CN112147677A - Oil and gas reservoir parameter tag data generation method and device - Google Patents

Oil and gas reservoir parameter tag data generation method and device Download PDF

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CN112147677A
CN112147677A CN202010928025.3A CN202010928025A CN112147677A CN 112147677 A CN112147677 A CN 112147677A CN 202010928025 A CN202010928025 A CN 202010928025A CN 112147677 A CN112147677 A CN 112147677A
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lithofacies
research area
parameter
curve
curves
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CN112147677B (en
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桂金咏
高建虎
李胜军
雍学善
刘炳杨
陈启艳
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Petrochina Co Ltd
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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. analysis, for interpretation, for correction
    • 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. analysis, for interpretation, for correction
    • G01V1/282—Application of seismic models, synthetic seismograms
    • 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. analysis, for interpretation, for correction
    • 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
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/40—Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • 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
    • G01V2210/61—Analysis by combining or comparing a seismic data set with other data
    • G01V2210/616—Data from specific type of measurement
    • G01V2210/6169—Data from specific type of measurement using well-logging
    • 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
    • G01V2210/624—Reservoir parameters
    • 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
    • G01V2210/624—Reservoir parameters
    • G01V2210/6242—Elastic parameters, e.g. Young, Lamé or Poisson
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
    • Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping

Abstract

The invention provides a method and a device for generating parameter tag data of an oil and gas reservoir, wherein the method comprises the following steps: obtaining prior distribution probability of lithofacies by using a Gaussian mixture distribution function according to logging data of a research area; carrying out Markov chain Monte Carlo random simulation to generate a plurality of lithofacies curves changing along with the depth; performing sequential Gaussian simulation to generate a plurality of lithofacies curves which change along with the transverse direction; randomly filling each lithofacies according to the prior characteristics of values of a plurality of lithofacies curves changing along with the depth, a plurality of lithofacies curves changing along with the transverse direction and a parameter logging curve of an oil and gas reservoir to obtain a physical property parameter curve of a research area; performing lithofacies constraint statistical rock physical modeling, and determining an elastic parameter curve of a research area; and performing convolution by using a Zoeppritz reflection equation and the seismic wavelets to generate oil and gas reservoir parameter label data. The number and the quality of the well logging in the research area are not required to be depended on, the mobility of the generated label data is improved, and the generation efficiency is further improved.

Description

Oil and gas reservoir parameter tag data generation method and device
Technical Field
The invention relates to the technical field of oil and gas geophysical exploration, in particular to a method and a device for generating oil and gas reservoir parameter tag data.
Background
In recent years, the emphasis of oil and gas exploration in China gradually shifts to lithologic oil and gas reservoir exploration. Different from the structured oil and gas reservoirs, the novel reservoirs are influenced by the heterogeneity of the structures and the reservoirs, the reservoir forming conditions are complex, the identification difficulty is high, the quantitative prediction is difficult, and the investment risk is high. In the field of oil and gas geophysical exploration, reservoir parameters, such as elastic parameters including lithofacies, longitudinal and transverse wave velocities and the like, and physical parameters including porosity, oil and gas saturation and the like, can describe the elastic properties and the physical properties of underground rocks, and are important parameters for lithologic oil and gas reservoir prediction. Due to complex reservoir formation conditions, highly nonlinear relationships exist between seismic response characteristics and reservoir parameters. The conventional technology for predicting the reservoir parameters by utilizing seismic data has large errors and cannot meet the requirement of quantitative exploration. The advent of artificial intelligence techniques has made possible the quantitative prediction of such complex reservoirs. In the field of oil and gas geophysical exploration, an intelligent geophysical exploration technology is formed by combining artificial intelligence with the conventional seismic data processing and explaining technology, and the working efficiency of seismic data processing and explaining can be greatly improved.
The label data is the basis of the artificial intelligence supervised learning network, and the quantity and the quality of the label data directly determine the quality of a prediction result. However, in the field of oil and gas geophysical exploration, due to the fact that data resources are limited, label data are extremely deficient, and development of an intelligent reservoir parameter prediction technology is severely limited. The existing generation of oil and gas reservoir parameter tag data mainly utilizes known logging data and well side channel seismic data to generate tag data. However, the method can cause the generation of the label data to depend on the quantity and quality of the logging in the research area seriously, so that the oil and gas reservoir parameter label data cannot be generated for the research area with less logging. In addition, the generated label data has extremely poor mobility, and a target research area is often changed, so that a large amount of logging data and well side channel seismic data need to be collected again, and the generation efficiency of the oil and gas reservoir parameter label data is very low.
Disclosure of Invention
The embodiment of the invention provides a method for generating parameter tag data of an oil and gas reservoir, which is used for improving the mobility of the generated tag data and the generation efficiency of the tag data without depending on the quantity and quality of logging in a research area, and comprises the following steps:
obtaining the prior distribution probability of the lithofacies of the research area by utilizing a Gaussian mixture distribution function according to the logging data of the research area;
according to the prior distribution probability of the lithofacies of the research area, carrying out Markov chain Monte Carlo random simulation to generate a plurality of lithofacies curves changing along with the depth;
performing sequential Gaussian simulation according to the generated multiple lithofacies curves changing along with the depth to generate multiple lithofacies curves changing along with the transverse direction;
randomly filling each lithofacies according to the prior characteristics of values of a plurality of lithofacies curves changing along with the depth, a plurality of lithofacies curves changing along with the transverse direction and an oil and gas reservoir parameter logging curve of the research area to obtain a physical property parameter curve of the research area;
according to the physical property parameter curve of the research area, performing lithofacies constraint statistical rock physical modeling, and determining an elastic parameter curve of the research area;
and (3) performing convolution by using a Zoeppritz reflection equation and seismic wavelets according to the elastic parameter curve of the research area to generate oil and gas reservoir parameter label data.
