CN107884824A - A kind of improvement particle cluster algorithm of earthquake data before superposition elastic parameter inversion problem - Google Patents

A kind of improvement particle cluster algorithm of earthquake data before superposition elastic parameter inversion problem Download PDF

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CN107884824A
CN107884824A CN201711013493.2A CN201711013493A CN107884824A CN 107884824 A CN107884824 A CN 107884824A CN 201711013493 A CN201711013493 A CN 201711013493A CN 107884824 A CN107884824 A CN 107884824A
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颜雪松
朱志新
伍庆华
王丽萍
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China University of Geosciences
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/30—Analysis
    • G01V1/303—Analysis for determining velocity profiles or travel times
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/30—Analysis
    • G01V1/306—Analysis for determining physical properties of the subsurface, e.g. impedance, porosity or attenuation profiles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/50—Corrections or adjustments related to wave propagation
    • G01V2210/51—Migration
    • G01V2210/512—Pre-stack
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—Analysis
    • G01V2210/62—Physical property of subsurface
    • G01V2210/622—Velocity, density or impedance
    • G01V2210/6222—Velocity; travel time
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—Analysis
    • G01V2210/62—Physical property of subsurface
    • G01V2210/622—Velocity, density or impedance
    • G01V2210/6224—Density

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Abstract

The invention discloses a kind of improvement particle cluster algorithm of earthquake data before superposition elastic parameter inversion problem, this method is improved traditional particle swarm optimization algorithm, when carrying out initialization operation to elastic parameter, the scope of first group of elastic parameter is constrained with improvement strategy, it is set to be comparatively close to actual value, other group of elastic parameter is constrained using the scope of difference, can lift the precision of inverting.This method improved procedure is simply efficient, and algorithm is simple；Global and local search ability is stronger, and exploration efficiency is higher, and time-consuming less, solution efficiency is higher；Traditional three parameter inversion problems are directed to, transverse wave speed, longitudinal wave velocity, density parameter isoinversion are preferable；In refutation process, the amplitude seismic data and actual amplitude geological data that are finally inversed by extremely are fitted, while the coefficient correlation between elastic parameter is very high.

Description

Improved particle swarm algorithm for prestack seismic data elastic parameter inversion problem
Technical Field
The invention relates to an improved particle swarm algorithm for the inversion problem of elastic parameters of pre-stack seismic data, which is a method for oil exploration by using seismic information and belongs to the technical field of seismic data inversion in oil-gas geophysical exploration.
Background
At present, seismic exploration is a method for oil exploration by using seismic information, and the method can be used for predicting reservoir parameters because the seismic information can reflect the variation trend of the reservoir parameters. The seismic data are divided into pre-stack seismic data and post-stack seismic data, and the pre-stack seismic data contain more fluid information than the post-stack seismic data, and the pre-stack inversion method has the obvious advantages of stable result, high resolution, strong controllability and the like. While intelligent algorithms are one of the main methods to solve the problems in the field of geophysical inversion, they also face some difficulties in geophysical nonlinear inversion. Firstly, the intelligent algorithms have some defects, and the genetic algorithm is good at the very strong global search capability, but has the problems of poor local search capability, easy precocity and the like; the particle swarm optimization algorithm mainly has the problems that premature convergence is easy to generate (especially in the process of complex multi-peak search problems), local optimization capability is poor, and the like, and is trapped in local minimum, mainly due to the loss of diversity of a swarm in a search space. Secondly, when the intelligent algorithms are used for carrying out nonlinear inverse problem research, the problems of low calculation efficiency and the like often exist in the process of searching solutions. When the genetic algorithm enters the later stage of the algorithm, the search efficiency is reduced and the time consumption is high due to poor local search efficiency. The search efficiency of the algorithm is closely related to the solving efficiency of the inverse problem, the search efficiency is low, and the solving efficiency of the problem is inevitably low.
For the conventional three-parameter inversion problem, the two terms of the shear wave velocity and the longitudinal wave velocity are usually very good in inversion, but the density term is very poor, which is a problem to be solved urgently. In the inversion process, the inverted amplitude seismic data and the actual amplitude seismic data are fit, but the inverted elastic parameters and the actual elastic parameters have large errors, that is, the correlation coefficient between the elastic parameters is low, which is also a problem to be solved urgently.
Disclosure of Invention
The invention aims to solve the traditional three-parameter inversion problem, improve the solving efficiency of the inversion problem and provide a more efficient improved particle swarm algorithm for the pre-stack seismic data parameter inversion problem.
