CN112147677B - Method and device for generating parameter tag data of oil and gas reservoir - Google Patents

Method and device for generating parameter tag data of oil and gas reservoir Download PDF

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CN112147677B
CN112147677B CN202010928025.3A CN202010928025A CN112147677B CN 112147677 B CN112147677 B CN 112147677B CN 202010928025 A CN202010928025 A CN 202010928025A CN 112147677 B CN112147677 B CN 112147677B
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lithofacies
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parameter
curves
curve
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CN112147677A (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 label data of an oil and gas reservoir, wherein the method comprises the following steps: according to the logging data of the research area, the prior distribution probability of the lithofacies is obtained by utilizing a Gaussian mixture distribution function; performing Markov chain Monte Carlo random simulation to generate a plurality of lithofacies curves which change along with depth; performing sequential Gaussian simulation to generate a plurality of lithofacies curves which change along with the transverse direction; according to the priori characteristics of values of a plurality of lithofacies curves changing along with depth, a plurality of lithofacies curves changing along with transverse direction and an oil and gas reservoir parameter logging curve, each lithofacies is randomly filled, and a physical property parameter curve of a research area is obtained; carrying out lithofacies constraint statistical petrophysical modeling, and determining an elastic parameter curve of a research area; and carrying out convolution on the Zoeppritz reflection equation and the seismic wavelet to generate the oil and gas reservoir parameter label data. The number and the quality of the well logging in the research area are not needed to be relied on, the mobility of the generated label data is improved, and the generation efficiency is further improved.

Description

Method and device for generating parameter tag data of oil and gas reservoir
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 focus of oil and gas exploration in China is gradually shifted to lithologic oil and gas reservoir exploration. Different from the construction of hydrocarbon reservoirs, these novel reservoirs are affected by construction and reservoir heterogeneity, and the reservoir formation conditions are complex, difficult to identify, difficult to quantitatively predict, and high in investment risk. In the field of oil and gas geophysical exploration, reservoir parameters such as elastic parameters of lithofacies, longitudinal and transverse wave speeds and physical parameters of porosity, oil and gas saturation and the like can describe the elastic properties and physical properties of underground rock, and are important parameters for lithologic oil and gas reservoir prediction. Due to the complex reservoir conditions, there is a highly non-linear relationship between the seismic response characteristics and the reservoir parameters. The conventional technology for predicting reservoir parameters by using seismic data has larger errors and cannot meet the requirement of quantitative exploration. The advent of artificial intelligence technology has enabled the quantitative prediction of such complex reservoirs. In the field of oil and gas geophysical exploration, an intelligent geophysical prospecting technology is formed by combining artificial intelligence with the conventional seismic data processing and interpretation technology, so that the seismic data processing and interpretation working efficiency can be greatly improved.
The label data is the basis of the artificial intelligent supervision learning network, and the quantity and the quality of the label data directly determine the quality of the prediction result. However, in the field of oil and gas geophysical exploration, due to limited data resources, tag data are extremely lack, and development of reservoir parameter intelligent prediction technology is severely restricted. The generation of the existing oil and gas reservoir parameter tag data mainly utilizes the known logging data and the well side channel seismic data to generate tag data. However, the method can make the generation of the tag data depend on the number and quality of the well logging in the research area seriously, so that the hydrocarbon reservoir parameter tag data cannot be generated in the research area with less well logging. In addition, the generated tag data has extremely poor mobility, and often is replaced by a target research area, so that a large amount of logging data and well bypass seismic data are required to be collected again, and the generation efficiency of the oil and gas reservoir parameter tag data is quite low.