The embodiment of the invention also provides a device for generating the parameter tag data of the oil and gas reservoir, which is used for improving the mobility of the generated tag data and the generation efficiency of the tag data without depending on the quantity and the quality of the logging in the research area, and comprises the following steps:
the prior distribution calculation module is used for obtaining the prior distribution probability of the lithofacies of the research area by utilizing a Gaussian mixture distribution function according to the logging data of the research area;
the Monte Carlo simulation module is used for carrying out Markov chain Monte Carlo random simulation according to the prior distribution probability of the lithofacies of the research area to generate a plurality of lithofacies curves changing along with the depth;
the sequential Gaussian simulation module is used for performing sequential Gaussian simulation according to the generated multiple lithofacies curves changing along with the depth to generate multiple lithofacies curves changing along with the transverse direction;
the physical property filling module is used for randomly filling each lithofacies according to the prior characteristics of values of a plurality of lithofacies curves changing along with the depth, a plurality of lithofacies curves changing along with the transverse direction and an oil and gas reservoir parameter logging curve of the research area to obtain a physical property parameter curve of the research area;
the statistical rock physics modeling module is used for carrying out lithofacies constraint statistical rock physics modeling according to the physical property parameter curve of the research area and determining an elastic parameter curve of the research area;
and the oil and gas reservoir parameter tag data generation module is used for performing convolution by utilizing a Zoeppritz reflection equation and seismic wavelets according to the elastic parameter curve of the research area to generate oil and gas reservoir parameter tag data.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can be run on the processor, wherein the processor realizes the oil and gas reservoir parameter tag data generation method when executing the computer program.
Embodiments of the present invention also provide a computer-readable storage medium storing a computer program for executing the method for generating hydrocarbon reservoir parameter tag data.
In the embodiment of the invention, the prior distribution probability of the lithofacies of the research area is obtained by utilizing a Gaussian mixture distribution function according to the logging data of the research area; according to the prior distribution probability of the lithofacies of the research area, carrying out Markov chain Monte Carlo random simulation to generate a plurality of lithofacies curves changing along with the depth; performing sequential Gaussian simulation according to the generated multiple lithofacies curves changing along with the depth to generate multiple lithofacies curves changing along with the transverse direction; randomly filling each lithofacies according to the prior characteristics of values of a plurality of lithofacies curves changing along with the depth, a plurality of lithofacies curves changing along with the transverse direction and an oil and gas reservoir parameter logging curve of the research area to obtain a physical property parameter curve of the research area; according to the physical property parameter curve of the research area, performing lithofacies constraint statistical rock physical modeling, and determining an elastic parameter curve of the research area; performing convolution by using a Zoeppritz reflection equation and seismic wavelets according to an elastic parameter curve of a research area to generate oil and gas reservoir parameter label data; a plurality of lithofacies curves changing along with depth are generated through Markov chain Monte Carlo random simulation and a plurality of lithofacies curves changing along with transverse direction are generated through sequential Gaussian simulation, so that sufficient label data can be generated when the number of logging in a research area is small or the quality is poor, and the generation of the parameter label data of the oil and gas reservoir does not depend on the number and the quality of logging in the research area; by introducing prior distribution probability of lithofacies of a research area, seismic wavelets and lithofacies constraint statistical rock physical modeling in the label generation process, oil and gas reservoir parameter label data matched with the characteristics of the research area can be obtained; therefore, when the research area is replaced, the generated label data can adapt to different research areas only by adaptive adjustment according to the characteristics of the research area, a large amount of logging data and well side channel seismic data do not need to be collected again, the mobility of the generated label data is improved, and the generation efficiency of the label data is further improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic diagram of a method for generating hydrocarbon reservoir parameter tag data according to an embodiment of the invention.
Fig. 2 is a schematic diagram of a specific implementation method of step 101 in an embodiment of the present invention.
Fig. 3 is a schematic diagram of a specific implementation method of step 104 in an embodiment of the present invention.
Fig. 4 is a schematic diagram of a specific implementation method of step 106 in an embodiment of the present invention.
Fig. 5 is a schematic structural diagram of a device for making tag data of hydrocarbon reservoir parameters in the implementation of the present invention.
FIG. 6 is a diagram illustrating lithofacies segmentation results based on elastic parameters in the practice of the present invention.
FIG. 7 is a diagram illustrating lithofacies partitioning results based on physical parameters in the practice of the present invention.
FIG. 8 is a schematic diagram of 5 adjacent facies curves as a function of depth in the practice of the present invention.
FIG. 9 is a schematic diagram of 5 adjacent lithofacies curves as a function of lateral variation in the practice of the present invention.
FIG. 10 is a diagram illustrating a lithofacies curve and a filled physical property parameter curve in the practice of the present invention.
FIG. 11 is a schematic diagram of an elastic parameter curve corresponding to FIG. 10 in an exemplary embodiment of the present invention.
FIG. 12 is a schematic view of a synthetic prestack seismic angle gather for a specific application of the present invention.
Fig. 13 is a schematic diagram of a noisy prestack seismic angle gather corresponding to fig. 12 in an implementation of a particular application of the present invention.
Fig. 14 is a schematic diagram of a hydrocarbon reservoir parameter tag data generation device in an embodiment of the invention.
Fig. 15 is a schematic structural diagram of a prior distribution calculation module 1401 in an embodiment of the present invention.
Fig. 16 is a schematic structural view of a physical property filling module 1404 in an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a method for generating parameter tag data of an oil and gas reservoir, which is used for improving the mobility of the generated tag data and the generation efficiency of the tag data without depending on the quantity and quality of logging in a research area, and as shown in figure 1, the method comprises the following steps:
step 101: obtaining the prior distribution probability of the lithofacies of the research area by utilizing a Gaussian mixture distribution function according to the logging data of the research area;
step 102: according to the prior distribution probability of the lithofacies of the research area, carrying out Markov chain Monte Carlo random simulation to generate a plurality of lithofacies curves changing along with the depth;
step 103: performing sequential Gaussian simulation according to the generated multiple lithofacies curves changing along with the depth to generate multiple lithofacies curves changing along with the transverse direction;
step 104: randomly filling each lithofacies according to the prior characteristics of values of a plurality of lithofacies curves changing along with the depth, a plurality of lithofacies curves changing along with the transverse direction and an oil and gas reservoir parameter logging curve of the research area to obtain a physical property parameter curve of the research area;
step 105: according to the physical property parameter curve of the research area, performing lithofacies constraint statistical rock physical modeling, and determining an elastic parameter curve of the research area;
step 106: and (3) performing convolution by using a Zoeppritz reflection equation and seismic wavelets according to the elastic parameter curve of the research area to generate oil and gas reservoir parameter label data.
