CN110031896B - Seismic random inversion method and device based on multi-point geostatistics prior information - Google Patents
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Abstract
本发明公开了一种基于多点地质统计学先验信息的地震随机反演方法及装置，该方法包括：根据已知三维地质模型，确定待反演剖面、井数据和训练图像；根据井数据和训练图像，确定待反演剖面的岩相概率分布模型；在岩相概率分布模型约束下，根据井数据以及对井数据进行岩石物理统计得到的不同岩相对应的弹性参数的概率密度函数和变差函数，确定待反演剖面上待反演地震道对应的弹性参数的先验概率密度函数；在贝叶斯框架下，根据岩相概率分布模型约束下得到的弹性参数的先验概率密度函数和地震数据，确定反演结果。本发明为反演提供了更为准确的先验信息，有利于提高反演结果的准确度和分辨率，使反演结果更符合实际生产的需求。
The invention discloses a seismic random inversion method and device based on multi-point geostatistical prior information. The method includes: determining a section to be inverted, well data and training images according to a known three-dimensional geological model; and training images to determine the lithofacies probability distribution model of the section to be inverted; under the constraints of the lithofacies probability distribution model, the probability density functions and Variation function to determine the prior probability density function of the elastic parameters corresponding to the seismic traces to be inverted on the section to be inverted; under the Bayesian framework, the prior probability density of the elastic parameters obtained under the constraints of the lithofacies probability distribution model function and seismic data to determine the inversion results. The invention provides more accurate prior information for the inversion, is beneficial to improve the accuracy and resolution of the inversion result, and makes the inversion result more in line with the actual production requirements.
Description
Technical Field
The invention relates to the field of petroleum exploration, in particular to a random seismic inversion method and device based on multipoint geostatistics prior information.
Background
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
With the development of society and economy, the national demand for energy is higher and higher. Since the development of new energy sources cannot meet the energy demand, the exploration and development of traditional energy sources (e.g., petroleum) are still very important. In the early exploration stage of petroleum, under the guidance of a anticline theory, structural oil and gas reservoirs such as found anticline oil and gas reservoirs are developed, but along with the gradual depletion of resources, the current oil and gas exploration is shifted to lithologic oil and gas reservoirs with high exploration difficulty. Due to the complex distribution of the underground medium, the lithology cannot be distinguished by the conventional seismic exploration means, and therefore, the seismic inversion plays an increasingly large role in the seismic exploration at the present stage. Especially, the study of pre-stack seismic inversion and related petrophysical theory is a very critical technology for exploration of lithologic hydrocarbon reservoirs.
At present, most oil and gas reservoirs enter an exploration stage with a high water cut period, and the oil exploitation difficulty of each oil field is large. The exploration of the remaining oil is therefore gradually becoming a center of gravity for each large field. The control of the distribution of the underground residual oil is an important means for the excavation of old oil fields, and the premise of the excavation depends on the control degree of reservoir parameters and the distribution rule of a work area, so that the development of the high-resolution reservoir seismic inversion technology is necessary.
The conventional deterministic inversion method can only obtain a smooth inversion result, is suitable for thicker reservoirs, and cannot identify small thin layers. Stochastic inversion techniques based on geostatistical theory are one of the most commonly used high resolution seismic inversion techniques at present, which have relatively high accuracy and resolution in all seismic inversion methods. Geostatistical inversion is a method combining geostatistical and seismic inversion, and the method integrates well logging information by using a geostatistical method to provide prior information for inversion. Firstly, performing petrophysical statistics on logging data to obtain the distribution condition of related geological random variables, and then acquiring a final inversion result by using seismic data. The method combines geological random modeling and seismic inversion technology, realizes the fusion of information with different scales, can improve the resolution of seismic inversion results, and widens the inversion result frequency band.
The current-stage geostatistics method mainly comprises two points of geostatistics methods and a multi-point geostatistics method.
