CN109490983B - reservoir geomechanical parameter automatic fitting method and system - Google Patents
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Abstract
The invention relates to a reservoir geomechanical parameter automatic fitting method and a system, comprising the following steps: generating a random field about the mechanical parameters by a Karhunen-Loeve expansion method; guiding the random field into an Abaqus finite element simulator for reservoir stratum crustal stress calculation; adjusting random number in a Karhunen-Loeve algorithm through an optimization algorithm to realize fitting of a formation stress simulation result and an actual observed value in an error range; and obtaining a more accurate geomechanical parameter field by using the random number optimized by the genetic algorithm. The method has the advantages that the generation of a random field is realized by using a Karhunen-Loeve expansion method, stress distribution under stratum conditions is simulated by using Abaqus software, geomechanical parameters fitted by an optimization algorithm are used, and a method for automatically fitting the geomechanical parameters is established.
Description
Technical Field
The invention relates to an automatic fitting method, in particular to an automatic fitting method and system for reservoir geomechanical parameters.
background
Due to the heterogeneity of the underground reservoir rock, the same reservoir has different mechanical properties and different ground stress distribution along with different positions. For the production of oil and gas reservoirs, mechanical parameters show high anisotropism and anisotropy in space, and a traditional homogeneous model has great deviation from an actual stratum. If the distribution of the mechanical parameters of the reservoir can be accurately described, the method is not only beneficial to finding the geological dessert area, but also has important significance for guiding fracturing construction in the later period.
disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a reservoir geomechanical parameter automatic fitting method and a reservoir geomechanical parameter automatic fitting system, and solves the problem that the traditional homogeneous model has great deviation with the actual stratum.
The purpose of the invention is realized by the following technical scheme: a reservoir geomechanical parameter automatic fitting method comprises the following steps:
generating a random field about the mechanical parameters by a Karhunen-Loeve expansion method;
guiding the random field into an Abaqus simulator to simulate the reservoir crustal stress;
and fitting the simulation result and the actual numerical value within an allowable error range through an optimization algorithm, so as to obtain an optimal geomechanical parameter field.
Before the step of generating the random field about the mechanical parameters by the Karhunen-Loeve expansion method, the step of deriving the Karhunen-Loeve expansion method needs to be completed.
after the step of the derivation process of the Karhunen-Loeve expansion method is completed and before the step of generating the random field about the mechanical parameters by the mechanical parameters obtained by the Karhunen-Loeve expansion method is performed, a related program for writing the Karhunen-Loeve expansion method according to the derivation implementation process needs to be completed.
the specific content of the step of generating the random field related to the mechanical parameters by the Karhunen-Loeve expansion method is as follows:
generating N independent Gaussian random variables xii(θ)；
Calculating an integral equation of the covariance matrix to obtain an eigenvalue lambdanand a characteristic function fn；
The characteristic value lambda is measurednAnd a characteristic function fnand gaussian random variable ξiSubstitution of (theta)taking N-order truncation to realize generation of a primary random field;
repeating the third step for P times to realize the generation of the random field for P times;
after P times of generation of random field is realized, according to random variable xii(θ) the following matrix is obtained:
the random variable xii(theta) satisfies the standard normal distribution and satisfies E [ xi ]i(θ)]＝0；E[ξi(θ)ξj(θ)]＝δijWherein, deltaijIs a Kronecker-delta function.
the Abaqus simulator comprises a pore elastic finite element simulator; the specific contents of the step of guiding the random field into an Abaqus simulator to simulate the reservoir ground stress are as follows:
introducing a mechanical property random field generated by a Karhunen-Loeve expansion method into an Abaqus simulator;
Endowing a strain boundary condition in the horizontal direction and a vertical stress boundary condition from an overburden layer to the pore elastic mechanics model in an Abaqus simulator;
The stress tensor distribution values for the random field are obtained.