As can be known from the flow shown in fig. 1, in the embodiment of the present invention, the prior distribution probability of the lithofacies of the research area is obtained by using the gaussian mixture distribution function according to the logging data of the research area; according to the prior distribution probability of the lithofacies of the research area, carrying out Markov chain Monte Carlo random simulation to generate a plurality of lithofacies curves changing along with the depth; performing sequential Gaussian simulation according to the generated multiple lithofacies curves changing along with the depth to generate multiple lithofacies curves changing along with the transverse direction; randomly filling each lithofacies according to the prior characteristics of values of a plurality of lithofacies curves changing along with the depth, a plurality of lithofacies curves changing along with the transverse direction and an oil and gas reservoir parameter logging curve of the research area to obtain a physical property parameter curve of the research area; according to the physical property parameter curve of the research area, performing lithofacies constraint statistical rock physical modeling, and determining an elastic parameter curve of the research area; performing convolution by using a Zoeppritz reflection equation and seismic wavelets according to an elastic parameter curve of a research area to generate oil and gas reservoir parameter label data; a plurality of lithofacies curves changing along with depth are generated through Markov chain Monte Carlo random simulation and a plurality of lithofacies curves changing along with transverse direction are generated through sequential Gaussian simulation, so that sufficient label data can be generated when the number of logging in a research area is small or the quality is poor, and the generation of the parameter label data of the oil and gas reservoir does not depend on the number and the quality of logging in the research area; by introducing prior distribution probability of lithofacies of a research area, seismic wavelets and lithofacies constraint statistical rock physical modeling in the label generation process, oil and gas reservoir parameter label data matched with the characteristics of the research area can be obtained; therefore, when the research area is replaced, the generated label data can adapt to different research areas only by adaptive adjustment according to the characteristics of the research area, a large amount of logging data and well side channel seismic data do not need to be collected again, the mobility of the generated label data is improved, and the generation efficiency of the label data is further improved.
In specific implementation, firstly, according to the logging data of the research area, a gaussian mixture distribution function is used to obtain a prior distribution probability of the lithofacies of the research area, and in specific implementation, as shown in fig. 2, the method includes:
step 201: performing multivariate Gaussian mixture distribution function estimation on the physical parameter logging curve and the elastic parameter logging curve of the research area by adopting an expected maximum estimation algorithm to obtain a proportionality coefficient in the Gaussian mixture distribution function corresponding to the physical parameter and a proportionality coefficient in the Gaussian mixture distribution function corresponding to the elastic parameter;
step 202: determining the type and prior distribution probability of a petrographic phase based on physical property division in a research area according to a proportionality coefficient in a Gaussian mixture distribution function corresponding to the physical property parameters;
step 203: determining the type and prior distribution probability of the lithofacies based on elastic partition in the research area according to the proportional coefficient in the Gaussian mixture distribution function corresponding to the elastic parameters;
step 204: and obtaining the prior distribution probability of the lithofacies of the research area according to the type and the prior distribution probability of the lithofacies of the research area based on physical property division and the type and the prior distribution probability of the lithofacies of the research area based on elastic division.
In a specific embodiment, the physical parameters of the research area include oil and gas saturation, porosity, mineral content and other parameters of the research area, and the elastic parameters include longitudinal wave velocity, transverse wave velocity, density and other parameters. And performing multivariate Gaussian mixture distribution function estimation on the physical parameter logging curve and the elastic parameter logging curve of the research area by adopting an expected maximum estimation algorithm, and estimating to obtain the number of Gaussian components and the proportion coefficient in the Gaussian mixture distribution function corresponding to the physical parameter and the number of Gaussian components and the proportion coefficient in the Gaussian mixture distribution function corresponding to the elastic parameter.
The number of types of the petrophysical phases classified based on the physical properties of the research region is determined based on the number of gaussian components (that is, the number of proportionality coefficients) in the gaussian mixture distribution function corresponding to the physical properties parameter, and the prior distribution probability of the petrophysical phases classified based on the physical properties of the research region is determined based on the value of the proportionality coefficient in the gaussian mixture distribution function corresponding to the physical properties parameter.
Determining the number of the types of the facies based on the elastic partition of the research area according to the number of Gaussian components (namely the number of proportionality coefficients) in the Gaussian mixture distribution function corresponding to the elastic parameters, and determining the prior distribution probability of the facies based on the elastic partition of the research area according to the value of the proportionality coefficients in the Gaussian mixture distribution function corresponding to the elastic parameters.
And combining the physical property division-based lithofacies type and prior distribution probability of the research area and the elastic division-based lithofacies type and prior distribution probability of the research area, and selecting the corresponding prior distribution probability with the largest lithofacies type number as the prior distribution probability of the lithofacies of the research area. And when the number of the types of the lithofacies of the two lithofacies is equal, taking the mean value of the prior distribution probabilities of the two lithofacies as the prior distribution probability of the lithofacies of the research area.
And after the prior distribution probability of the lithofacies of the research area is obtained, carrying out Markov chain Monte Carlo random simulation according to the prior distribution probability of the lithofacies of the research area to generate a plurality of lithofacies curves changing along with the depth. In specific implementation, the probability that the time t belongs to a certain category of lithofacies is only related to the probability of the lithofacies category to which the time t-1 belongs, and the change in the depth direction is regarded as the transition between different lithofacies categories. Defining a Markov transition probability matrix of the downward rock facies as follows:
Figure BDA0002669149170000071
wherein the matrix element pi,jRepresents the conditional probability of turning from facies category i to facies category j:
pi,j＝P(Fj|Fi)
＝P(Fi|Fj)P(Fj)/P(Fi)
in a specific embodiment, the markov transition probability matrix and the prior distribution probability P (F) of the facies at a known time t are obtained by counting well-logging samples of the study areat) The prior distribution probability P (F) of the lithofacies at the time t +1 can be obtained by using the obtained Markov transition probability matrixt+1)：
P(Ft+1)＝P(Ft)PT
Obtaining the prior distribution probability of the lithofacies at each moment, and generating a plurality of lithofacies curves which change along with the depth by utilizing a Monte Carlo random simulation method.
And after a plurality of lithofacies curves changing along with the depth are generated, sequential Gaussian simulation is carried out according to the generated plurality of lithofacies curves changing along with the depth, and a plurality of lithofacies curves changing along with the transverse direction are generated. During specific implementation, each lithofacies curve changing along with the depth is used as a seed sample, and sequential Gaussian simulation is performed by using a variation function to generate a plurality of lithofacies curves changing along with the transverse direction; wherein, the variation function is obtained by performing elliptic function fitting by utilizing a lithofacies sample at the well logging position of the research area.