The two-point geostatistics method is used for representing the spatial correlation of the elastic parameters based on the variation function, and can effectively represent the correlation degree between points in a space. Two most important techniques in two-point geostatistics are kriging interpolation technique and sequential gaussian simulation technique. The kriging interpolation method is a method for deterministically estimating a model space by using logging data and a variation function statistically obtained from the logging data, and the output of the method is a smooth modeling result and a variance used for evaluating uncertainty of the modeling result. The sequential Gaussian simulation method is a method for carrying out random simulation by using a mean value and a variance obtained by the kriging interpolation on the basis of the kriging interpolation. Compared with the kriging interpolation, sequential Gaussian simulation obtains a plurality of elastic parameters with equal probability. The method for Kergin interpolation and sequential Gaussian simulation has good simulation effect on discrete parameters such as rocks and continuous parameters such as speed. Because the variation function can only represent the correlation of two points in space each time, for a complex geological structure, the underground uncertainty is high, the anisotropy is strong, the lithofacies and the elastic parameters have different spatial correlations in different directions, and at the moment, the spatial correlation cannot be described by using a single variation function. Thus, two-point geostatistical methods do not enable the simulation of complex geological structures.
The multi-point geostatistics method utilizes a training image to represent a set of correlation among a plurality of points in a space, and utilizes a defined data template to scan the training image to acquire the probability distribution of parameters in a region to be simulated, so as to carry out random modeling. The main simulation object of the multipoint geostatistics is rock equivalent discrete parameters, and the simulation of continuous parameters such as porosity, speed, density and the like cannot be realized.
From the above analysis, the prior art cannot realize simultaneous simulation of continuous and discrete parameters of a complex geological structure, and further provides more accurate prior information for seismic inversion.
Disclosure of Invention
The embodiment of the invention provides a seismic random inversion method based on multipoint geostatistics prior information, which is used for solving the technical problem that the prior art can not realize simultaneous simulation of continuous and discrete parameters of a complex geological structure so as to provide more accurate prior information for seismic inversion, and comprises the following steps: determining a profile to be inverted, well data and a training image according to a known three-dimensional geological model; determining a lithofacies probability distribution model of a section to be inverted based on a multi-point geostatistics direct sampling algorithm according to well data and a training image; under the constraint of a lithofacies probability distribution model, determining a prior probability density function of elastic parameters corresponding to seismic channels to be inverted on a section to be inverted according to well data and probability density functions and variation functions of the elastic parameters corresponding to different lithofacies obtained by performing petrophysical statistics on the well data based on a two-point geostatistics algorithm; under a Bayes frame, determining a mean value and a covariance matrix of posterior probability distribution according to a prior probability density function of an elastic parameter and seismic data obtained under the constraint of a lithofacies probability distribution model; and randomly simulating the inverted solution space according to the mean value and the covariance matrix of the posterior probability distribution to obtain a plurality of different equal probability inversion results.
The embodiment of the invention also provides a seismic random inversion device based on the prior information of the multipoint geology, which is used for solving the technical problem that the prior art can not realize the simultaneous simulation of continuous and discrete parameters of a complex geological structure so as to provide more accurate prior information for seismic inversion, and comprises the following steps: the data acquisition module is used for determining a profile to be inverted, well data and a training image according to a known three-dimensional geological model; the lithofacies prior information determining module is used for determining a lithofacies probability distribution model of a section to be inverted based on a multi-point geostatistics direct sampling algorithm according to well data and a training image; the elastic parameter prior information determining module is used for determining a prior probability density function of an elastic parameter corresponding to a seismic channel to be inverted on a section to be inverted on the section to be inverted based on a two-point geostatistics algorithm under the constraint of a lithofacies probability distribution model and according to well data and probability density functions and variation functions of the elastic parameter corresponding to different lithofacies obtained by performing petrophysical statistics on the well data; and the inversion module is used for determining a mean value and a covariance matrix of posterior probability distribution according to the prior probability density function of the elastic parameters and the seismic data which are obtained under the constraint of the lithofacies probability distribution model under a Bayes framework, and randomly simulating an inverted solution space according to the mean value and the covariance matrix of the posterior probability distribution to obtain a plurality of different equal probability inversion results.
The embodiment of the invention also provides computer equipment for solving the technical problem that the prior art can not realize simultaneous simulation of continuous and discrete parameters of a complex geological structure so as to provide more accurate prior information for seismic inversion.