the optimization algorithm comprises a genetic algorithm; the specific steps of the genetic algorithm for realizing the fitting of the simulation result and the actual numerical value within the error range are as follows:
setting parameters: the number of populations per generation in the genetic algorithm is determined. The population number is 8-15% of the total number of all grids; a stop condition for the genetic algorithm is determined. Stopping the algorithm when the II type norm of the difference value of the two adjacent generations of random numbers is less than 0.001; setting the retention of elite in the genetic algorithm and the mutation probability to be 0.1 and 0.02;
and (3) encoding: before searching, expressing solution data (random array) of a solution space into genotype string structure data of a genetic space, wherein different combinations of the string structure data form different points;
and a fitness evaluation step: the fitness evaluation is used for indicating the advantages and disadvantages of the individual or the solution, and for the research problem, a fitness function is the difference between the simulated stress result and the actual observed value;
selecting: selecting excellent individuals from the group as parents for breeding descendants for the next generation;
a crossing step: obtaining a new generation of individuals with combined characteristics of the parents through crossing;
A mutation step: randomly selecting an individual from the group, and randomly changing the value of a certain string in the string structure data by the selected individual to realize the data mutation.
The principle of selecting good individuals in the selection step is to contribute one or more high-adaptability individuals with high offspring probability to the next generation.
A reservoir geomechanical parameter automatic fitting system comprises a derivation programming module, a random field generation module, a simulation module and an optimization fitting module; the derivation becomes a module for realizing the implementation process of deriving the Karhunen-Loeve expansion method and carrying out corresponding programming according to the implementation process; the random field generation module generates a random field related to the mechanical parameters from the obtained mechanical parameters by a Karhunen-Loeve expansion method; the simulation module is used for guiding a random field into an Abaqus simulator to simulate the reservoir ground stress; the optimization fitting module realizes fitting of a simulated stress result and an actual observed value in an error range through an optimization algorithm.
The simulation module comprises an importing unit and a condition endowing unit; the leading-in unit leads the mechanical property random field generated by the Karhunen-Loeve expansion method into an Abaqus simulator; the condition endowing unit endows a strain boundary condition in the horizontal direction and a vertical stress boundary condition from an overburden layer to the random field in an Abaqus simulator, and obtains a stress distribution value related to the random field.
The optimization fitting module comprises parameter setting, a coding unit, an initial population generating unit, an evaluating unit, a selective crossing unit and a variation unit;
the encoding unit realizes that before search, solution data (random array) of a solution space is expressed into genotype string structure data of a genetic space, and different combinations of the string structure data form different points; the fitness evaluation is used for indicating the advantages and disadvantages of the individual or the solution, and for the research problem, a fitness function is the difference between the simulated stress result and the actual observed value; the selective crossing unit is used for selecting excellent individuals from the group as parents for breeding descendants for the next generation, and obtaining a new generation of individuals combined with the characteristics of the parents through crossing; the mutation unit randomly selects an individual from the group, and randomly changes the value of a certain string in the string structure data of the selected individual to realize the mutation of the data.
The invention has the beneficial effects that: a reservoir geomechanical parameter automatic fitting method and a system thereof utilize a Karhunen-Loeve expansion method to realize the expansion of a random field, utilize Abaqus software to simulate stress distribution under stratum conditions, utilize an optimization algorithm to fit accurate geomechanical parameters, and establish a geomechanical parameter automatic fitting method.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a graph showing accurate Young's modulus parameters;
FIG. 3 is a schematic diagram of a generation 1 Young's modulus parameter field generated using the Karhunen-Loeve expansion method;
FIG. 4 is a schematic diagram of a 25 th generation Young's modulus parameter field generated using the Karhunen-Loeve expansion method;
FIG. 5 is a schematic diagram of an accurate stress distribution using Abaqus software;
FIG. 6 is a schematic diagram of simulation of the stress distribution of the 1 st generation model using Abaqus software;
Fig. 7 uses the Abaqus software to simulate the stress distribution profile in the model generation 25.
Detailed Description
the invention will be further described with reference to the accompanying drawings, but the scope of the invention is not limited to the following.
As shown in fig. 1, a method for automatically fitting reservoir geomechanical parameters includes the following steps:
S1, generating a random field related to the mechanical parameters from the obtained mechanical parameters by a Karhunen-Loeve expansion method;
S2, guiding the random field into an Abaqus simulator to simulate the reservoir ground stress;
And S3, fitting the simulation result and the actual value within an allowable error range through an optimization algorithm.