And after a plurality of lithofacies curves which change along with the transverse direction are generated, filling all the lithofacies randomly according to the plurality of lithofacies curves which change along with the depth, the plurality of lithofacies curves which change along with the transverse direction and the prior characteristics of the values of the oil and gas reservoir parameter logging curves of the research area to obtain the physical property parameter curve of the research area. The specific implementation process, as shown in fig. 3, includes:
step 301: constructing a Gaussian mixture distribution function according to the prior characteristics of the logging curve values of the oil and gas reservoir parameters of the research area, and randomly generating a plurality of reservoir parameters which accord with the prior distribution characteristics of the oil and gas reservoir parameters of each lithofacies interval;
step 302: and obtaining a physical property parameter curve of the research area according to the plurality of reservoir parameters, the plurality of lithofacies curves changing along with the depth and the plurality of lithofacies curves changing along with the transverse direction.
The prior characteristics of the logging curve values of the oil and gas reservoir parameters in the research area comprise the maximum value, the minimum value, the mean value and the variance characteristics of the oil and gas reservoir parameters in each lithofacies in the research area. The physical property parameter curve may be, for example, a porosity curve, a water saturation curve, a shale content curve, or the like.
And after the physical property parameter curve of the research area is obtained, performing lithofacies constraint statistical rock physical modeling according to the physical property parameter curve of the research area, and determining the elastic parameter curve of the research area. In specific implementation, the relation between physical property parameters and elastic parameters in different rock facies is different, and the rock physical modeling of facies constraint statistics adopts the following formula to determine the elastic parameter curve of a research area:
E＝f(R,F)+χ
wherein E represents an elasticity parameter, R represents a physical property parameter, and F represents a lithofacies;
f (-) represents a petrophysical model, which is obtained by petrophysical experiments or historical empirical relations of a research area;
and chi represents the statistical error between the rock physical model and an actual value, follows Gaussian truncation distribution, and is obtained by statistics of the error distribution characteristics between the actually measured logging curve and the simulated logging curve.
In one embodiment, the elastic parameter curves may be, for example, a compressional velocity curve, a shear velocity curve, and a density curve.
After the elastic parameter curve of the research area is determined, convolution is carried out on the Zoeppritz reflection equation and the seismic wavelets according to the elastic parameter curve of the research area, and oil and gas reservoir parameter label data are generated. The specific implementation process, as shown in fig. 4, includes:
step 401: performing convolution by using a Zoeppritz reflection equation and seismic wavelets according to an elastic parameter curve of a research area, introducing random noise with different intensities, and synthesizing a prestack seismic angle gather; the prestack seismic angle gather comprises a plurality of prestack gather samples of which the amplitudes are changed along with incident angles under different noise intensities;
step 402: extracting the lithofacies, physical parameters and elastic parameters of the samples to be used as parameter label data of the oil and gas reservoir.
The dominant frequency and the length of the adopted seismic wavelets are determined according to the dominant frequency and the thickness of a target layer of seismic data in a research area. The random noise is gaussian noise, and a plurality of random noises with different intensities are generated by using a gaussian function to simulate seismic data with different signal-to-noise ratios. The prestack seismic angle gathers are gathers of which the amplitude changes along with the angle, and the number of the angles is determined according to the angle range of prestack seismic data in a research area.
The synthesized pre-stack seismic angle trace set comprises samples of the pre-stack trace set, wherein the amplitude of the samples changes along with the incident angle under different noise intensities, and the lithofacies, the physical parameters and the elastic parameters corresponding to each sample can be used as the oil and gas reservoir parameter label data of the research area.
Further, in the specific embodiment, the expandability of the tag data can be realized by adjusting the number of curves generated by the markov chain monte carlo random simulation and the sequential gaussian simulation. The number of actually generated curves has controllability, the demand of samples containing label data is predicted according to the oil and gas reservoir parameters in an artificial intelligence mode, and the longitudinal and transverse change characteristics of oil and gas in a research area can be achieved, so that the number of the needed curves can be expanded. For example, in a research area with drastic change along with the depth, the number of curves generated by Markov chain Monte Carlo random simulation is increased; in the research area with severe change along with the transverse direction, the number of curves generated by sequential Gaussian simulation is increased.
In the specific embodiment, the value characteristics of the logging curve of the oil and gas reservoir parameters, the characteristics of seismic wavelets, lithofacies constraint statistical rock physical modeling and the angle number of the synthesized pre-stack seismic gather are adjusted according to the characteristics of different research areas, so that the generated label data can be migrated to adapt to different research areas, and the migration of the label data is realized. Specifically, the evaluation characteristics of the logging curves of the oil and gas reservoir parameters specifically refer to the estimation of a multivariate Gaussian mixture distribution function on the physical property parameter logging curves and the elastic parameter logging curves of the actual research area, and the estimated data such as the number of Gaussian components, the mean value, the variance, the proportionality coefficient and the like in the Gaussian mixture distribution function. The characteristics of the seismic wavelets, including the dominant frequency and the length of the seismic wavelets, are determined based on the dominant frequency and the thickness of the target layer of seismic data in the actual study area. Facies constraint statistics petrophysical modeling specifically refers to a petrophysical model determined by constraint statistics with facies of an actual research area. The number of angles of the synthetic prestack seismic gathers is determined from the angular range of the prestack seismic data of the actual study area.
A specific example is given below to illustrate how embodiments of the present invention perform the generation of hydrocarbon reservoir parameter tag data. This example applies to a particular area of study.
This concrete example provides a hydrocarbon reservoir parameter label data making devices, and the structure is shown as figure 5, includes:
a prior distribution calculation module 501, configured to obtain a prior distribution probability of a lithofacies in a study region by using a gaussian mixture distribution function;
a monte carlo simulation module 502, configured to generate a large number of lithofacies curves that vary with depth by using markov chain monte carlo stochastic simulation according to the prior distribution probability of the lithofacies in the research region;
a sequential gaussian simulation module 503, configured to generate a plurality of lithofacies curves that vary laterally by using sequential gaussian simulation according to the lithofacies curves that vary with depth;
the physical property filling module 504 is used for randomly filling each lithofacies according to the lithofacies curve changing along with the depth and the lithofacies curve changing along with the transverse direction by combining the reservoir parameter logging curve value prior characteristics to obtain a physical property parameter curve of the research area;
the statistical petrophysical modeling module 505 is configured to perform petrophysical modeling by using lithofacies constraint according to the physical parameter curve of the research region, and convert the physical parameter curve into an elastic parameter curve of the research region;
a pre-stack seismic angle gather synthesis module 506, configured to perform convolution according to the elastic parameter curve of the study area by using a Zoeppritz reflection equation and seismic wavelets, introduce random noise with different intensities, and synthesize a corresponding pre-stack seismic angle gather;
a training sample generation module 507, configured to use the synthetic pre-stack seismic angle gather as an observation sample set for artificial intelligent prediction of an oil and gas reservoir, where a lithofacies, a physical property parameter, and an elastic parameter corresponding to each sample in the sample set are used as tag data;
a tag data expansion module 508 for expanding the number of generated facies curves that vary with depth and facies curves that vary laterally;
and a tag data migration module 509, configured to change the reservoir parameter log value prior characteristic, the characteristic of the seismic wavelet, and select a suitable rock physical model based on lithofacies constraints, so as to migrate tag data to adapt to different research areas.