The embodiment of the invention also provides a computer readable storage medium, which is used for solving the technical problem that the prior art can not realize simultaneous simulation of continuous and discrete parameters of a complex geological structure so as to provide more accurate prior information for seismic inversion.
In the embodiment of the invention, after a section to be inverted, well data and a training image are determined according to a given known three-dimensional geological model, a lithofacies probability distribution model of the section to be inverted is determined according to the well data and the training image based on a multi-point geostatistics algorithm, and a prior probability density function of an elastic parameter corresponding to a seismic channel to be inverted on the section to be inverted is determined according to the well data and a probability density function and a variation function of the elastic parameter corresponding to different lithofacies obtained by performing petrophysical statistics on the well data based on the two-point geostatistics algorithm under the constraint of the lithofacies probability distribution model obtained by the multi-point geostatistics algorithm; and finally, determining an inversion result according to a prior probability density function of the elastic parameters and seismic data obtained under the constraint of the lithofacies probability distribution model under a Bayes framework.
According to the embodiment of the invention, the multi-point geostatistics and the two-point geostatistics are fused, the advantages of the two-point geostatistics in simulating the elastic parameters and the advantages of the multi-point geostatistics representing the complex geologic body are fully exerted, more accurate prior information is provided for inversion, the accuracy and the resolution of the inversion result are favorably improved, and the inversion result is more in line with the requirement of actual production.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:
FIG. 1 is a flow chart of a seismic stochastic inversion method based on multi-point geostatistical prior information according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of a three-dimensional compressional velocity model provided in an embodiment of the present invention;
FIG. 3 is a schematic diagram of a three-dimensional shear velocity model according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a three-dimensional density model provided in an embodiment of the present invention;
FIG. 5 is a schematic diagram of a two-dimensional compressional velocity model provided in an embodiment of the present invention;
FIG. 6 is a schematic diagram of a two-dimensional shear velocity model according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a two-dimensional density model provided in an embodiment of the present invention;
FIG. 8 is a schematic diagram of a training image provided in an embodiment of the present invention;
FIG. 9 is a diagram illustrating a lithofacies probability distribution simulation result provided in an embodiment of the present invention;
FIG. 10 is a schematic diagram illustrating an inversion result of a two-dimensional compressional velocity model according to an embodiment of the present invention;
fig. 11 is a schematic diagram illustrating an inversion result of a two-dimensional shear wave velocity model according to an embodiment of the present invention;
FIG. 12 is a schematic diagram illustrating inversion results of a two-dimensional density model according to an embodiment of the present invention;
FIG. 13 is a schematic diagram of a single-pass inversion test result of a two-dimensional longitudinal wave velocity model according to an embodiment of the present invention;
FIG. 14 is a schematic diagram of a single-pass inversion test result of a two-dimensional shear wave velocity model according to an embodiment of the present invention;
FIG. 15 is a schematic diagram of a two-dimensional density model single-pass inversion test result provided in an embodiment of the present invention;
FIG. 16 is a schematic diagram illustrating a multiple random implementation of a single-pass inversion test result of a two-dimensional longitudinal wave velocity model according to an embodiment of the present invention;
FIG. 17 is a schematic diagram illustrating a multiple random implementation of a single-pass inversion test result of a two-dimensional shear wave velocity model according to an embodiment of the present invention;
FIG. 18 is a schematic diagram illustrating a multiple random implementation of a single inversion test result of a two-dimensional density model according to an embodiment of the present disclosure;
fig. 19 is a schematic diagram of a seismic stochastic inversion apparatus based on multi-point geostatistical prior information according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
The embodiment of the invention provides a seismic random inversion method based on multi-point geostatistics prior information. Fig. 1 is a flowchart of a seismic stochastic inversion method based on multi-point geostatistical prior information according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
and S101, determining a profile to be inverted, well data and a training image according to the known three-dimensional geological model.
It should be noted that the three-dimensional geological model may include, but is not limited to, at least one of the following: a three-dimensional lithofacies model, a three-dimensional longitudinal wave velocity model, a three-dimensional shear wave velocity model, or a three-dimensional density model. According to the given three-dimensional longitudinal wave velocity, transverse wave velocity, density and lithofacies geological model, a two-dimensional section is randomly selected as model data tested by an inversion method, and then several rows of data are selected from the selected section as pseudo logging data and a training image representing a geological mode. It should be noted that, since the well data used in the embodiments of the present invention is well data of a known geological model, and is not well data obtained by actual logging, some embodiments are also referred to as "pseudo-well data".