Before the step of generating the random field about the mechanical parameters by the Karhunen-Loeve expansion method, the step of deriving the Karhunen-Loeve expansion method needs to be completed.
After the step of the derivation process of the Karhunen-Loeve expansion method is completed and before the step of generating the random field about the mechanical parameters by the mechanical parameters obtained by the Karhunen-Loeve expansion method is performed, a related program for writing the Karhunen-Loeve expansion method according to the derivation implementation process needs to be completed.
The specific content of the step of generating the random field related to the mechanical parameters by the Karhunen-Loeve expansion method is as follows:
S11, generating N independent Gaussian random variables xii(θ)；
s12, calculating an integral equation of the covariance matrix to obtain an eigenvalue lambdanAnd a characteristic function fn；
S13, converting the characteristic value lambdanAnd a characteristic function fnAnd gaussian random variable ξisubstitution of (theta) Taking N-order truncationrealizing the generation of a primary random field;
s14, repeating the third step for P times to realize the generation of random fields for P times;
s15, after P times of generation of random field is realized, according to the random variable xii(θ) the following matrix is obtained:
the random variable xii(theta) satisfies the standard normal distribution and satisfies E [ xi ]i(θ)]＝0；E[ξi(θ)ξj(θ)]＝δijWherein, deltaijis a Kronecker-delta function.
the Abaqus simulator comprises a pore elastic finite element simulator; the specific contents of the step of guiding the random field into an Abaqus simulator to simulate the reservoir ground stress are as follows:
s21, introducing the mechanical property random field generated by the Karhunen-Loeve expansion method into an Abaqus simulator;
S22, endowing a boundary condition and an initial stress condition to the random field in an Abaqus simulator;
And S23, obtaining a stress distribution value about the random field.
The optimization algorithm comprises a genetic algorithm; the specific steps of the genetic algorithm for realizing the fitting of the simulation result and the actual numerical value within the error range are as follows:
And (3) encoding: before search, GA expresses solution data of a solution space into genotype string structure data of a genetic space, and different combinations of the string structure data form different points;
Generating an initial population: randomly generating M initial string structure data, wherein each string structure data is called an individual, and M individuals form a group; wherein, the GA starts to evolve by taking the M strings of structure data as initial points;
and a fitness evaluation step: the fitness evaluation is used for indicating the advantages and disadvantages of individuals or solutions, and different definition modes of the fitness function are realized for different problems;
Selecting: selecting excellent individuals from the group as parents for breeding descendants for the next generation;
a crossing step: obtaining a new generation of individuals with combined characteristics of the parents through crossing;
A mutation step: randomly selecting an individual from the group, and randomly changing the value of a certain string in the string structure data by the selected individual to realize the data mutation.
among them, the probability of occurrence of the variation in GA is low, and thus the values are generally small.
The principle of selecting good individuals in the selection step is to contribute one or more high-adaptability individuals with high offspring probability (maximum probability of entering) to the next generation.
A reservoir geomechanical parameter automatic fitting system comprises a derivation programming module, a random field generation module, a simulation module and an optimization fitting module; the derivation becomes a module for realizing the implementation process of deriving the Karhunen-Loeve expansion method and carrying out corresponding programming according to the implementation process; the random field generation module generates a random field related to the mechanical parameters from the obtained mechanical parameters by a Karhunen-Loeve expansion method; the simulation module is used for guiding a random field into an Abaqus simulator to simulate the reservoir ground stress; the optimization fitting module realizes fitting of a simulation result and an actual numerical value within an error range through an optimization algorithm.
The simulation module comprises an importing unit and a condition endowing unit; the leading-in unit leads the mechanical property random field generated by the Karhunen-Loeve expansion method into an Abaqus simulator; the condition endowing unit endows the random field with boundary conditions and initial stress conditions in an Abaqus simulator and obtains a stress distribution value related to the random field.
the optimization fitting module comprises a coding unit, an initial population generating unit, an evaluating unit, a selective crossing unit and a variation unit;
The encoding unit realizes that the solution data of the solution space is expressed into genotype string structure data of a genetic space before the GA searches, and different combinations of the string structure data form different points; the initial population generating unit randomly generates M initial string structure data, each string structure data is called an individual, the M individuals form a population, and the GA starts to evolve by taking the M string structure data as initial points; the evaluation unit can be used for evaluating the fitness to show the advantages and disadvantages of individuals or solutions, and realizing different definition modes of the fitness function for different problems; the selective crossing unit is used for selecting excellent individuals from the group as parents for breeding descendants for the next generation, and obtaining a new generation of individuals combined with the characteristics of the parents through crossing; the mutation unit randomly selects an individual from the group, and randomly changes the value of a certain string in the string structure data of the selected individual to realize the mutation of the data.