According to the device for manufacturing the oil and gas reservoir parameter tag data, the oil and gas reservoir parameter tag data is manufactured, and the process flow of the manufacturing method specifically comprises the following steps:
step S1: and performing multivariate Gaussian mixture distribution function estimation on the elastic parameter well logging curve in the research area by adopting an expected maximum estimation algorithm, estimating the number of Gaussian components, the mean value, the variance and the proportional coefficient in the Gaussian mixture distribution function, and determining the category and the prior probability of the facies divided by the elastic physical properties according to the number and the value of the estimated proportional coefficient.
FIG. 6 shows the result of Gaussian mixture distribution function estimation using elastic parameters such as compressional velocity, shear velocity, and density in an embodiment of the present invention. It can be seen that the distribution form of the elastic parameter is decomposed into 3 gaussian functions, that is, the number of components in the gaussian mixture distribution is 3, the facies representing the area is divided into 3 facies, the 3 gaussian functions (solid line, dot-dash line, and dotted line) are overlapped to form the prior distribution form of the elastic parameter, the overlapping ratio is 0.46, 0.28, and 0.26 respectively, and represents the prior probability of each facies. Therefore, in the specific example of the present invention, by combining lithology and fluid information actually existing in the research area, it can be determined that the 3 kinds of lithofacies are divided into a shale facies (solid line), a water-bearing sandstone facies (dotted line), and a gas-bearing sandstone facies (dotted line) based on the elastic parameters, and the prior probabilities respectively correspond to 0.46, 0.28, and 0.26.
Step S2: and performing multivariate Gaussian mixture distribution function estimation on the physical property parameter well logging curve of the research area by adopting an expected maximum estimation algorithm, estimating the number of Gaussian components, the mean value, the variance and the proportion coefficient in the Gaussian mixture distribution function, and determining the type and the prior probability of the lithofacies based on physical property division according to the number and the value of the estimated proportion coefficient.
FIG. 7 shows the result of Gaussian mixture distribution function estimation using physical parameters such as shale content, porosity, and gas saturation in the embodiment of the present invention. It can be seen that the distribution form of the physical property parameters is decomposed into 3 gaussian functions, that is, the number of components in the gaussian mixture distribution is 3, the lithofacies representing the area is divided into 3 lithofacies, the 3 gaussian functions (solid line, dot-dash line, and dashed line) are overlapped to form the prior distribution form of the elastic parameters, the overlapping ratio is 0.42, 0.29, and 0.29 respectively, and represents the prior probability of each lithofacies. Therefore, in the embodiment of the present invention, by combining lithology and fluid information actually existing in the research area, it can be determined that 3 kinds of lithofacies are divided into a shale facies (solid line), a water-bearing sandstone facies (dot-dash line), and a gas-bearing sandstone facies (dotted line) based on the physical property parameters, and the prior probabilities corresponding to the lithofacies are 0.42, 0.29, and 0.29, respectively.
Step S3: and combining the lithofacies types and prior probabilities based on physical properties and elastic division, and selecting the division result corresponding to the maximum number of the lithofacies types as a final lithofacies division and lithofacies prior probability result. If the two divided lithofacies types are equal, the average value of the prior probabilities of the two lithofacies is taken as the final prior probability of the lithofacies.
Since the lithofacies types based on physical properties and elastic classification are 3, in this specific example, the lithofacies classification type is 3, which is a mudstone phase, a water-bearing sandstone phase, and a gas-bearing sandstone phase, and the prior probabilities are 0.44, 0.285, and 0.275, respectively.
Step S4: and according to the prior distribution probability of the lithofacies of the research area in the step S3, generating a large number of lithofacies curves changing along with the depth by utilizing Markov chain Monte Carlo random simulation, wherein a lithofacies transition probability matrix in the Markov chain along with the depth is obtained by counting the existing logging comprehensive interpretation curve samples.
Fig. 8 shows 5 adjacent lithofacies curves as a function of depth in an embodiment of the present invention.
Step S5: and taking each generated lithofacies curve changing along with the depth as a seed sample, and generating a plurality of transverse variable lithofacies curves by utilizing a sequential Gaussian simulation technology, wherein a variation function used for simulating transverse change by the sequential Gaussian is obtained by fitting the lithofacies samples at known well points in a research area through an elliptic function.
FIG. 9 shows 5 adjacent facies curves as a function of lateral variation in an embodiment of the present invention.
Step S6: and filling all lithofacies randomly according to the lithofacies curves generated in the steps S4 and S5 and by combining the prior characteristic of the reservoir parameter logging curve value, so as to obtain a physical property parameter curve. The reservoir parameter logging value prior characteristics comprise maximum value, minimum value, mean value and variance characteristics of reservoir parameters in all lithofacies. And according to the characteristics, constructing a Gaussian mixture distribution function to randomly generate a series of reservoir parameters which accord with the prior distribution characteristics of the reservoir parameters of each lithofacies interval.
FIG. 10 shows a lithofacies curve and a filled physical property parameter curve according to an embodiment of the present invention.
Step S7: according to the physical property parameter curve generated in the step S6, adopting lithofacies constraint statistical petrophysical modeling to convert the reservoir parameter curve into a corresponding elastic parameter curve; the following formula is used for modeling:
E＝f(R,F)+χ
wherein E represents an elasticity parameter, R represents a physical property parameter, and F represents a lithofacies;
f (-) represents a petrophysical model, obtained from petrophysical experiments or empirical relations of the research area;
and chi represents the statistical error between the rock physical model and an actual value, follows Gaussian truncation distribution, and is obtained by statistics of the error distribution characteristics between the actually measured logging curve and the simulated logging curve.
FIG. 11 is a graph of elastic parameters generated by petrophysical modeling using facies-constrained statistical rock physics, according to the physical parameters of FIG. 10, in accordance with an embodiment of the present invention.