For example, fig. 2, 3, and 4 illustrate a three-dimensional compressional velocity model (km/s), a three-dimensional shear velocity model (km/s), and a three-dimensional density model (g/cc), respectively, of a three-dimensional geological model employed by embodiments of the present invention. Accordingly, a longitudinal wave velocity model (km/s), a transverse wave velocity model (km/s) and a density model (g/cc) of a two-dimensional section selected from the three-dimensional geological model are respectively shown in fig. 5, 6 and 7, and the transverse 150-channel data and the longitudinal time sampling points of the two-dimensional section are 80.
And S102, determining a lithofacies probability distribution model of the section to be inverted according to the well data and the training image.
Specifically, a lithofacies simulation result of a section to be inverted is obtained by utilizing a multi-point geostatistical algorithm according to well data and a training image.
Because the existing multipoint geostatistical algorithm (for example, SIMPAT algorithm or SNESIM algorithm) has the disadvantage of too long calculation time, as an optional implementation manner, in the embodiment of the invention, a lithofacies probability distribution model of a section to be inverted is determined according to well data and a training image based on a multipoint geostatistical Direct Sampling (DS) algorithm. Through a direct sampling algorithm, the multipoint geostatistics calculation process can be simplified, and the calculation efficiency is improved. Fig. 8 is a schematic diagram of a training image provided in an embodiment of the present invention, as shown in fig. 8, a training image is formed by several randomly extracted sections in a three-dimensional lithofacies model, an icon 801 (black area) shows a sandstone phase, and an icon 802 (white area) shows a mudstone phase. FIG. 9 is a schematic diagram of a facies probability distribution simulation result determined by the multi-point geostatistical direct sampling algorithm from the training image shown in FIG. 8.
It should be noted that, in the prior art, an approximate lithofacies probability distribution is directly given according to well data as lithofacies prior information, so that the accuracy of an inversion result obtained based on the prior information is not high.
S103, under the constraint of a lithofacies probability distribution model, determining a prior probability density function of elastic parameters corresponding to seismic traces to be inverted on a section to be inverted according to well data and probability density functions and variation functions of the elastic parameters corresponding to different lithofacies obtained by performing petrophysical statistics on the well data.
It should be noted that, the elastic parameters corresponding to the seismic traces to be inverted on the section to be inverted may include, but are not limited to, at least one of the following: lithofacies, compressional velocity, shear velocity, or density. By counting parameters such as lithofacies, longitudinal wave velocity, transverse wave velocity and density in well data, probability density functions and variation functions corresponding to different lithofacies can be obtained. According to the embodiment of the invention, after a lithofacies probability distribution model of data to be inverted is obtained by utilizing multipoint geostatistical random simulation, a prior probability density function of longitudinal wave velocity, transverse wave velocity and density corresponding to a seismic channel to be inverted is obtained under the constraint of the lithofacies probability distribution model.
And S104, determining an inversion result according to the prior probability density function of the elastic parameters and the seismic data obtained under the constraint of the lithofacies probability distribution model under a Bayes framework.
Specifically, under a Bayes framework, the prior information (prior probability density functions of longitudinal wave velocity, transverse wave velocity and density) of elastic parameters under the constraint of a lithofacies probability distribution model of multi-point geostatistics simulation and seismic data are integrated to obtain an inversion result. FIG. 10 is an inversion (km/s) of the two-dimensional compressional velocity model shown in FIG. 5; FIG. 11 is an inversion result (km/s) of the two-dimensional shear velocity model shown in FIG. 6; FIG. 12 is the inversion (g/cc) of the two-dimensional density model shown in FIG. 7.
