reservoir parameters, the fracturing parameters and the yield-increasing effect of the fractured wells belong to a positive correlation or a negative correlation, and performing 4-class equal evaluation grades between the minimum value and the maximum value of the parameters on the parameters according to the positive correlation and the negative correlation;
s300, establishing a membership function relationship and a single-factor fuzzy evaluation matrix of the reservoir parameters and the yield increasing effect data and the fracturing parameters and the yield increasing effect data based on the mathematical statistics result;
the step S300 specifically includes:
establishing membership functions of all parameters according to the mathematical statistics result of the single factor values among the reservoir parameters, the fracturing parameters and the yield increasing effect of the fractured wells, selecting approximate normal distribution membership functions,
in the formula: u. ofv-membership functions, dimensionless; d-evaluating the specific value of the parameter without dimension; v-evaluation grade, 4 types of evaluation grades including I, II, III and IV are adopted, and no factor is generated; a, b-index parameters of each evaluation grade without dimension;
the membership function for the same rating level is determined as,
in the formula: u. ofvj(di)-diAt an evaluation level vjMembership degree on the surface, and no dimension; di-specific values of the ith evaluation parameter, dimensionless;
as shown in the formula (2): when d isiWhen being a, uvj(a) Since 1 indicates that the degree of membership of the evaluation parameter belonging to one evaluation level is the largest, a is a mathematical expectation of a certain level, that is, a
a＝(d1+d2)/2 (3)
The points with the same membership degree of the adjacent evaluation grades are called transition points, and the membership degree of the transition points is taken as 0.5, namely
In the formula: d1、d2Upper and lower limits of the j-th evaluation level interval, d of each evaluation level of each evaluation parameter1、d2The value is determined by a mathematical statistic result, and a known membership function relationship can be determined after the values a and b are determined;
the single-factor fuzzy evaluation matrix corresponding to each parameter can be obtained by the calculation of the formulas (1) to (5), if m evaluation grades and n evaluation parameters are shared, a single-factor fuzzy evaluation matrix R of n multiplied by m orders can be obtained,
s400, based on the mathematical statistics result, establishing a weight matrix of reservoir parameters in yield increasing effect data and fracturing parameters in yield increasing effect data by using a grey correlation theory;
s500, applying an orthogonal experimental scheme design, and simultaneously combining the fracturing parameters of the fractured well and the reservoir parameters of the well to be fractured to form a plurality of groups of fracturing parameter alternative schemes of the well to be fractured, wherein the fracturing parameters of the fractured well are obtained by statistics, even segmentation and acquisition according to fracturing data;
s600, based on the fracturing parameter alternative scheme of the well to be fractured, applying the membership function relationship established by the fracturing well and the single-factor fuzzy evaluation matrix, and compounding the single-factor fuzzy evaluation matrix and the weight matrix to obtain a fuzzy comprehensive evaluation matrix of the alternative scheme;
s700, converting the fuzzy comprehensive evaluation matrix of the alternative scheme into a fuzzy comprehensive evaluation score, and determining a final optimal scheme from high to low according to the maximum membership principle and combining the construction risk from low to high.
2. The method for optimizing fracturing parameters according to claim 1, wherein the step S500 is specifically:
according to the fracturing parameters of the fractured well, the range of the fracturing parameters of the fractured well is counted, three 3-level factors are determined between the minimum value and the maximum value of each fracturing parameter, a scheme of construction discharge capacity, a pad fluid ratio, an average sand ratio and sand adding strength of the fracturing parameters of the fractured well is formed, the scheme is an experimental scheme of three 3-level factors of four factors in total, and L is established9(34) Preferred orthogonal experiments preferred tables;
and matching the collected reservoir effective thickness, porosity, gas saturation, compensation neutrons and compensation density parameters of the well to be fractured, and combining a preferred orthogonal experiment preferred table to form a candidate scheme of the fracturing parameters of the well to be fractured.
3. The method for optimizing fracturing parameters according to claim 2, wherein the step S600 is specifically:
determining a single-factor evaluation matrix R of each parameter by applying a formula (6) according to the effective thickness, porosity, gas saturation, compensation neutrons, compensation density parameters and fracturing parameter optimization orthogonal experiment optimization table combination of the well to be fractured;
according to the single-factor evaluation matrix R, a matrix product summation algorithm is adopted to compound the fuzzy weight vector A and the single-factor fuzzy evaluation matrix R, the contribution of all parameters is comprehensively considered to obtain a fuzzy comprehensive evaluation matrix B,
4. the method for optimizing fracturing parameters according to claim 3, wherein the step S700 is specifically:
converting the fuzzy comprehensive evaluation matrix of each alternative scheme into a fuzzy comprehensive evaluation score according to a maximum membership principle, wherein the higher the score is, the better the yield increasing effect of the alternative scheme is after implementation is shown; when fuzzy comprehensive scores of different alternatives are the same, comprehensively considering the fracturing risk and the operation cost to determine a final optimal scheme;
the calculation formula is as follows
Wherein: v. ofI、vII、vIII、vIVThe scores corresponding to the evaluation grades were 100, 75, 50, and 25, respectively.
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| CN108442911A (en) * | 2018-02-28 | 2018-08-24 | 西南石油大学 | A kind of shale gas horizontal well refracturing hydraulic fracture method for optimally designing parameters |
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| CN109209321B (en) * | 2018-09-03 | 2020-01-21 | 中国石油大学(华东) | Fracturing potential-based horizontal well fracturing design method and device to be fractured |
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| CN109815542B (en) * | 2018-12-21 | 2023-04-18 | 东软集团股份有限公司 | Fracturing sand plugging identification method and device, storage medium and electronic equipment |
| CN111894545B (en) * | 2019-05-06 | 2022-07-22 | 中国石油化工股份有限公司 | Method for determining proppant pumping scheme |
| CN110955985A (en) * | 2019-12-19 | 2020-04-03 | 长江大学 | Method and device for optimizing fracturing construction parameters and readable storage medium |
| CN113833445B (en) * | 2020-06-08 | 2023-10-27 | 中国石油天然气股份有限公司 | Fracture reservoir reconstruction process |
| CN113833446B (en) * | 2020-06-23 | 2023-08-22 | 中国石油天然气股份有限公司 | Fracturing effect evaluation method and device |
| CN112529443B (en) * | 2020-12-18 | 2024-05-28 | 河南理工大学 | Coalbed methane development target area optimization method based on fuzzy pattern recognition |
| CN113033114B (en) * | 2021-03-04 | 2022-02-11 | 中国石油大学(北京) | Optimization method for mineral kinetic parameters in reservoir water rock reaction simulation |
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