Step S8: and generating the seismic wavelets given the dominant frequency and the length of the seismic wavelets. In the embodiment of the invention, the generation is performed according to the Rake wavelet formula:
Figure BDA0002669149170000121
wherein w (t) represents a Rake wavelet; f. of0Indicates dominant frequency in hertz; t is the length of time in seconds.
Other types of seismic wavelets may be selected in this step, and embodiments of the present invention are not limited in this respect.
Step S9: and calculating a reflection coefficient according to a Zoeppritz equation by using the elastic parameters generated in the step S7, performing convolution operation on the reflection coefficient and the seismic wavelets in the step S8, and synthesizing a prestack seismic angle gather.
FIG. 12 is a synthetic prestack seismic angle gather for an embodiment of the present invention.
Step S10: gaussian noises with different intensities are generated and added into the pre-stack seismic angle gather synthesized in the step S9, and pre-stack seismic angle gathers with different signal-to-noise ratios are simulated.
FIG. 13 shows the noise-containing prestack seismic angle gathers of FIG. 12 with the signal-to-noise ratios of 10, 6, and 4, respectively.
Step S11: taking the noise-containing prestack seismic angle gather synthesized in the step S10 as a training sample, the lithofacies curves generated in the steps S4 and S5, the physical property parameter curve generated in the step S6 and the elastic parameter curve generated in the step S7 as label data, and providing deep network training learning data for intelligent prediction of reservoir parameters.
Step S12: and adjusting the number of curves generated by Markov chain Monte Carlo random simulation and sequential Gaussian simulation according to the requirements of the step S11 on the number of training samples and label data sets, thereby realizing the expandability of the label data. In a research area with violent depth change, the number of curves generated by Markov chain Monte Carlo random simulation is increased; in the research area with severe change along with the transverse direction, the number of curves generated by sequential Gaussian simulation is increased.
In the specific embodiment of the invention, 50 lithofacies curves are generated along with the change of the depth, and each lithofacies curve generates 50 curves along with the change of the transverse direction, so that 2500 lithofacies curves are simulated in a common mode.
Step S13: according to the requirement of the step S11 on the mobility of the training sample and the label data set, the well logging curves of the elastic parameters and the physical parameters in the steps S1 and S2 are changed, the rock physical model in the step S7 is changed, the dominant frequency and the duration of the wavelet in the step S8 and the angle range of the prestack seismic angle trace set in the step S9 are adjusted, and the generated training sample and the label data set are made to accord with the target research area.
In the specific embodiment of the invention, the used rock physical model is a KT model, the seismic wavelet has the dominant frequency of 35 Hz, the time duration of 0.064 seconds, and the angle range of the prestack seismic angle gather is 0-32 degrees.
The specific examples show that the device and the method for manufacturing the oil and gas reservoir parameter tag data have flexible expansibility and mobility.
The implementation of the above specific application is only an example, and the rest of the embodiments are not described in detail.
Based on the same inventive concept, embodiments of the present invention further provide a device for generating oil and gas reservoir parameter tag data, because the principle of the problem solved by the device for generating oil and gas reservoir parameter tag data is similar to the method for generating oil and gas reservoir parameter tag data, the implementation of the device for generating oil and gas reservoir parameter tag data can refer to the implementation of the method for generating oil and gas reservoir parameter tag data, the repeated parts are not repeated, and the specific structure is as shown in fig. 14:
a prior distribution calculation module 1401, configured to obtain a prior distribution probability of a lithofacies of the research region by using a gaussian mixture distribution function according to the well logging data of the research region;
a monte carlo simulation module 1402, configured to perform markov chain monte carlo stochastic simulation according to the prior distribution probability of the lithofacies in the study area to generate a plurality of lithofacies curves that vary with depth;
a sequential gaussian simulation module 1403, configured to perform sequential gaussian simulation according to the generated multiple lithofacies curves that change with depth, so as to generate multiple lithofacies curves that change with lateral direction;
a physical property filling module 1404, configured to randomly fill each lithofacies according to a plurality of lithofacies curves that change with depth, a plurality of lithofacies curves that change with lateral direction, and prior characteristics of values of the oil and gas reservoir parameter logging curve of the research area, to obtain a physical property parameter curve of the research area;
a statistical petrophysical modeling module 1405, configured to perform lithofacies constrained statistical petrophysical modeling according to the physical property parameter curve of the research region, and determine an elastic parameter curve of the research region;
and the oil and gas reservoir parameter tag data generating module 1406 is used for performing convolution on the Zoeppritz reflection equation and the seismic wavelets according to the elastic parameter curve of the research area to generate oil and gas reservoir parameter tag data.
In a specific embodiment, the prior distribution calculating module 1401, as shown in fig. 15, includes:
a proportionality coefficient determining unit 1501, configured to perform multivariate gaussian mixture distribution function estimation on the physical property parameter well-logging curve and the elastic parameter well-logging curve of the study area by using an expected maximum estimation algorithm, to obtain a proportionality coefficient in a gaussian mixture distribution function corresponding to the physical property parameter and a proportionality coefficient in a gaussian mixture distribution function corresponding to the elastic parameter;
the physical property dividing unit 1502 is configured to determine a type and a prior distribution probability of a physical property division-based lithofacies in the research region according to a proportionality coefficient in a gaussian mixture distribution function corresponding to the physical property parameter;
the elastic dividing unit 1503 is used for determining the type and the prior distribution probability of the elastic-division-based lithofacies of the research region according to the proportionality coefficient in the Gaussian mixture distribution function corresponding to the elastic parameter;
a prior distribution determining unit 1504, configured to obtain a prior distribution probability of the facies of the research region according to the type and the prior distribution probability of the facies of the research region divided based on the physical properties and the type and the prior distribution probability of the facies of the research region divided based on the elasticity.
In a specific embodiment, the sequential gaussian simulation module 1403 is specifically configured to:
taking each lithofacies curve changing along with the depth as a seed sample, and performing sequential Gaussian simulation by using a variation function to generate a plurality of lithofacies curves changing along with the transverse direction; wherein, the variation function is obtained by performing elliptic function fitting by utilizing a lithofacies sample at the well logging position of the research area.
In specific implementation, the physical property filling module 1404, as shown in fig. 16, includes:
the reservoir parameter determining unit 1601 is used for constructing a Gaussian mixture distribution function according to the prior characteristics of the logging curve values of the hydrocarbon reservoir parameters of the research area, and randomly generating a plurality of reservoir parameters which accord with the prior distribution characteristics of the hydrocarbon reservoir parameters of each lithofacies interval;
and a physical property parameter curve determining unit 1602, configured to obtain a physical property parameter curve of the research area according to the plurality of reservoir parameters, the plurality of depth-varying lithofacies curves, and the plurality of transverse-varying lithofacies curves.