Optionally, a mean and a covariance matrix of the posterior probability distribution may be determined according to a prior probability density function of the elastic parameter and seismic data obtained under the constraint of the lithofacies probability distribution model, and a plurality of inversion results may be determined according to the determined mean and covariance matrix of the posterior probability distribution. Fig. 13, 14 and 15 show the results of single-pass inversion test of the compressional velocity, shear velocity and density model, respectively, with the solid line being the real model and the dashed line being the inversion result; fig. 16, 17 and 18 show multiple random realizations of single-pass inversion test results for compressional velocity, shear velocity and density models, respectively, where the thick line is the real model and the thin line is the 10 different random inversion results.
From the above, in the embodiment of the present invention, the lithofacies probability distribution model is determined based on the multi-point geostatistics algorithm, the prior probability density function of the elastic parameter is determined based on the two-point geostatistics algorithm under the constraint of the lithofacies probability distribution model obtained by the multi-point geostatistics, and finally, the inversion result is determined according to the prior probability density function of the elastic parameter and the seismic data obtained under the constraint of the lithofacies probability distribution model under the bayesian framework. Through the embodiment of the invention, more accurate prior information is provided for inversion, the accuracy and the resolution of the inversion result are improved, and the inversion result is more in line with the requirement of actual production.
In one embodiment, for a certain grid point x to be simulated, it is assumed that u (x) represents the lithofacies of the point to be simulated, and z (x) represents a certain type of elastic parameter value (compressional velocity, shear velocity, density, etc.) of the point. Two rock facies are assumed to be sand and mud rocks and obey two-point distribution; the elastic parameters follow a gaussian distribution, and then:
wherein p represents the probability that the variable value in the two-point distribution takes 1, μxMean value, Σ, of a gaussian distribution representing compliance of an elastic parameterxRepresenting the variance of the gaussian distribution to which the elasticity parameter obeys.
(ii) DS stochastic simulation:
assuming that there are several well points within the work area, the well data is used as "hard data" for stochastic simulation or modeling. And setting a random path as a simulation sequence of all points in the work area. And setting the maximum simulation point number for a certain point to be simulated, searching well data or simulated points around the point to be simulated as condition data, wherein the condition number does not exceed the maximum simulation point number. Suppose that k eligible conditional data points are found, each having a position x1,x2,···,xkCalculating the 'lag vector' corresponding to each condition data point and the point to be simulated respectively and expressing as follows:
L＝{h1,h2,···,hk}＝{x1-x,x2-x,···,xk-x} (2)
it should be noted that, the "lag vector" is used to describe the mutual position relationship between the point to be simulated and the known condition data point, and for a two-dimensional work area, each element of the "lag vector" is the difference between two-dimensional coordinates. Assuming that the rock phase values of the known condition data points are respectively: u (x)1),U(x2),···,U(xk) Then the point to be simulated and the known well data in the neighborhood together constitute a data event:
dev(x,L)＝{U(x+h1),U(x+h2),···,U(x+hk)}＝{U(x1),U(x2),···,U(xk)} (3)
and setting a random path in the prepared training image, and scanning the sampling points along the random path. Assuming that the position vector of a certain point in the training image is y, the same method for finding data events is used as above to obtain data events dev (y, L) having the same structure as dev (x, L) around the point y in the training image, using the "lag vector" obtained from the region to be simulated, i.e.:
dev(y,L)＝{U(y+h1),U(y+h2),···,U(y+hk)}＝{U(y1),U(y2),···,U(yk)} (4)
the "distance" between two data events is calculated, namely:
namely: the closer the facies values of the two data events are, the smaller the distance is, and vice versa. And after the distance value is obtained, comparing the distance value with a set threshold value, if the distance is equal to 0 or less than the threshold value, assigning U (y) to U (x), and if the distance is greater than the threshold value, scanning the next point of the training image until the last point in the random path. And if the points with the distance smaller than the threshold value are not found after the training image is scanned, assigning the rock facies value corresponding to the point with the minimum distance to the point to be simulated in the simulation area.
Compared with the traditional SNESIM algorithm and SIMPAT algorithm, the probability distribution of the points to be simulated is not solved by the algorithm, but the points which have similar geological patterns with the neighborhood of the points to be simulated in the training image are directly selected and assigned. Based on the thought of sequential simulation, after the point to be simulated is simulated, the point to be simulated can be used as a known point to participate in subsequent calculation, and the number of the known points in the neighborhood of the point to be simulated is increased along with the progress of the simulation, which is similar to the effect of the multiple grids in the traditional SNESIM algorithm.