In order to achieve the purpose, the invention adopts the technical scheme that: an improved particle swarm algorithm for the prestack seismic data elastic parameter inversion problem comprises the following steps:
step S1: sampling a plurality of points in the well logging of the underground reservoir of the oil field, acquiring a plurality of groups of elastic parameters, and measuring a plurality of groups of seismic record values corresponding to the sampling points;
step S2: initializing the sets of elastic parameters obtained in step S1 to obtain a value range, and randomly selecting the sets of elastic parameters within the value range;
step S3: measuring a plurality of groups of corresponding seismic record values by utilizing the plurality of groups of randomly selected elastic parameters obtained in the step S2;
step S4: based on the inversion objective function, comparing the seismic record value obtained in the step S3 with the seismic record value in the step S1 to obtain an inversion objective function value, if the inversion objective function value is smaller than the objective function preset value, stopping calculation, and outputting the elastic parameter and the corresponding seismic record value at the moment, otherwise, entering the step S5;
step S5: and (4) performing iterative operation on the elastic parameters obtained in the step (S2), updating the numerical values of the elastic parameters, increasing the iteration times once, stopping calculation if the iteration times exceed the maximum iteration times, and outputting the elastic parameters and the corresponding seismic record values at the moment, otherwise, returning to the step (S3).
Further, each set of the elastic parameters is a longitudinal wave velocity VpTransverse wave velocity VsAnd a density ρ.
Further, in the step S1, logging is performed on n +1 sampling points of the oil field underground reservoir, and n +1 sets of elastic parameters are obtained:
[Vpi，Vsi，ρi](where i ═ 1,2 …, n + 1);
and (3) measuring to obtain m groups of seismic record values:
[s(θij)](where i is 1,2 …, n; j is 1,2 … m)
Wherein, Vpi，Vsi，ρiThe longitudinal wave velocity, the transverse wave velocity and the density [ s (theta) ] in the i-th group of elastic parametersij)]And the seismic record value corresponding to the j angle in the ith layer of sampling points is shown, and theta is an angle.
Further, each particle in the improved particle swarm optimization algorithm is a real number type one-dimensional array with a length of 3n +3, and the value range obtained by the initialization operation in the step S2 is determined according to 3n +3 elastic parameters in the n +1 sets obtained by logging in the step S1.
Further, the value range obtained by the initialization operation in step S2 is constrained as follows: the values of the first set of three elastic parameters are chosen randomly with the following constraints:
0.9·Vp1well≤Vp1≤1.1·Vp1well
0.9·Vs1well≤Vs1≤1.1·Vs1well
0.95·ρ1well≤ρ1≤1.05·ρ1well
the values of the three elastic parameters from the second group to the nth group are randomly selected according to the following constraints:
wherein, VpiwellIs the velocity of longitudinal wave, Vs, of the logging ith layer in step S1iwellThe transverse wave velocity, rho, of the logging ith layer in the step S1iwellIs the density, Vp, of the i-th zone of the log in step S1iLongitudinal wave velocity of i-th layer, Δ Vp, generated for initializationiThe difference value of longitudinal wave velocity of the ith layer and the (i +1) th layer, Vs, generated for initializationiShear wave velocity, Δ Vs, of the i-th layer generated for initializationiThe difference of the transverse wave speed of the ith layer and the (i +1) th layer, rho, generated for initializationiDensity of i-th layer, Δ ρ, generated for initializationiThe density difference between the ith layer and the (i +1) th layer is generated for initialization.
Further, the step S3 of measuring the seismic record value corresponding to the elastic parameter includes the following steps:
step 1: use Aki&Recard approximation equation calculating reflectance RppThe expression is as follows:
wherein, is Δ Vp，ΔVsAnd Δ ρ represents upper and lower two V layers, respectivelyp、VsAnd the difference between the p and the p,andrepresents upper and lower layers Vp、VsAnd p, theta is the angle,
step 2: acquiring a Rake wavelet, wherein the expression is as follows:
wherein, VmThe frequency is the main frequency, t is the time, and the frequency can be manually set;
and step 3: convolution calculation is carried out on the reflection coefficient and the Rake wavelets, and the expression is as follows:
s(θ)＝Rpp(θ)*f(t)+n(t)
wherein R ispp(theta) is the reflection coefficient function, f (t) is the seismic wavelet, and n (t) is the noise.
Further, in the step S4, the inverse objective function is established, and the expression is as follows:
wherein, s (θ)i,j) Is the seismic record value, S' (θ), obtained in step S1i,j) The seismic record value obtained in step S3.
Further, in step S5, the elastic parameter is iteratively calculated, and in each iteration, the value of the elastic parameter of each particle is updated through the individual extremum and the global extremum or the local extremum, and the updating is performed according to the following formula:
Vmin＝-Vmax
wherein,is the velocity in the jth dimension of the ith particle, w is the inertial weight,is the position of the ith particle in the jth dimension,is the extreme value of the individual,is a global extremum, rand () is a random number between 0 and 1, c1、c2The learning factors Vmax and Vmin respectively correspond to the maximum value and the minimum value of the movement distance of the ith particle in the jth dimension.