Disclosure of Invention
The embodiment of the invention provides a method for generating oil and gas reservoir parameter tag data, which is used for improving the mobility of generated tag data and improving the generation efficiency of the tag data without depending on the number and quality of logging in a research area, and comprises the following steps:
according to the logging data of the research area, the prior distribution probability of the lithofacies of the research area is obtained by utilizing a Gaussian mixture distribution function;
according to the prior distribution probability of the lithofacies in the research area, carrying out Markov chain Monte Carlo random simulation to generate a plurality of lithofacies curves which change along with the depth;
according to the generated lithofacies curves which change along with the depth, carrying out sequential Gaussian simulation to generate a plurality of lithofacies curves which change along with the transverse direction;
according to the prior characteristics of values of a plurality of lithofacies curves changing along with depth, a plurality of lithofacies curves changing along with transverse direction and an oil-gas reservoir parameter logging curve of a research area, randomly filling each lithofacies to obtain a physical property parameter curve of the research area;
according to the physical property parameter curve of the research area, rock phase constraint statistical petrophysical modeling is carried out, and an elastic parameter curve of the research area is determined;
according to the elastic parameter curve of the research area, carrying out convolution by utilizing a Zoeppritz reflection equation and the seismic wavelet to generate oil and gas reservoir parameter label data;
According to the logging data of the research area, the prior distribution probability of the lithofacies of the research area is obtained by using a Gaussian mixture distribution function, and the method comprises the following steps:
adopting an expected maximum estimation method to perform multi-element Gaussian mixture distribution function estimation on a physical property parameter logging curve and an elastic parameter logging curve of a research area 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;
determining the type and prior distribution probability of lithofacies 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 parameter;
determining the type and prior distribution probability of lithofacies based on elastic partitioning in a research area according to a proportionality coefficient in a Gaussian mixture distribution function corresponding to the elastic parameter;
obtaining the prior distribution probability of the lithofacies of the research area according to the category and prior distribution probability of the lithofacies of the research area based on physical partitioning and the category and prior distribution probability of the lithofacies of the research area based on elastic partitioning;
selecting the prior distribution probability corresponding to the rock facies with the largest category number as the prior distribution probability of the rock facies in the research area; when the types of the lithofacies of the two are equal in number, taking the average value of the prior distribution probabilities of the lithofacies of the two as the prior distribution probability of the lithofacies of the research area.
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 number and the quality of logging in a research area, and comprises the following steps:
the prior distribution calculation module is used for obtaining prior distribution probability of lithofacies of the research area by utilizing a Gaussian mixture distribution function according to 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 in the research area to generate a plurality of lithofacies curves which change along with the depth;
the sequential Gaussian simulation module is used for carrying out sequential Gaussian simulation according to the generated plurality of rock phase curves which change along with depth and generating a plurality of rock phase curves which change along with 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 depth, a plurality of lithofacies curves changing along with transverse direction and an oil-gas reservoir parameter logging curve of a research area to obtain a physical property parameter curve of the research area;
the statistical petrophysical modeling module is used for carrying out petrographic constraint statistical petrophysical modeling according to the physical parameter curve of the research area and determining the elastic parameter curve of the research area;
The oil and gas reservoir parameter label data generation module is used for generating oil and gas reservoir parameter label data by carrying out convolution on the Zoeppritz reflection equation and the seismic wavelet according to the elastic parameter curve of the research area;
the prior distribution calculation module comprises:
the proportional coefficient determining unit is used for estimating the physical property parameter logging curve and the elastic parameter logging curve of the research area by adopting an expected maximum estimation method, and obtaining the proportional coefficient in the Gaussian mixture distribution function corresponding to the physical property parameter and the proportional coefficient in the Gaussian mixture distribution function corresponding to the elastic parameter;
the physical property dividing unit is used for determining the type and priori distribution probability of lithofacies based on physical property division in the research area according to the proportionality coefficient in the Gaussian mixture distribution function corresponding to the physical property parameter;
the elastic dividing unit is used for determining the type and priori distribution probability of lithofacies based on elastic division in the research area according to the proportionality coefficient in the Gaussian mixture distribution function corresponding to the elastic parameter;
the prior distribution determining unit is used for obtaining the prior distribution probability of the lithofacies of the research area according to the type and prior distribution probability of the lithofacies of the research area based on physical property division and the type and prior distribution probability of the lithofacies of the research area based on elastic division;
Selecting the prior distribution probability corresponding to the rock facies with the largest category number as the prior distribution probability of the rock facies in the research area; when the types of the lithofacies of the two are equal in number, taking the average value of the prior distribution probabilities of the lithofacies of the two as the prior distribution probability of the lithofacies of the research area.
The embodiment of the invention also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the method for generating the oil and gas reservoir parameter label data when executing the computer program.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program for executing the oil and gas reservoir parameter label data generation method.