Kriging interpolation under multi-point geostatistics constraint:
knowing the region of work to be simulated, multi-point geostatisticsAnd obtaining a lithofacies simulation result and logging data by the algorithm. For a certain point x to be simulated, a neighborhood of selection condition data is first set. Let Z (x) be the elastic parameter of k condition data in the neighborhood1),Z(x2),···,Z(xk) The lithofacies are respectively U (x)1),U(x2),···,U(xk). Restriction in lithofacies
Under the action of (2), it is assumed that there are k' condition data. I.e. there are k' data with the same prior distribution of lithofacies. The kriging interpolation expression under the lithofacies constraint is as follows:
wherein λ isiRepresenting the proportion of the ith data point in the kriging interpolation. The kriging interpolation can generate a smoother deterministic estimation result which can be used for an initial model mu of a subsequent inversion methodm|MPGI.e. mum|MPG＝Z(x)。
Random inversion under the constraint of multipoint geology statistics:
the seismic inversion problem can be described by a Bayesian theory framework, and the basic expression is as follows:
σM(m)＝αρM(m)L(m) (7)
wherein L (m) is a likelihood function describing the degree of match between the earth's surface observation and the geological parameters, ρM(m) and σM(m) respectively represents the prior probability distribution and the posterior probability distribution of the obtained geological parameters, and alpha can be regarded as a normalization factor and can be ignored in a general inversion method.
Assuming that the noise obeys a mean of 0 and the covariance matrix is Σ for seismic recordingseOf (a) a multivariate Gaussian distribution, i.e. ε -N (0, Σ)e) According to the forward modeling relation of the seismic data of epsilon-d-Gm, the probability distribution expression of the noise is as follows:
wherein D is the dimension of the multivariate variable. d represents seismic data and G represents a forward operator between the seismic data and the elastic parameters. Assuming that m representing model parameters such as longitudinal wave velocity, transverse wave velocity and density also obeys multivariate Gaussian distribution m-N (mu m)m|MPG,Σm) The expression is as follows:
wherein mum|MPGThe model is a prior model, can be obtained by a kriging interpolation method constrained by multipoint geology and statistics, and comprises three parts: a longitudinal wave velocity model, a shear wave velocity model, and a density model. SigmamIs a multivariate covariance matrix of the model parameters. Assuming that the model parameter corresponding to a seismic data has n sampling points, for the pre-stack three-parameter inversion, the covariance matrix is a 3n × 3n matrix. The covariance matrix has a large influence on the inversion result, and the expression is as follows:
each element of the matrix is a covariance matrix indexed by a corresponding parameter, e.g. ∑psRepresenting a multivariate covariance matrix between compressional and shear wave velocities. The covariance matrix can be obtained by a variation function. By sigmappTaking the calculation of (2), statistical analysis is performed on the logging data to obtain a variation function matrix R, and according to the relationship between the variation function and the covariance function:
Σpp＝C0-R (11)
C0to become a variation boxBase station number of. The covariance matrix of the model is determined by a variation function, and different geological structures need to be simulated by different variation functions in geological modeling; in seismic inversion, different geologic structures require different varistors to provide a priori correlation of the model. The region with violent speed change and large change amplitude is always provided with a variation function of small variation range and large base station value; on the contrary, in the area where the speed changes slowly and the change degree is small, the variation function has a large variation range and a small base station value.
The lithofacies results of the multi-point geostatistical stochastic simulation may provide a priori information about the distribution of subsurface lithofacies. According to the method, different variation functions are obtained according to well data statistics, and different variation function values are given to different lithofacies in a lithofacies model obtained through multipoint geostatistical simulation to describe the correlation among sampling points. Under the constraint of the lithofacies model, a more accurate covariance matrix sigma can be obtainedm|MPGSo that the expression of the prior probability distribution is:
according to prior information under the constraint of multipoint geostatistics, a likelihood function formed by combining seismic data and a linear Bayesian inversion theory, a mean value and a covariance matrix of posterior probability distribution can be obtained:
mu at the left end represents the mean value of the posterior probability distribution, which is equivalent to a smoother inversion solution, and sigma represents the covariance matrix of the posterior probability distribution. According to the two parameters, random simulation can be carried out on the inversion solution space, and a plurality of different equal probability inversion results can be obtained.