Further, said c1、c2The values of (a) and (b) are all 2, the value of w is 0.5, the number of particles is 40, and the maximum iteration number is 5000.
Compared with the prior art, the method has the beneficial effects that:
1. the global and local search capability is strong, the exploration efficiency is high, the time consumption is low, and the solution efficiency is high;
2. for the traditional three-parameter inversion problem, the inversion of the shear wave velocity, the longitudinal wave velocity, the density parameter and the like is good;
3. in the inversion process, inverted amplitude seismic data and actual amplitude seismic data are quite fitted, and meanwhile, the error between the inverted elastic parameters and the actual elastic parameters is small, namely the correlation coefficient between the elastic parameters is high;
4. the particle swarm optimization algorithm is improved without adopting the traditional mixed other algorithms, the improvement mode is simple and efficient, the algorithm is simple, and the understanding is easy.
Drawings
FIG. 1 is an inversion flow chart of an improved particle swarm algorithm for the prestack seismic data parameter inversion problem of the present invention;
FIG. 2 is a seismic record of the present invention for eight different angles;
FIG. 3 is a comparison graph of the average correlation coefficients of three elastic parameters obtained by the three algorithm experiments according to the present invention;
FIG. 4 is a diagram of inversion target functions obtained by three algorithm experiments according to the present invention.
Detailed Description
To further facilitate a better understanding of the nature of the present invention to those skilled in the art, the present invention is described in further detail below with reference to the accompanying drawings and examples:
as shown in FIG. 1, the invention provides an improved particle swarm algorithm for the pre-stack seismic data elastic parameter inversion problem, which comprises the following steps:
step S1: sampling a plurality of points in the well logging of the underground reservoir of the oil field, acquiring a plurality of groups of elastic parameters, and measuring a plurality of groups of seismic record values corresponding to the sampling points;
step S2: initializing the sets of elastic parameters obtained in step S1 to obtain a value range, and randomly selecting the sets of elastic parameters within the value range;
step S3: measuring a plurality of groups of corresponding seismic record values by utilizing the plurality of groups of randomly selected elastic parameters obtained in the step S2;
step S4: based on the inversion objective function, comparing the seismic record value obtained in the step S3 with the seismic record value in the step S1 to obtain an inversion objective function value, if the inversion objective function value is smaller than the objective function preset value, stopping calculation, and outputting the elastic parameter and the corresponding seismic record value at the moment, otherwise, entering the step S5;
step S5: and (4) performing iterative operation on the elastic parameters obtained in the step (S2), updating the numerical values of the elastic parameters, increasing the iteration times once, stopping calculation if the iteration times exceed the maximum iteration times, and outputting the elastic parameters and the corresponding seismic record values at the moment, otherwise, returning to the step (S3).
Specifically, each set of elastic parameters is the longitudinal wave velocity VpTransverse wave velocity VsAnd a density ρ. Assuming that n sampling points are provided, each sampling point is taken as one layer, that is, there are n layers, and the elastic parameter of the solution model is 3 × n, the corresponding individual encoding mode can be represented by formula (1).
Gi＝(Vp1,Vs1,ρ1,Vp2,Vs2,ρ2,…,Vpn,Vsn,ρn),n＝1,2,…,241 (1)
In a population space, a population individual (particle) is designed by adopting a traditional real number code, the population individual is initialized by a random initialization method within a certain range, and each particle is composed of a group of real numbers. Assume a population size of N particles, where Vpj,Vsj,ρjRepresents an individual GiThe variation range of the values of the three parameters corresponding to the jth sampling point is set according to actual logging data.
In step S2, initializing the sets of elastic parameters obtained in step S1 to obtain value ranges, and the present invention adopts the following strategies for initialization:
the first set of three parameter bound range constraints is shown in equation (2):
0.9·Vp1well≤Vp1≤1.1·Vp1well
0.9·Vs1well≤Vs1≤1.1·Vs1well
0.95·ρ1well≤ρ1≤1.05·ρ1well(2)
the second through nth sets of three parameter constraints are shown in equation (3):
wherein, VpiwellIs the velocity of longitudinal wave, Vs, of the logging ith layer in step S1iwellThe transverse wave velocity, rho, of the logging ith layer in the step S1iwellIs the density, Vp, of the i-th zone of the log in step S1iLongitudinal wave velocity of i-th layer, Δ Vp, generated for initializationiThe difference value of longitudinal wave velocity of the ith layer and the (i +1) th layer, Vs, generated for initializationiShear wave velocity, Δ Vs, of the i-th layer generated for initializationiThe difference of the transverse wave speed of the ith layer and the (i +1) th layer, rho, generated for initializationiDensity of i-th layer, Δ ρ, generated for initializationiThe density difference between the ith layer and the (i +1) th layer is generated for initialization.