Preferably, as shown in fig. 2-7, the method for automatically fitting the reservoir geomechanical parameters according to the present invention is described by taking the example of simulating the young's modulus distribution of a small-scale theoretical model. The basic parameters are as follows:
the mean value of the Young modulus parameter random field is 5; the variance is 1; size of theoretical model: 13 x 3; the truncation term N in the Karhunen-Loeve expansion method is 20; each generation of population contains 20 individuals; in total, 25 individuals were generated.
The effect of the fitting of the invention is evaluated by taking the accurate Young modulus parameter field as a reference field, and as can be seen from FIGS. 2 and 3, the Young modulus distribution of the 1 st generation Young modulus random field and the reference field has great deviation, and the randomness is fully considered; from fig. 4, it can be found that fig. 2 and fig. 4 have higher similarity, which illustrates that after 25 generations of genetic algorithm optimization selection, the automatically fitted young modulus parameter field can more accurately characterize the reference field. Further observing the stress distribution of the model, the fact that a graph 6 obtained by simulating the stress distribution of the model of the 1 st generation by utilizing Abaqus software has great deviation from an accurate stress distribution graph 5 of the model can be found, and after 25 generations of automatic fitting, a model stress distribution schematic diagram 7 obtained has high similarity to the accurate stress distribution graph 5, and the method can accurately and quickly realize the automatic fitting of the model parameter field. And in the aspect of reservoir geological simulation, a reservoir geomechanical parameter field can be accurately and automatically fitted, the operation is simple, the implementation speed is high, and the fitting precision is high.
Although the present invention has been described with reference to the above embodiments, it should be understood that the present invention is not limited to the above embodiments, and those skilled in the art can make various changes and modifications without departing from the scope of the present invention.
Claims (9)
1. A reservoir geomechanical parameter automatic fitting method is characterized in that: it comprises the following steps:
Generating a random field about the mechanical parameters by a Karhunen-Loeve expansion method;
Guiding the random field into an Abaqus simulator to simulate the reservoir crustal stress;
fitting the simulation result and the actual numerical value within an allowable error range through an optimization algorithm;
the Abaqus simulator comprises a pore elastic finite element simulator; the specific contents of the step of guiding the random field into an Abaqus simulator to simulate the reservoir ground stress are as follows:
Introducing a mechanical property random field generated by a Karhunen-Loeve expansion method into an Abaqus simulator;
endowing a strain boundary condition in the horizontal direction and a vertical stress boundary condition from an overburden layer to the pore elastic mechanics model in an Abaqus simulator;
The stress tensor distribution values for the random field are obtained.
2. The method for automatically fitting reservoir geomechanical parameters of claim 1, wherein: the derivation of the Karhunen-Loeve expansion is also required before the step of generating random fields for mechanical parameters by Karhunen-Loeve expansion is performed.
3. the method for automatically fitting reservoir geomechanical parameters of claim 2, wherein: after completing the Karhunen-
After the derivation procedure of the Loeve expansion method and before the step of generating the random field about the mechanical parameters from the obtained mechanical parameters by using the Karhunen-Loeve expansion method, it is necessary to complete the related procedures of writing the Karhunen-Loeve expansion method according to the derivation implementation procedure.