According to 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 in the research area, carrying out Markov chain Monte Carlo random simulation to generate a plurality of lithofacies curves which change along with the depth; according to the generated lithofacies curves which change along with the depth, carrying out sequential Gaussian simulation to generate a plurality of lithofacies curves which change along with the transverse direction; according to the prior characteristics of values of a plurality of lithofacies curves changing along with depth, a plurality of lithofacies curves changing along with transverse direction and an oil-gas reservoir parameter logging curve of a research area, randomly filling each lithofacies to obtain a physical property parameter curve of the research area; according to the physical property parameter curve of the research area, rock phase constraint statistical petrophysical modeling is carried out, and an elastic parameter curve of the research area is determined; according to the elastic parameter curve of the research area, carrying out convolution by utilizing a Zoeppritz reflection equation and the seismic wavelet to generate oil and gas reservoir parameter label data; generating a plurality of lithofacies curves which change along with depth through Markov chain Monte Carlo random simulation and a plurality of lithofacies curves which change along with transverse direction through sequential Gaussian simulation, so that sufficient tag data can be generated when the number of well logging in a research area is small or the quality is poor, and the generation of the parameter tag data of an oil and gas reservoir does not need to depend on the number and the quality of the well logging in the research area; the prior distribution probability of lithofacies of a research area, seismic wavelets and lithofacies constraint statistical petrophysical modeling are introduced in the label generation process, so that 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 tag data can be adapted to different research areas only by adaptively adjusting according to the characteristics of the research area, a large amount of logging data and well side channel seismic data are not required to be acquired again, the mobility of the generated tag data is improved, and the generation efficiency of the tag data is further improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of a method for generating parameter tag data of an oil and gas reservoir according to an embodiment of the present 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 diagram of a device for producing hydrocarbon reservoir parameter tag data in accordance with an embodiment of the present invention.
Fig. 6 is a schematic diagram of lithofacies classification results based on elastic parameters in the implementation of the present invention.
FIG. 7 is a graph showing the results of lithofacies partitioning based on physical parameters in the practice of the present invention.
Fig. 8 is a schematic representation of 5 adjacent facies curves as a function of depth in an implementation of the invention.
Fig. 9 is a schematic view of 5 adjacent lithofacies curves as a function of lateral direction in an implementation of the present invention.
FIG. 10 is a graph showing the physical properties of a lithofacies curve and a filled lithofacies curve in the practice of the present invention.
FIG. 11 is a graph showing the elastic parameters of FIG. 10 in the implementation of the present invention.
FIG. 12 is a schematic representation of a synthetic pre-stack seismic angle gather in an implementation of the invention.
FIG. 13 is a schematic representation of the noisy pre-stack seismic angle gather corresponding to FIG. 12 in an implementation of the invention.
Fig. 14 is a schematic diagram of an apparatus for generating hydrocarbon reservoir parameter tag data according to an embodiment of the present 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 diagram of a physical filling module 1404 according to an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the invention provides a method for generating oil and gas reservoir parameter tag data, which is used for improving the mobility of generated tag data and improving the generation efficiency of the tag data without depending on the number and quality of logging in a research area, and as shown in fig. 1, the method comprises the following steps:
step 101: according to the logging data of the research area, the prior distribution probability of the lithofacies of the research area is obtained by utilizing a Gaussian mixture distribution function;
step 102: according to the prior distribution probability of the lithofacies in the research area, carrying out Markov chain Monte Carlo random simulation to generate a plurality of lithofacies curves which change along with the depth;
step 103: according to the generated lithofacies curves which change along with the depth, carrying out sequential Gaussian simulation to generate a plurality of lithofacies curves which change along with the transverse direction;
step 104: according to the prior characteristics of values of a plurality of lithofacies curves changing along with depth, a plurality of lithofacies curves changing along with transverse direction and an oil-gas reservoir parameter logging curve of a research area, randomly filling each lithofacies to obtain a physical property parameter curve of the research area;
step 105: according to the physical property parameter curve of the research area, rock phase constraint statistical petrophysical modeling is carried out, and an elastic parameter curve of the research area is determined;
Step 106: and according to the elastic parameter curve of the research area, carrying out convolution by utilizing the Zoeppritz reflection equation and the seismic wavelet to generate the parameter label data of the oil and gas reservoir.