The embodiment of the invention also provides a seismic stochastic inversion device based on the multipoint geostatistical prior information, which is described in the following embodiment. Because the principle of solving the problem of the embodiment of the device is similar to the seismic random inversion method based on the multipoint geostatistics prior information, the implementation of the embodiment of the device can refer to the implementation of the method, and repeated parts are not repeated.
Fig. 19 is a schematic diagram of a seismic stochastic inversion apparatus based on multi-point geostatistical prior information according to an embodiment of the present invention, as shown in fig. 19, the apparatus includes: the system comprises a data acquisition module 191, a lithofacies prior information determination module 192, an elastic parameter prior information determination module 193 and an inversion module 194.
The data acquisition module 191 is used for determining a profile to be inverted, well data and a training image according to a known three-dimensional geological model; the lithofacies prior information determining module 192 is used for determining a lithofacies probability distribution model of the section to be inverted according to the well data and the training image; an elastic parameter prior information determining module 193, configured to determine, under the constraint of a lithofacies probability distribution model, a prior probability density function of an elastic parameter corresponding to a seismic trace to be inverted on a profile to be inverted according to probability density functions and variation functions of the elastic parameter corresponding to different lithofacies obtained by performing petrophysical statistics on well data and well data; and the inversion module 194 is configured to determine an inversion result according to the prior probability density function of the elastic parameter and the seismic data obtained under the constraint of the lithofacies probability distribution model in a bayesian framework.
It should be noted that the three-dimensional geological model includes at least one of the following: a three-dimensional lithofacies model, a three-dimensional longitudinal wave velocity model, a three-dimensional transverse wave velocity model or a three-dimensional density model; the elastic parameters corresponding to the seismic channels to be inverted on the section to be inverted comprise at least one of the following parameters: lithofacies, compressional velocity, shear velocity, or density.
Optionally, the data obtaining module 191 is further configured to obtain parameters of an inversion algorithm, so as to perform processing such as correcting or low-pass filtering on the logging data. Because the well data used in embodiments of the present invention is important, it must be accurately corrected for use.
Optionally, the lithofacies prior information determining module 192 is configured to determine a lithofacies probability distribution model of the profile to be inverted according to the well data and the training image based on a multi-point geostatistics direct sampling algorithm.
In an alternative embodiment, the inversion module 194 is configured to determine a mean and a covariance matrix of a posterior probability distribution according to the prior probability density function of the elastic parameter and the seismic data obtained under the constraint of the lithofacies probability distribution model in a bayesian framework, and determine an inversion result according to the mean and the covariance matrix of the posterior probability distribution.
As can be seen from the above, in the embodiment of the present invention, after determining a profile to be inverted, well data, and a training image according to a known three-dimensional geological model by the data obtaining module 191, a lithofacies probability distribution model is determined by the lithofacies prior information determining module 192 based on a multi-point geostatistics algorithm, a prior probability density function of an elastic parameter is determined by the elastic parameter prior information determining module 193 based on the constraints of the lithofacies probability distribution model obtained by the multi-point geostatistics algorithm under the constraints of the lithofacies probability distribution model obtained by the multi-point geostatistics algorithm, and finally, an inversion result is determined by the inversion module 194 under a bayesian framework according to the prior probability density function of the elastic parameter and seismic data obtained under the constraints of the litho. Through the embodiment of the invention, more accurate prior information is provided for inversion, the accuracy and the resolution of the inversion result are improved, and the inversion result is more in line with the requirement of actual production.
The embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the processor implements any one of the optional or preferred seismic stochastic inversion methods based on the multi-point geostatistical prior information in the above method embodiments.
The embodiment of the invention also provides a computer readable storage medium, which stores a computer program for executing any one of the optional or preferred seismic stochastic inversion methods based on the multi-point geostatistical prior information in the method embodiments.