The initialization strategy in the present invention is not limited to the above limitation, and may be that the three parameters from the first group to the x-th group are constrained by formula (2), and the three parameters from the x + 1-th group to the n-th group are constrained by formula (3), where x is 1,2 … … n-1. In the present invention, if the well log is 241 layers, that is, 241 sampling points, the seismic log is 240 layers, where the number of particles in the selected particle group is 40.
Establishing an inversion convolution model is one of the main steps of performing prestack AVO (Amplitude variation with offset) elastic parameter inversion, and the inversion convolution model is used for calculating seismic record values. In step S3, the basic steps of measuring the sets of seismic record values of the corresponding sampling points using the randomly selected sets of elastic parameters obtained in step S2 are as follows:
step 1: use Aki&Recard approximation equation calculating reflectance RppThe expression is shown in formula (4):
wherein, is Δ Vp，ΔVsAnd Δ ρ represents upper and lower two V layers, respectivelyp、VsAnd the difference between the p and the p, andrepresents upper and lower layers Vp、VsAnd p, theta is the angle,calculated according to actual data, R can be obtained according to the formulappAs a component of the seismic record convolution operation;
step 2: obtaining a seismic wavelet, wherein the seismic wavelet is another component of a seismic record convolution model, seismic record data are obtained by performing convolution operation on the wavelet and a reflection coefficient, and the seismic record data are suitable for establishing a forward model and manufacturing a synthetic seismic trace record, the invention uses a Rake wavelet which is a seismic wavelet with a zero phase and obtains the Rake wavelet, and an expression is shown in a formula (5):
wherein, VmThe frequency is the main frequency, t is the time, and the frequency can be manually set;
and step 3: convolution calculation is carried out on the reflection coefficient and the Rake wavelets, and an expression is shown as a formula (6):
s(θ)＝Rpp(θ)*f(t)+n(t) (6)
wherein R isppIn the invention, the calculated s (theta) can be used for constructing an inversion target function without considering noise factors.
Aki for emphasizing lithologic parameter variation is used in common midpoint channel concentration before stacking and the reflection coefficient of non-zero offset seismic channel includes information of longitudinal wave, transverse wave and density&Approximate equation for Recard. In use Aki&Calculation of the reflection coefficient R based on the Zoeppritz approximation equation proposed by RecardppIn the process of (1), each RppIs obtained from the elastic parameters of the upper and lower layers, but is mathematically transformed, Aki&The Recard approximation equation may ultimately be represented by the elastic parameter of the upper layer, and the difference between the elastic parameters of the upper and lower layers. The transformation process of equation (4) can be represented by equation (7):
suppose Vp1Relatively close to the true value, Δ VpIs also relatively close to the true value, then Vp2Will also approach the true value, and so on, Vp3,Vp4,...,VpnAll approach the true value, VsThe same is true for ρ. The reflection coefficient R thus calculatedppAnd will be quite accurate, and achieving this effect is clearly a requirement that the first set of three elastic parameters be relatively close to true values.
In step S4, based on the inversion objective function, the seismic record value obtained in step S3 is compared with the seismic record value in step S1, and an inversion objective function value is obtained. The simulation-optimization method is to convert the pre-stack AVO elastic parameter inversion problem into an optimization problem and then solve the optimization problem by using an optimization algorithm. From the optimization perspective, when the difference between the inverted seismic data generated by the optimized elastic parameters and the actual seismic record data is 0 or less than a certain threshold, the elastic parameters are considered to be satisfactory. Because the optimization algorithm can evaluate the quality of an individual according to the fitness function converted by the inversion target function, the quality of the inversion target function constructed aiming at the inversion problem is a main factor influencing the inversion effect of the pre-stack AVO elastic parameters, and the inversion target function in the invention is the fitness function.
In the present invention, Aki is first utilized&Solving R by Recard approximation equationppThe value of (1), i.e. the reflection coefficient of the reflected longitudinal wave, then by the pair RppAnd performing convolution with the wavelets to obtain synthetic seismic record data. And (3) calculating n multiplied by m seismic record data by setting the number of sampling points to be n and requiring m different angles for each sampling point. And finally, performing difference and square on the m groups of seismic record data of each sampling point obtained through optimization and the actual seismic record data, accumulating and summing the obtained data, dividing the accumulated data by m, accumulating the data obtained by n sampling points, dividing the data obtained by n sampling points by n, and obtaining the final result. From the above formula derivation, an inversion objective function can be established as shown in formula (8):
wherein, s (θ)i,j) For forward seismic recording, i.e. the seismic record value, S' (θ) obtained in step S1i,j) The seismic record values obtained in step S3 are inverted.