As can be seen from the flow shown in fig. 1, in the embodiment of the present invention, the prior distribution probability of the lithofacies in the research area is obtained by using a gaussian mixture distribution function according to the logging data of the research area; according to the prior distribution probability of the lithofacies in the research area, carrying out Markov chain Monte Carlo random simulation to generate a plurality of lithofacies curves which change along with the depth; according to the generated lithofacies curves which change along with the depth, carrying out sequential Gaussian simulation to generate a plurality of lithofacies curves which change along with the transverse direction; according to the prior characteristics of values of a plurality of lithofacies curves changing along with depth, a plurality of lithofacies curves changing along with transverse direction and an oil-gas reservoir parameter logging curve of a research area, randomly filling each lithofacies to obtain a physical property parameter curve of the research area; according to the physical property parameter curve of the research area, rock phase constraint statistical petrophysical modeling is carried out, and an elastic parameter curve of the research area is determined; according to the elastic parameter curve of the research area, carrying out convolution by utilizing a Zoeppritz reflection equation and the seismic wavelet to generate oil and gas reservoir parameter label data; generating a plurality of lithofacies curves which change along with depth through Markov chain Monte Carlo random simulation and a plurality of lithofacies curves which change along with transverse direction through sequential Gaussian simulation, so that sufficient tag data can be generated when the number of well logging in a research area is small or the quality is poor, and the generation of the parameter tag data of an oil and gas reservoir does not need to depend on the number and the quality of the well logging in the research area; the prior distribution probability of lithofacies of a research area, seismic wavelets and lithofacies constraint statistical petrophysical modeling are introduced in the label generation process, so that 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 tag data can be adapted to different research areas only by adaptively adjusting according to the characteristics of the research area, a large amount of logging data and well side channel seismic data are not required to be acquired again, the mobility of the generated tag data is improved, and the generation efficiency of the tag data is further improved.
In the implementation, firstly, according to the logging data of the research area, the prior distribution probability of the lithofacies of the research area is obtained by using a Gaussian mixture distribution function, and in the implementation, as shown in fig. 2, the method comprises the following steps:
step 201: adopting an expected maximum estimation method to perform multi-element Gaussian mixture distribution function estimation on a physical property parameter logging curve and an elastic parameter logging curve of a research area 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;
step 202: determining the type and prior distribution probability of lithofacies 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 parameter;
step 203: determining the type and prior distribution probability of lithofacies based on elastic partitioning in a research area according to a proportionality coefficient in a Gaussian mixture distribution function corresponding to the elastic parameter;
step 204: and obtaining the prior distribution probability of the lithofacies of the research area according to the category and prior distribution probability of the lithofacies of the research area based on physical partitioning and the category and prior distribution probability of the lithofacies of the research area based on elastic partitioning.
In a specific embodiment, the physical parameters of the research area include parameters such as oil-gas saturation, porosity, mineral content and the like of the research area, and the elastic parameters include parameters such as longitudinal wave speed, transverse wave speed, density and the like. And (3) adopting an expected maximum estimation method to perform multi-element Gaussian mixture distribution function estimation on the physical property parameter logging curve and the elastic parameter logging curve of the research area, and obtaining the Gao Sibu pieces and the proportionality coefficient in the Gaussian mixture distribution function corresponding to the physical property parameter and the Gao Sibu pieces and the proportionality coefficient in the Gaussian mixture distribution function corresponding to the elastic parameter through estimation.
The number of types of lithofacies based on physical property division in the research area is determined according to the number of Gaussian components (namely the number of proportionality coefficients) in the Gaussian mixture distribution function corresponding to the physical property parameters, and the prior distribution probability of the lithofacies based on physical property division in the research area is determined according to the value of the proportionality coefficient in the Gaussian mixture distribution function corresponding to the physical property parameters.
And determining the category number of the lithofacies based on the elastic partitioning of the research area according to the Gaussian component number (namely the number of the proportionality coefficients) in the Gaussian mixture distribution function corresponding to the elastic parameter, and determining the priori distribution probability of the lithofacies based on the elastic partitioning of the research area according to the value of the proportionality coefficient in the Gaussian mixture distribution function corresponding to the elastic parameter.
The method comprises the steps of integrating the lithofacies category and prior distribution probability based on physical property division of a research area and the lithofacies category and prior distribution probability based on elastic division of the research area, and selecting the prior distribution probability corresponding to the greatest category number of the lithofacies as the prior distribution probability of the lithofacies of the research area. When the types of the lithofacies of the two are equal in number, taking the average value of the prior distribution probabilities of the lithofacies of the two as the prior distribution probability of the lithofacies of the research area.