In a particular embodiment, the statistical petrophysical modeling module 1405 is specifically configured to:
according to the following formula, performing lithofacies constraint statistical rock physics modeling according to the physical property parameter curve of the research region, and determining the elastic parameter curve of the research region:
E＝f(R,F)+χ
wherein E represents an elasticity parameter, R represents a physical property parameter, and F represents a lithofacies;
f (-) represents a petrophysical model, obtained from petrophysical experiments or empirical relations of the research area;
and chi represents the statistical error between the rock physical model and an actual value, follows Gaussian truncation distribution, and is obtained by statistics of the error distribution characteristics between the actually measured logging curve and the simulated logging curve.
In a specific embodiment, the hydrocarbon reservoir parameter tag data generation module 1406 is specifically configured to:
performing convolution by using a Zoeppritz reflection equation and seismic wavelets according to an elastic parameter curve of a research area, introducing random noise with different intensities, and synthesizing a prestack seismic angle gather; the prestack seismic angle gather comprises a plurality of prestack gather samples of which the amplitudes are changed along with incident angles under different noise intensities;
extracting lithofacies, physical parameters and elastic parameters of the samples to serve as parameter tag data of the oil and gas reservoir.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can be run on the processor, wherein the processor realizes the oil and gas reservoir parameter tag data generation method when executing the computer program.
The embodiment of the invention also provides a computer readable storage medium, which stores a computer program for executing the oil and gas reservoir parameter tag data generation method.
In summary, the method and the device for generating the parameter tag data of the oil and gas reservoir provided by the embodiment of the invention have the following advantages:
obtaining the prior distribution probability of the lithofacies of the research area by utilizing a Gaussian mixture distribution function according to the logging data of the research area; according to the prior distribution probability of the lithofacies of the research area, carrying out Markov chain Monte Carlo random simulation to generate a plurality of lithofacies curves changing along with the depth; performing sequential Gaussian simulation according to the generated multiple lithofacies curves changing along with the depth to generate multiple lithofacies curves changing along with the transverse direction; randomly filling each lithofacies according to the prior characteristics of values of a plurality of lithofacies curves changing along with the depth, a plurality of lithofacies curves changing along with the transverse direction and an oil and gas reservoir parameter logging curve of the research area to obtain a physical property parameter curve of the research area; according to the physical property parameter curve of the research area, performing lithofacies constraint statistical rock physical modeling, and determining an elastic parameter curve of the research area; performing convolution by using a Zoeppritz reflection equation and seismic wavelets according to an elastic parameter curve of a research area to generate oil and gas reservoir parameter label data; a plurality of lithofacies curves changing along with depth are generated through Markov chain Monte Carlo random simulation and a plurality of lithofacies curves changing along with transverse direction are generated through sequential Gaussian simulation, so that sufficient label data can be generated when the number of logging in a research area is small or the quality is poor, and the generation of the parameter label data of the oil and gas reservoir does not depend on the number and the quality of logging in the research area; by introducing prior distribution probability of lithofacies of a research area, seismic wavelets and lithofacies constraint statistical rock physical modeling in the label generation process, oil and gas reservoir parameter label data matched with the characteristics of the research area can be obtained; therefore, when the research area is replaced, the generated label data can adapt to different research areas only by adaptive adjustment according to the characteristics of the research area, a large amount of logging data and well side channel seismic data do not need to be collected again, the mobility of the generated label data is improved, and the generation efficiency of the label data is further improved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, 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 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 description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes may be made to the embodiment of the present invention by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (14)

1. A method for generating parameter tag data of an oil and gas reservoir is characterized by comprising the following steps:
obtaining the prior distribution probability of the lithofacies of the research area by utilizing a Gaussian mixture distribution function according to the logging data of the research area;
according to the prior distribution probability of the lithofacies of the research area, carrying out Markov chain Monte Carlo random simulation to generate a plurality of lithofacies curves changing along with the depth;
performing sequential Gaussian simulation according to the generated multiple lithofacies curves changing along with the depth to generate multiple lithofacies curves changing along with the transverse direction;
randomly filling each lithofacies according to the prior characteristics of values of a plurality of lithofacies curves changing along with the depth, a plurality of lithofacies curves changing along with the transverse direction and an oil and gas reservoir parameter logging curve of the research area to obtain a physical property parameter curve of the research area;
according to the physical property parameter curve of the research area, performing lithofacies constraint statistical rock physical modeling, and determining an elastic parameter curve of the research area;
and (3) performing convolution by using a Zoeppritz reflection equation and seismic wavelets according to the elastic parameter curve of the research area to generate oil and gas reservoir parameter label data.
2. The method of claim 1, wherein obtaining a prior distribution probability of facies of the region of interest from the log data of the region of interest using a gaussian mixture distribution function comprises:
performing multivariate Gaussian mixture distribution function estimation on the physical parameter logging curve and the elastic parameter logging curve of the research area by adopting an expected maximum estimation algorithm to obtain a proportionality coefficient in the Gaussian mixture distribution function corresponding to the physical parameter and a proportionality coefficient in the Gaussian mixture distribution function corresponding to the elastic parameter;
determining the type and prior distribution probability of a petrographic phase based on physical property division in a research area according to a proportionality coefficient in a Gaussian mixture distribution function corresponding to the physical property parameters;
determining the type and prior distribution probability of the lithofacies based on elastic partition in the research area according to the proportional coefficient in the Gaussian mixture distribution function corresponding to the elastic parameters;
and obtaining the prior distribution probability of the lithofacies of the research area according to the type and the prior distribution probability of the lithofacies of the research area based on physical property division and the type and the prior distribution probability of the lithofacies of the research area based on elastic division.
3. The method of claim 1, wherein performing a sequential gaussian simulation based on the plurality of depthwise-varying lithofacies curves to generate a plurality of laterally-varying lithofacies curves comprises:
taking each lithofacies curve changing along with the depth as a seed sample, and performing sequential Gaussian simulation by using a variation function to generate a plurality of lithofacies curves changing along with the transverse direction;
and the variation function is obtained by performing elliptic function fitting by using a lithofacies sample at the well logging position of the research area.