Due to the reflection coefficient RppIs calculated by the elastic parameter Vp、VsAnd p, and finally calculating to obtain the same set of inverted seismic data consisting of infinite types of Vp、VsAnd ρ are combined. Therefore, there is a case where the inverted seismic data obtained by calculating all of the three parameters with errors is the same as the inverted seismic data obtained by calculating both of the two parameters without errors and one of the parameters with errors. In order to better evaluate the quality of the inversion result of the optimization algorithm on the pre-stack AVO elastic parameters, the invention adopts Pearson product-moment correlation coefficient (also called PPMCC or PCCs) to measure the three parameters of the inversion and the actual three parametersThe established correlation coefficient function is shown in formula (9):
wherein, XiIs a standard value of a certain parameter of three parameters, YiIs the inversion value corresponding to it,respectively, are the average of a set of values.
The solving process of the seismic data is very complex, the smaller the inversion objective function value is, the higher the correlation coefficient of the three parameters is not necessarily, and the higher the correlation coefficient of the three parameters is, the smaller the objective function value is also not necessarily, but when the objective function reaches the theoretical optimal value which is 0, V isp、VsThe correlation coefficients of p and p can reach a theoretical optimal value of 1. Therefore, the invention judges the quality of the inversion result by combining the objective function value and the correlation coefficient. The final objective is to make the objective function value obtained by inversion small and make the correlation coefficient of the three parameters high.
In the invention, a Zoeppritz approximate equation is utilized to forward a model, the number of logging layers is 241 layers, namely, the number of sampling points is 241, seismic records are 240 layers, each sampling point respectively takes eight angles of 0 degrees, 6 degrees, 11 degrees, 17 degrees, 23 degrees, 29 degrees, 34 degrees and 40 degrees, the reflection coefficient of each layer is calculated, the calculated eight reflection coefficient sequences and zero-phase Rake wavelets are subjected to convolution calculation, and a synthetic seismic record is obtained, namely angle gather data is shown in figure 2.
As shown in FIG. 3, for the comparison of the three-parameter average correlation coefficients obtained by the three algorithm experiments, V of Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Improved Particle Swarm Optimization (IPSO) are shown from left to rightp、VsAnd the correlation coefficient of rho, the method can be knownThe three-parameter average correlation coefficients corresponding to the improved particle swarm optimization algorithm applied in the invention are all the highest.
As shown in fig. 4, the inversion objective function graph obtained by the three algorithm experiments is an inversion objective function value graph of each generation of the iteration times from 0 to 5000, that is, the maximum iteration time is set to 5000, and three curves in the graph are inversion objective function value curves corresponding to GA, PSO, and IPSO from top to bottom in sequence.
The method has the advantages of simple and efficient improvement mode and simple algorithm; the global and local search capability is strong, the exploration efficiency is high, the time consumption is low, and the solution efficiency is high; for the traditional three-parameter inversion problem, the inversion of the shear wave velocity, the longitudinal wave velocity, the density parameter and the like is good; in the inversion process, the inverted amplitude seismic data and the actual amplitude seismic data are well fitted, and meanwhile, the correlation coefficient between the elastic parameters is high.
In summary, the present invention has been described in detail, but the scope of the present invention should not be limited thereby. It is contemplated that various modifications, adaptations, and equivalents may be made to the disclosed embodiments without departing from the scope of the invention.

Claims (9)

1. An improved particle swarm algorithm for the prestack seismic data elastic parameter inversion problem is characterized by comprising the following steps:
step S1: sampling a plurality of points in the well logging of the underground reservoir of the oil field, acquiring a plurality of groups of elastic parameters, and measuring a plurality of groups of seismic record values corresponding to the sampling points;
step S2: initializing the sets of elastic parameters obtained in step S1 to obtain a value range, and randomly selecting the sets of elastic parameters within the value range;
step S3: measuring a plurality of groups of corresponding seismic record values by utilizing the plurality of groups of randomly selected elastic parameters obtained in the step S2;
step S4: based on the inversion objective function, comparing the seismic record value obtained in the step S3 with the seismic record value in the step S1 to obtain an inversion objective function value, if the inversion objective function value is smaller than the objective function preset value, stopping calculation, and outputting the elastic parameter and the corresponding seismic record value at the moment, otherwise, entering the step S5;
step S5: and (4) performing iterative operation on the elastic parameters obtained in the step (S2), updating the numerical values of the elastic parameters, increasing the iteration times once, stopping calculation if the iteration times exceed the maximum iteration times, and outputting the elastic parameters and the corresponding seismic record values at the moment, otherwise, returning to the step (S3).