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 which change along with the depth. In the specific implementation, the probability that the moment t belongs to a rock facies of a certain category is assumed to be related to the probability of the rock facies category to which the moment t-1 belongs, and the change in the depth direction is regarded as transition between different rock facies categories. Defining a Markov transition probability matrix for a lithofacies downward as:
Wherein the matrix element p i,j The conditional probability of turning from facies category i to facies category j is represented:
p i,j ＝P(F j |F i )
＝P(F i |F j )P(F j )/P(F i )
in a specific embodiment, the above Markov transition probability matrix can be obtained by counting the log samples of the investigation region, and the prior distribution probability P (F t ) The prior distribution probability P (F) of the lithofacies at the time t+1 can be obtained by using the obtained Markov transition probability matrix t+1 )：
P(F t+1 )＝P(F t )P T
The prior distribution probability of the lithofacies at each moment is obtained, and a plurality of lithofacies curves which change along with the depth can be generated by using a Monte Carlo random simulation method.
After a plurality of lithofacies curves which change along with the depth are generated, sequential Gaussian simulation is carried out according to the generated plurality of lithofacies curves which change along with the depth, and a plurality of lithofacies curves which change along with the transverse direction are generated. In the specific implementation, each lithofacies curve which changes along with the depth is used as a seed sample, and a variation function is utilized to perform sequential Gaussian simulation, so that a plurality of lithofacies curves which change along with the transverse direction are generated; the variogram is obtained by carrying out elliptic function fitting by using a lithofacies sample at a logging position of a research area.
And after generating a plurality of lithofacies curves which change along with the transverse direction, randomly filling each lithofacies according to the prior characteristics of the values of 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 oil and gas reservoir parameter logging curve of the research area, and obtaining 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 priori characteristics of the values of the oil and gas reservoir parameter logging curves of the research area, and randomly generating a plurality of reservoir parameters which accord with the priori 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 reservoir parameters, the lithofacies curves changing along with the depth and the lithofacies curves changing along with the transverse direction.
The prior characteristics of the values of the logging curves of the parameters of the oil and gas reservoirs in the research area comprise the characteristics of the maximum value, the minimum value, the mean value and the variance of the parameters of the oil and gas reservoirs in each rock phase in the research area. The physical property parameter curves may be, for example, a porosity curve, a water saturation curve, a clay content curve, and the like.
And after obtaining the physical property parameter curve of the research area, carrying out litho-phase constraint statistical petrophysical modeling according to the physical property parameter curve of the research area, and determining the elasticity parameter curve of the research area. In specific implementation, the relationship between physical parameters and elastic parameters in different lithofacies is different, and the lithofacies constraint statistical petrophysical modeling adopts the following formula to determine an elastic parameter curve of a research area:
E＝f(R,F)+χ
wherein E represents an elastic parameter, R represents a physical property parameter, and F represents a lithology;
f (·) represents a petrophysical model derived from petrophysical experiments or historical empirical relationships of the study area;
and χ represents the statistical error between the petrophysical model and the actual value, obeys Gaussian cut-off distribution, and is obtained by statistics of error distribution characteristics between the actually measured logging curve and the simulated logging curve.
In a specific embodiment, the elastic parameter curve may be, for example, a longitudinal wave velocity curve, a transverse wave velocity curve, and a density curve.
After the elastic parameter curve of the research area is determined, the Zoeppritz reflection equation and the seismic wavelet are utilized to carry out convolution according to the elastic parameter curve of the research area, and the oil and gas reservoir parameter label data is generated. The specific implementation process, as shown in fig. 4, includes:
step 401: according to the elastic parameter curve of the research area, carrying out convolution by utilizing a Zoeppritz reflection equation and the seismic wavelets, introducing random noise with different intensities, and synthesizing a prestack seismic angle trace set; the pre-stack seismic angle gather comprises a plurality of samples of the pre-stack gather, wherein the amplitude of the samples varies with the incidence angle under different noise intensities;
step 402: and extracting lithofacies, physical parameters and elastic parameters of the sample, and taking the lithofacies, physical parameters and elastic parameters as oil and gas reservoir parameter label data.