4. The method for automatically fitting reservoir geomechanical parameters of claim 2, wherein: the specific content of the step of generating the random field related to the mechanical parameters by using the Karhunen-Loeve expansion method is as follows:
Calculating an integral equation of the covariance matrix to obtain an eigenvalue lambdanand a characteristic function fn；
Generating M independent Gaussian random variables epsiloni(θ), the length M is determined by: firstly, all characteristic values are arranged in a descending order; making the sum of the first M eigenvalues greater than 80% of the sum of all eigenvalues;
the characteristic value lambda is measurednAnd a characteristic function fnand a Gaussian random variable εisubstitution of (theta)
taking M-order truncation to realize generation of a primary random field;
Repeating the third step for P times to realize the generation of the random field for P times;
After P times of generation of random field is realized, according to random variable xii(θ) the following matrix is obtained:
5. the method for automatically fitting reservoir geomechanical parameters of claim 4, wherein: the random variable epsiloni(theta) satisfies the standard normal distribution and satisfies E [ epsilon ]i(θ)]＝0；E[εi(θ)εj(θ)]＝δijwherein, deltaijIs a Kronecker-delta function.
6. the method for automatically fitting reservoir geomechanical parameters of claim 1, wherein: the optimization algorithm comprises a genetic algorithm; the specific steps of the genetic algorithm for realizing the fitting of the simulation result and the actual observation value in the error range and updating the random number are as follows: setting parameters: determining the population number of each generation in the genetic algorithm, wherein the population number is 8-15% of the total number of all grids; determining a stopping condition of the genetic algorithm, and stopping the algorithm when the II-type norm of the difference value of two adjacent generations of random numbers is less than 0.001; setting the retention of elite in the genetic algorithm and the mutation probability to be 0.1 and 0.02;
and (3) encoding: before searching, expressing solution data of a solution space into genotype string structure data of a genetic space, wherein different combinations of the string structure data form different points;
And a fitness evaluation step: the fitness evaluation is used for indicating the advantages and disadvantages of the individual or the solution, and for the research problem, a fitness function is the difference between the simulated stress result and the actual observed value;
selecting: selecting excellent individuals from the group as parents for breeding descendants for the next generation;
A crossing step: obtaining a new generation of individuals with combined characteristics of the parents through crossing;
A mutation step: randomly selecting an individual from the group, and randomly changing the value of a certain string in the string structure data by the selected individual to realize the data mutation.
7. a reservoir geomechanical parameters auto-fitting system according to any one of claims 1 to 6, wherein: the device comprises a derivation programming module, a random field generation module, a simulation module and an optimization fitting module; the derivation programming module realizes the implementation process of deriving the Karhunen-Loeve expansion method and carries out corresponding programming according to the implementation process; the random field generation module generates a random field related to mechanical parameters by a Karhunen-Loeve expansion method; the simulation module is used for guiding a random field into an Abaqus simulator to simulate the reservoir ground stress; the optimization fitting module realizes fitting of a simulation result and an actual numerical value within an error range through an optimization algorithm, so that an optimal geomechanical parameter field is obtained.
8. A reservoir geomechanical parameters auto-fitting system of claim 7, wherein: the simulation module comprises an importing unit and a condition endowing unit; the leading-in unit leads the mechanical property random field generated by the Karhunen-Loeve expansion method into an Abaqus simulator; the condition endowing unit endows the random field with horizontal strain and vertical stress boundary conditions in an Abaqus simulator and obtains a stress distribution value related to the random field.
9. A reservoir geomechanical parameters auto-fitting system of claim 7, wherein: the optimization fitting module comprises an initial parameter setting unit, a coding unit, an elite unit, an evaluation unit, a cross selection unit and a variation unit;
setting the initial parameters to determine the population of each generation in the genetic algorithm, wherein the population is 8-15% of the total number of all grids; determining a stopping condition of the genetic algorithm, and stopping the algorithm when the II-type norm of the difference value of two adjacent generations of random numbers is less than 0.001; setting the retention of elite in the genetic algorithm and the mutation probability to be 0.1 and 0.02;
The encoding unit realizes that the solution data of the solution space is expressed into genotype string structure data of a genetic space before the GA searches, and different combinations of the string structure data form different points; the fitness evaluation is used for indicating the advantages and disadvantages of the individual or the solution, and for the research problem, a fitness function is the difference between the simulated stress result and the actual observed value; the selective crossing unit is used for selecting excellent individuals from the group as parents for breeding descendants for the next generation, and obtaining a new generation of individuals combined with the characteristics of the parents through crossing; the mutation unit randomly selects an individual from the group, and randomly changes the value of a certain string in the string structure data of the selected individual to realize the mutation of the data.
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