In summary, the embodiments of the present invention can achieve, but are not limited to, the following technical effects: (1) the embodiment of the invention combines multi-point geostatistics with traditional two-point geostatistics, firstly realizes that lithofacies probability distribution obtained by using the multi-point geostatistics serves for inverting elastic parameters, and provides more accurate prior information; (2) random simulation of lithofacies probability distribution is realized by adopting a multi-point geostatistics DS algorithm, and the calculation efficiency and the simulation effect are superior to those of the traditional algorithms such as SIMPAT and SNESIM; (3) according to different lithofacies, different prior probability distributions are given to the elastic parameters (namely, different prior mean values and different prior covariance functions are given to the elastic parameters with different lithologies), the method is more reasonable and more suitable for actual conditions, and an inversion result has higher resolution from a one-dimensional model test chart; (4) according to the embodiment of the invention, the inversion solution and the posterior covariance matrix can be obtained simultaneously, wherein the posterior covariance matrix can be used for evaluating the uncertainty of the inversion result, and the uncertainty changes along with lithology, for example, in a stable and large set of mudstone section, the uncertainty of the inversion result is lower, and in the position of a sand-mudstone interbed, the uncertainty is higher.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (6)
1. A seismic stochastic inversion method based on multi-point geostatistical prior information is characterized by comprising the following steps:
determining a profile to be inverted, well data and a training image according to a known three-dimensional geological model;
determining a lithofacies probability distribution model of a section to be inverted based on a multi-point geostatistics direct sampling algorithm according to well data and a training image;
under the constraint of the lithofacies probability distribution model, determining a prior probability density function of elastic parameters corresponding to seismic channels to be inverted on a section to be inverted according to well data and probability density functions and variation functions of the elastic parameters corresponding to different lithofacies obtained by performing petrophysical statistics on the well data based on a two-point geostatistics algorithm;
under a Bayes frame, determining a mean value and a covariance matrix of posterior probability distribution according to a prior probability density function of the elastic parameters and seismic data obtained under the constraint of the lithofacies probability distribution model;
and randomly simulating the inverted solution space according to the mean value and the covariance matrix of the posterior probability distribution to obtain a plurality of different equal probability inversion results.
2. The method of claim 1, wherein the known three-dimensional geological model comprises at least one of: a three-dimensional lithofacies model, a three-dimensional longitudinal wave velocity model, a three-dimensional transverse wave velocity model or a three-dimensional density model; the elastic parameters corresponding to the seismic channels to be inverted on the section to be inverted comprise at least one of the following parameters: lithofacies, compressional velocity, shear velocity, or density.
3. A seismic stochastic inversion device based on multi-point geostatistical prior information, comprising:
the data acquisition module is used for determining a profile to be inverted, well data and a training image according to a known three-dimensional geological model;
the lithofacies prior information determining module is used for determining a lithofacies probability distribution model of a section to be inverted based on a multi-point geostatistics direct sampling algorithm according to well data and a training image;
the elastic parameter prior information determining module is used for determining the prior probability density function of the elastic parameter corresponding to the seismic channel to be inverted on the section to be inverted based on a two-point geostatistics algorithm under the restriction of the lithofacies probability distribution model and according to well data and the probability density function and the variation function of the elastic parameter corresponding to different lithofacies obtained by performing petrophysical statistics on the well data;
and the inversion module is used for determining a mean value and a covariance matrix of posterior probability distribution according to the prior probability density function of the elastic parameters and the seismic data which are obtained under the constraint of the lithofacies probability distribution model under a Bayes framework, and randomly simulating an inverted solution space according to the mean value and the covariance matrix of the posterior probability distribution to obtain a plurality of different equal probability inversion results.
4. The apparatus of claim 3, wherein the known three-dimensional geological model comprises at least one of: a three-dimensional lithofacies model, a three-dimensional longitudinal wave velocity model, a three-dimensional transverse wave velocity model or a three-dimensional density model; the elastic parameters corresponding to the seismic channels to be inverted on the section to be inverted comprise at least one of the following parameters: lithofacies, compressional velocity, shear velocity, or density.
5. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements the method for seismic stochastic inversion based on multi-point geostatistical priors of claim 1 or 2.
6. A computer-readable storage medium storing a computer program for executing the method for seismic stochastic inversion based on multi-point geostatistical prior information according to claim 1 or 2.
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