2. The improved particle swarm algorithm for the prestack seismic data elastic parameter inversion problem according to claim 1, wherein: each set of the elastic parameters is longitudinal wave velocity VpTransverse wave velocity VsAnd a density ρ.
3. The improved particle swarm algorithm for the prestack seismic data elastic parameter inversion problem according to claim 1, wherein: in the step S1, n +1 points are sampled in the well logging of the oil field underground reservoir to obtain n +1 groups of elastic parameters:
[Vpi，Vsi，ρi](where i ═ 1,2 …, n + 1);
and (3) measuring to obtain m groups of seismic record values:
[s(θij)](where i is 1,2 …, n; j is 1,2 … m)
Wherein, Vpi，Vsi，ρiThe longitudinal wave velocity, the transverse wave velocity and the density [ s (theta) ] in the i-th group of elastic parametersij)]And the seismic record value corresponding to the j angle in the ith layer of sampling points is shown, and theta is an angle.
4. The improved particle swarm algorithm for the prestack seismic data elastic parameter inversion problem according to claim 1, wherein: each particle in the improved particle swarm optimization algorithm is a real number type one-dimensional array with the length of 3n +3, and the value range obtained by the initialization operation in the step S2 is determined according to 3n +3 elastic parameters in the n +1 groups obtained by logging in the step S1.
5. The improved particle swarm algorithm for the prestack seismic data elastic parameter inversion problem according to claim 4, wherein: the value range obtained by the initialization operation in step S2 is constrained as follows: the values of the first set of three elastic parameters are chosen randomly with the following constraints:
0.9·Vp1well≤Vp1≤1.1·Vp1well
0.9·Vs1well≤Vs1≤1.1·Vs1well
0.95·ρ1well≤ρ1≤1.05·ρ1well
the values of the three elastic parameters from the second group to the nth group are randomly selected according to the following constraints:
<mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>Vp</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>=</mo> <msub> <mi>Vp</mi> <mi>i</mi> </msub> <mo>+</mo> <msub> <mi>&amp;Delta;Vp</mi> <mi>i</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>Vs</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>=</mo> <msub> <mi>Vs</mi> <mi>i</mi> </msub> <mo>+</mo> <msub> <mi>&amp;Delta;Vs</mi> <mi>i</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>&amp;rho;</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>=</mo> <msub> <mi>&amp;rho;</mi> <mi>i</mi> </msub> <mo>+</mo> <msub> <mi>&amp;Delta;&amp;rho;</mi> <mi>i</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>0.8</mn> <mo>&amp;CenterDot;</mo> <mrow> <mo>(</mo> <msub> <mi>Vp</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> <mi>w</mi> <mi>e</mi> <mi>l</mi> <mi>l</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>Vp</mi> <mrow> <mi>i</mi> <mi>w</mi> <mi>e</mi> <mi>l</mi> <mi>l</mi> </mrow> </msub> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msub> <mi>&amp;Delta;Vp</mi> <mi>i</mi> </msub> <mo>&amp;le;</mo> <mn>1.2</mn> <mo>&amp;CenterDot;</mo> <mrow> <mo>(</mo> <msub> <mi>Vp</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> <mi>w</mi> <mi>e</mi> <mi>l</mi> <mi>l</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>Vp</mi> <mrow> <mi>i</mi> <mi>w</mi> <mi>e</mi> <mi>l</mi> <mi>l</mi> </mrow> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>0.8</mn> <mo>&amp;CenterDot;</mo> <mrow> <mo>(</mo> <msub> <mi>Vs</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> <mi>w</mi> <mi>e</mi> <mi>l</mi> <mi>l</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>Vs</mi> <mrow> <mi>i</mi> <mi>w</mi> <mi>e</mi> <mi>l</mi> <mi>l</mi> </mrow> </msub> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msub> <mi>&amp;Delta;Vs</mi> <mi>i</mi> </msub> <mo>&amp;le;</mo> <mn>1.2</mn> <mo>&amp;CenterDot;</mo> <mrow> <mo>(</mo> <msub> <mi>Vs</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> <mi>w</mi> <mi>e</mi> <mi>l</mi> <mi>l</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>Vs</mi> <mrow> <mi>i</mi> <mi>w</mi> <mi>e</mi> <mi>l</mi> <mi>l</mi> </mrow> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>0.9</mn> <mo>&amp;CenterDot;</mo> <mrow> <mo>(</mo> <msub> <mi>&amp;rho;</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> <mi>w</mi> <mi>e</mi> <mi>l</mi> <mi>l</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>&amp;rho;</mi> <mrow> <mi>i</mi> <mi>w</mi> <mi>e</mi> <mi>l</mi> <mi>l</mi> </mrow> </msub> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msub> <mi>&amp;Delta;&amp;rho;</mi> <mi>i</mi> </msub> <mo>&amp;le;</mo> <mn>1.1</mn> <mo>&amp;CenterDot;</mo> <mrow> <mo>(</mo> <msub> <mi>&amp;rho;</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> <mi>w</mi> <mi>e</mi> <mi>l</mi> <mi>l</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>&amp;rho;</mi> <mrow> <mi>i</mi> <mi>w</mi> <mi>e</mi> <mi>l</mi> <mi>l</mi> </mrow> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>n</mi> </mrow>
wherein, VpiwellIs the velocity of longitudinal wave, Vs, of the logging ith layer in step S1iwellThe transverse wave velocity, rho, of the logging ith layer in the step S1iwellIs the density, Vp, of the i-th zone of the log in step S1iLongitudinal wave velocity of i-th layer, Δ Vp, generated for initializationiThe difference value of longitudinal wave velocity of the ith layer and the (i +1) th layer, Vs, generated for initializationiShear wave velocity, Δ Vs, of the i-th layer generated for initializationiThe difference of the transverse wave speed of the ith layer and the (i +1) th layer, rho, generated for initializationiDensity of i-th layer, Δ ρ, generated for initializationiThe density difference between the ith layer and the (i +1) th layer is generated for initialization.