The dominant frequency and length of the adopted seismic wavelets need to be determined according to the dominant frequency and the target layer thickness of the seismic data of the research area. The random noise is Gaussian noise, and a plurality of random noises with different intensities are generated by utilizing a Gaussian function to simulate seismic data with different signal to noise ratios. The prestack seismic angle trace set is a trace set with amplitude changing along with angles, and the number of angles is required to be determined according to the angle range of prestack seismic data of a research area.
The synthesized pre-stack seismic angle trace set contains samples of the pre-stack trace set with amplitude changing along with the incident angle under different noise intensities, and lithofacies, physical parameters and elastic parameters corresponding to each sample can be used as oil and gas reservoir parameter label data of a research area.
Further, in a specific embodiment, the scalability of the tag data can be achieved by adjusting the number of curves generated by markov chain monte carlo random simulation and sequential gaussian simulation. The number of the curves actually generated is controllable, the required quantity of the samples containing the label data is predicted artificially and intelligently according to the parameters of the oil and gas reservoir, and the longitudinal and transverse change characteristics of oil and gas in a research area are researched, so that the number of the needed curves can be expanded. For example, in a research area with intense variation along with depth, increasing the number of curves generated by random simulation of Markov chain Monte Carlo; in the research area with violent change along with the transverse direction, the number of curves generated by sequential Gaussian simulation is increased.
In a specific embodiment, the value characteristics of logging curves of parameters of the oil and gas reservoir, the characteristics of seismic wavelets, lithofacies constraint statistics rock physical modeling and the angle number of synthesized pre-stack seismic gathers are adjusted according to the characteristics of different research areas, so that the generated tag data can be migrated to adapt to different research areas, and the mobility of the tag data is realized. Specifically, the value characteristic of the logging curve of the oil and gas reservoir parameters specifically refers to data such as Gao Sibu pieces, mean values, variances, proportion coefficients and the like in the estimated Gaussian mixture distribution function by performing multi-element Gaussian mixture distribution function estimation on the physical property parameter logging curve and the elastic parameter logging curve of an actual research area. The characteristics of the seismic wavelet include the dominant frequency and length of the seismic wavelet, which are determined based on the dominant frequency and the thickness of the target layer of the seismic data of the actual investigation region. The rock facies constraint statistics petrophysical modeling specifically refers to a petrophysical model determined by taking rock facies of an actual research area as constraint statistics. The number of angles of the composite pre-stack seismic gather is determined from the angular range of the pre-stack seismic data of the actual investigation region.
A specific example is given below to illustrate how the embodiments of the present invention may be used to generate hydrocarbon reservoir parameter tag data. This example applies to a specific study area.
The embodiment provides an oil gas reservoir parameter label data making device, the structure is as shown in fig. 5, including:
the prior distribution calculation module 501 is configured to obtain prior distribution probability of the lithofacies in the research area by using a gaussian mixture distribution function;
the monte carlo simulation module 502 is configured to generate a large number of lithofacies curves that vary with depth by using markov chain monte carlo random simulation according to the prior distribution probability of the lithofacies in the research area;
a sequential gaussian simulation module 503, configured to generate a plurality of lithofacies curves that vary in the lateral direction according to the lithofacies curves that vary in the depth direction by using sequential gaussian simulation;
the physical property filling module 504 is configured to randomly fill each lithofacies according to the lithofacies curve varying with depth and the lithofacies curve varying with transverse direction by combining the value priori characteristics of the reservoir parameter logging curve, so as to obtain a physical property parameter curve of the research area;
the statistical petrophysical modeling module 505 is configured to convert the physical parameter curve into an elastic parameter curve of the research area by adopting petrographic constraint statistical petrophysical modeling according to the physical parameter curve of the research area;
The pre-stack seismic angle gather synthesis module 506 is configured to synthesize a corresponding pre-stack seismic angle gather by performing convolution with the seismic wavelet according to an elastic parameter curve of the study area using a Zoeppritz reflection equation and introducing random noise with different intensities;
the training sample generation module 507 is configured to use the synthetic pre-stack seismic angle gather as an observation sample set for artificial intelligent prediction of the oil and gas reservoir, and the lithology parameter, the physical property parameter and the 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 lithofacies curves varying with depth and lithofacies curves varying with lateral direction;
the tag data migration module 509 is configured to change the value priori characteristics of the reservoir parameter log, the characteristics of the seismic wavelet, and select a suitable petrophysical model based on lithofacies constraints, and migrate tag data to adapt to different research areas.