4. The method of claim 1, wherein randomly filling each facies according to a plurality of facies curves that vary with depth, a plurality of facies curves that vary laterally, and a priori characteristics of values of a hydrocarbon reservoir parameter log of the study area to obtain a physical property parameter curve of the study area comprises:
constructing a Gaussian mixture distribution function according to the prior characteristics of the logging curve values of the oil and gas reservoir parameters of the research area, and randomly generating a plurality of reservoir parameters which accord with the prior distribution characteristics of the oil and gas reservoir parameters of each lithofacies interval;
and obtaining a physical property parameter curve of the research area according to the plurality of reservoir parameters, the plurality of lithofacies curves changing along with the depth and the plurality of lithofacies curves changing along with the transverse direction.
5. The method of claim 1, wherein facies constrained statistical petrophysical modeling is performed based on the physical parameter curve of the region of interest to determine an elastic parameter curve of the region of interest according to the following formula:
E＝f(R,F)+χ
wherein E represents an elasticity parameter, R represents a physical property parameter, and F represents a lithofacies;
f (-) represents a petrophysical model, obtained from petrophysical experiments or empirical relations of the research area;
and chi represents the statistical error between the rock physical model and an actual value, follows Gaussian truncation distribution, and is obtained by statistics of the error distribution characteristics between the actually measured logging curve and the simulated logging curve.
6. The method of claim 1, wherein convolving the seismic wavelets with a Zoeppritz reflection equation to generate hydrocarbon reservoir parameter signature data based on elastic parameter curves for the region of interest comprises:
performing convolution by using a Zoeppritz reflection equation and seismic wavelets according to an elastic parameter curve of a research area, introducing random noise with different intensities, and synthesizing a prestack seismic angle gather; wherein the prestack seismic angle gather comprises a plurality of samples of the prestack gather of which the amplitude varies with the incident angle under different noise intensities;
extracting the lithofacies, physical parameters and elastic parameters of the sample to be used as parameter label data of the oil and gas reservoir.
7. A hydrocarbon reservoir parameter tag data generation device, comprising:
the prior distribution calculation module is used for obtaining the prior distribution probability of the lithofacies of the research area by utilizing a Gaussian mixture distribution function according to the logging data of the research area;
the Monte Carlo simulation module is used for carrying out Markov chain Monte Carlo random simulation according to the prior distribution probability of the lithofacies of the research area to generate a plurality of lithofacies curves changing along with the depth;
the sequential Gaussian simulation module is used for performing sequential Gaussian simulation according to the generated multiple lithofacies curves changing along with the depth to generate multiple lithofacies curves changing along with the transverse direction;
the physical property filling module is used for randomly filling each lithofacies according to the prior characteristics of values of a plurality of lithofacies curves changing along with the depth, a plurality of lithofacies curves changing along with the transverse direction and an oil and gas reservoir parameter logging curve of the research area to obtain a physical property parameter curve of the research area;
the statistical rock physics modeling module is used for carrying out lithofacies constraint statistical rock physics modeling according to the physical property parameter curve of the research area and determining an elastic parameter curve of the research area;
and the oil and gas reservoir parameter tag data generation module is used for performing convolution by utilizing a Zoeppritz reflection equation and seismic wavelets according to the elastic parameter curve of the research area to generate oil and gas reservoir parameter tag data.
8. The apparatus of claim 7, wherein the a priori distribution calculation module comprises:
the proportion coefficient determining unit is used for carrying out multivariate Gaussian mixture distribution function estimation on the physical property parameter logging curve and the elastic parameter logging curve of the research area by adopting an expected maximum estimation algorithm to obtain the proportion coefficient in the Gaussian mixture distribution function corresponding to the physical property parameter and the proportion coefficient in the Gaussian mixture distribution function corresponding to the elastic parameter;
the physical property dividing unit is used for determining the type and the prior distribution probability of the physical property division-based lithofacies in the research area according to the proportional coefficient in the Gaussian mixture distribution function corresponding to the physical property parameters;
the elastic dividing unit is used for determining the type and the prior distribution probability of the lithofacies based on the elastic division in the research area according to the proportional coefficient in the Gaussian mixture distribution function corresponding to the elastic parameters;
and the prior distribution determining unit is used for obtaining the prior distribution probability of the facies of the research area according to the type and the prior distribution probability of the facies of the research area divided based on the physical properties and the type and the prior distribution probability of the facies of the research area divided based on the elasticity.
9. The apparatus of claim 7, wherein the sequential Gaussian simulation module is specifically configured to:
taking each lithofacies curve changing along with the depth as a seed sample, and performing sequential Gaussian simulation by using a variation function to generate a plurality of lithofacies curves changing along with the transverse direction;
and the variation function is obtained by performing elliptic function fitting by using a lithofacies sample at the well logging position of the research area.
10. The apparatus of claim 7, wherein the physical property filling module comprises:
the reservoir parameter determining unit is used for constructing a Gaussian mixture distribution function according to the prior characteristics of the logging curve values of the oil and gas reservoir parameters in the research area and randomly generating a plurality of reservoir parameters which accord with the prior distribution characteristics of the oil and gas reservoir parameters in each lithofacies interval;
and the physical property parameter curve determining unit is used for obtaining a physical property parameter curve of the research area according to the plurality of reservoir parameters, the plurality of lithofacies curves changing along with the depth and the plurality of lithofacies curves changing along with the transverse direction.
11. The apparatus of claim 7, wherein the statistical petrophysical modeling module is specifically configured to:
according to the following formula, performing lithofacies constraint statistical rock physics modeling according to the physical property parameter curve of the research region, and determining the elastic parameter curve of the research region:
E＝f(R,F)+χ
wherein E represents an elasticity parameter, R represents a physical property parameter, and F represents a lithofacies;
f (-) represents a petrophysical model, obtained from petrophysical experiments or empirical relations of the research area;
and chi represents the statistical error between the rock physical model and an actual value, follows Gaussian truncation distribution, and is obtained by statistics of the error distribution characteristics between the actually measured logging curve and the simulated logging curve.
12. The apparatus of claim 7, wherein the hydrocarbon reservoir parameter tag data generation module is specifically configured to:
performing convolution by using a Zoeppritz reflection equation and seismic wavelets according to an elastic parameter curve of a research area, introducing random noise with different intensities, and synthesizing a prestack seismic angle gather; wherein the prestack seismic angle gather comprises a plurality of samples of the prestack gather of which the amplitude varies with the incident angle under different noise intensities;
extracting the lithofacies, physical parameters and elastic parameters of the sample to be used as parameter label data of the oil and gas reservoir.
13. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 6 when executing the computer program.
14. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 6.
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