6. The improved particle swarm algorithm for the prestack seismic data elastic parameter inversion problem according to claim 1, wherein: in step S3, the step of measuring the seismic record value corresponding to the elastic parameter includes the following steps:
step 1: use Aki&Recard approximation equation calculating reflectance RppThe expression is as follows:
<mrow> <msub> <mi>R</mi> <mrow> <mi>p</mi> <mi>p</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>&amp;theta;</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <mrow> <mo>(</mo> <mn>1</mn> <mo>+</mo> <msup> <mi>tan</mi> <mn>2</mn> </msup> <mi>&amp;theta;</mi> <mo>)</mo> </mrow> <mfrac> <mrow> <msub> <mi>&amp;Delta;V</mi> <mi>p</mi> </msub> </mrow> <mover> <msub> <mi>V</mi> <mi>p</mi> </msub> <mo>&amp;OverBar;</mo> </mover> </mfrac> <mo>-</mo> <mrow> <mo>(</mo> <mn>4</mn> <msup> <mi>&amp;gamma;</mi> <mn>2</mn> </msup> <msup> <mi>sin</mi> <mn>2</mn> </msup> <mi>&amp;theta;</mi> <mo>)</mo> </mrow> <mfrac> <mrow> <msub> <mi>&amp;Delta;V</mi> <mi>s</mi> </msub> </mrow> <mover> <msub> <mi>V</mi> <mi>s</mi> </msub> <mo>&amp;OverBar;</mo> </mover> </mfrac> <mo>+</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mn>4</mn> <msup> <mi>&amp;gamma;</mi> <mn>2</mn> </msup> <msup> <mi>sin</mi> <mn>2</mn> </msup> <mi>&amp;theta;</mi> <mo>)</mo> </mrow> <mfrac> <mrow> <mi>&amp;Delta;</mi> <mi>&amp;rho;</mi> </mrow> <mover> <mi>&amp;rho;</mi> <mo>&amp;OverBar;</mo> </mover> </mfrac> </mrow>
wherein, is Δ Vp，ΔVsAnd Δ ρ represents upper and lower two V layers, respectivelyp、VsAnd the difference between the p and the p, andrepresents upper and lower layers Vp、VsAverage value of rhoAnd theta is an angle of the angle,
step 2: acquiring a Rake wavelet, wherein the expression is as follows:
<mrow> <mi>f</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mn>2</mn> <msup> <mi>&amp;pi;</mi> <mn>2</mn> </msup> <msup> <msub> <mi>V</mi> <mi>m</mi> </msub> <mn>2</mn> </msup> <msup> <mi>t</mi> <mn>2</mn> </msup> <mo>)</mo> </mrow> <msup> <mi>e</mi> <mrow> <mo>-</mo> <msup> <mi>&amp;pi;</mi> <mn>2</mn> </msup> <msup> <msub> <mi>V</mi> <mi>m</mi> </msub> <mn>2</mn> </msup> <msup> <mi>t</mi> <mn>2</mn> </msup> </mrow> </msup> </mrow>
wherein, VmThe frequency is the main frequency, t is the time, and the frequency can be manually set;
and step 3: convolution calculation is carried out on the reflection coefficient and the Rake wavelets, and the expression is as follows:
s(θ)＝Rpp(θ)*f(t)+n(t)
wherein R ispp(theta) is the reflection coefficient function, f (t) is the seismic wavelet, and n (t) is the noise.
7. The improved particle swarm algorithm for the prestack seismic data elastic parameter inversion problem according to claim 1, wherein: establishing an inversion objective function in step S4, where the expression is as follows:
<mrow> <mi>f</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <msqrt> <mfrac> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>m</mi> </munderover> <msup> <mrow> <mo>(</mo> <mi>s</mi> <mo>(</mo> <msub> <mi>&amp;theta;</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> <mo>)</mo> <mo>-</mo> <msup> <mi>s</mi> <mo>&amp;prime;</mo> </msup> <mo>(</mo> <msub> <mi>&amp;theta;</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> <mo>)</mo> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> <mrow> <mi>n</mi> <mo>&amp;times;</mo> <mi>m</mi> </mrow> </mfrac> </msqrt> </mrow>
wherein, s (θ)i,j) Is the seismic record value, S' (θ), obtained in step S1i,j) The seismic record value obtained in step S3.
8. The improved particle swarm algorithm for the prestack seismic data elastic parameter inversion problem according to claim 1, wherein: in step S5, performing iterative operation on the elastic parameter, where in each iteration, each particle updates the value of the elastic parameter through an individual extremum and a global extremum or a local extremum, and updates according to the following formula:
<mrow> <msubsup> <mi>v</mi> <mi>i</mi> <mi>j</mi> </msubsup> <mo>=</mo> <mi>w</mi> <mo>&amp;CenterDot;</mo> <msubsup> <mi>v</mi> <mi>i</mi> <mi>j</mi> </msubsup> <mo>+</mo> <msub> <mi>c</mi> <mn>1</mn> </msub> <mo>&amp;CenterDot;</mo> <mi>r</mi> <mi>a</mi> <mi>n</mi> <mi>d</mi> <mrow> <mo>(</mo> <mo>)</mo> </mrow> <mo>&amp;CenterDot;</mo> <mrow> <mo>(</mo> <msubsup> <mi>pbest</mi> <mi>i</mi> <mi>j</mi> </msubsup> <mo>-</mo> <msubsup> <mi>population</mi> <mi>i</mi> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>c</mi> <mn>2</mn> </msub> <mo>&amp;CenterDot;</mo> <mi>r</mi> <mi>a</mi> <mi>n</mi> <mi>d</mi> <mrow> <mo>(</mo> <mo>)</mo> </mrow> <mo>&amp;CenterDot;</mo> <mrow> <mo>(</mo> <msubsup> <mi>gbest</mi> <mi>i</mi> <mi>j</mi> </msubsup> <mo>-</mo> <msubsup> <mi>population</mi> <mi>i</mi> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> </mrow>
<mrow> <mi>V</mi> <mi>m</mi> <mi>a</mi> <mi>x</mi> <mo>=</mo> <mrow> <mo>(</mo> <msubsup> <mi>bound</mi> <mn>1</mn> <mi>j</mi> </msubsup> <mo>-</mo> <msubsup> <mi>bound</mi> <mn>0</mn> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> <mo>&amp;CenterDot;</mo> <mn>0.1</mn> </mrow>
Vmin＝-Vmax
<mrow> <msubsup> <mi>v</mi> <mi>i</mi> <mi>j</mi> </msubsup> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mi>V</mi> <mi>m</mi> <mi>a</mi> <mi>x</mi> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msubsup> <mi>v</mi> <mi>i</mi> <mi>j</mi> </msubsup> <mo>&gt;</mo> <mi>V</mi> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>V</mi> <mi>m</mi> <mi>i</mi> <mi>n</mi> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msubsup> <mi>v</mi> <mi>i</mi> <mi>j</mi> </msubsup> <mo>&lt;</mo> <mi>V</mi> <mi>min</mi> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>
<mrow> <msubsup> <mi>population</mi> <mi>i</mi> <mi>j</mi> </msubsup> <mo>=</mo> <msubsup> <mi>population</mi> <mi>i</mi> <mi>j</mi> </msubsup> <mo>+</mo> <msubsup> <mi>v</mi> <mi>i</mi> <mi>j</mi> </msubsup> </mrow>
wherein,is the velocity in the jth dimension of the ith particle, w is the inertial weight,is the position of the ith particle in the jth dimension,is the extreme value of the individual,is a global extremum, rand () is a random number between 0 and 1, c1、c2The learning factors Vmax and Vmin respectively correspond to the maximum value and the minimum value of the movement distance of the ith particle in the jth dimension.
9. The improved particle swarm algorithm for the prestack seismic data elastic parameter inversion problem according to claim 8, wherein: c is mentioned1、c2The values of (a) and (b) are all 2, the value of w is 0.5, the number of particles is 40, and the maximum iteration number is 5000.
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