According to the device for manufacturing the parameter label data of the oil and gas reservoir, the manufacturing of the parameter label data of the oil and gas reservoir is performed, and the process flow of the manufacturing method specifically comprises the following steps:
step S1: and (3) adopting an expected maximum estimation method to carry out multi-element Gaussian mixture distribution function estimation on the elastic parameter logging curve of the research area, estimating Gao Sibu pieces of pieces, mean values, variances and proportionality coefficients in the Gaussian mixture distribution function, and determining the types and prior probabilities of lithofacies divided by the elastic physical properties according to the estimated numbers and values of the proportionality coefficients.
FIG. 6 shows the result of Gaussian mixture distribution function estimation using elastic parameters such as longitudinal wave velocity, transverse wave velocity, density, etc. in an embodiment of the 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 lithofacies representing the region is divided into 3 lithofacies, and 3 gaussian functions (solid line, dash-dot line and dotted line) are stacked together to form the prior distribution form of the elastic parameter, and the stacking proportion is 0.46, 0.28 and 0.26 respectively, which represent the prior probability of each lithofacies. Therefore, in the embodiment of the invention, by combining the lithology and the fluid information actually existing in the research area, it can be determined that the 3 lithofacies are respectively a mudstone phase (solid line), a water-containing sandstone phase (dash-dot line) and a gas-containing sandstone phase (dashed line) based on the elastic parameter, and the prior probabilities respectively corresponding to the 3 lithofacies are 0.46, 0.28 and 0.26.
Step S2: and (3) carrying out multi-element Gaussian mixture distribution function estimation on the physical property parameter logging curve of the research area by adopting an expected maximum estimation method, estimating Gao Sibu pieces of pieces, mean values, variances and proportionality coefficients in the Gaussian mixture distribution function, and determining the types and prior probabilities of lithofacies based on physical property division according to the estimated numbers and values of the proportionality coefficients.
FIG. 7 shows the results of Gaussian mixture distribution function estimation using physical parameters such as clay content, porosity, gas saturation, etc. in an embodiment of the invention. It can be seen that the distribution form of the physical parameters is decomposed into 3 gaussian functions, that is, the number of components in the gaussian mixture distribution is 3, the lithofacies representing the region is divided into 3 lithofacies, and the 3 gaussian functions (solid line, dash-dot line, and dotted line) are stacked together to form the prior distribution form of the elastic parameters, and the stacking ratios are respectively 0.42, 0.29, and 0.29, which represent the prior probability of each lithofacies. Therefore, in the embodiment of the invention, by combining the lithology and the fluid information actually existing in the research area, it can be determined that the 3 lithofacies are respectively a mudstone phase (solid line), a water-containing sandstone phase (dash-dot line) and a gas-containing sandstone phase (dashed line) based on the physical property parameters, and the prior probabilities respectively corresponding to the 3 lithofacies are 0.42, 0.29 and 0.29.
Step S3: and comprehensively dividing lithofacies types and prior probabilities based on physical properties and elasticity, and selecting the dividing result with the maximum number of lithofacies types as a final lithofacies division and lithofacies prior probability result. And if the rock facies types divided by the two are equal, taking the average value of the rock facies prior probabilities of the two as the final rock facies prior probability.
Since the lithofacies classification based on physical properties and on elastic classification are 3, the lithofacies classification in this specific example is 3, namely, a mudstone phase, a water-containing sandstone phase and a gas-containing sandstone phase, and the prior probabilities corresponding to the above are 0.44, 0.285 and 0.275, respectively.
Step S4: and (3) according to the prior distribution probability of the lithofacies of the research area in the step (S3), generating a large number of lithofacies curves which change along with the depth by utilizing Markov chain Monte Carlo random simulation, wherein a lithofacies transition probability matrix along with the depth in the Markov chain is obtained by counting the existing comprehensive well logging interpretation curve samples.
As shown in fig. 8, there are 5 adjacent lithofacies curves as a function of depth in an embodiment of the present invention.
Step S5: and taking each generated lithofacies curve which changes along with the depth as a seed sample, and generating a plurality of transverse lithofacies curves by using a sequential Gaussian simulation technology, wherein a variation function used for the sequential Gaussian simulation of the transverse variation is obtained by fitting the lithofacies sample at a known well point of a research area through an elliptic function.
Fig. 9 shows 5 adjacent facies curves as a function of lateral direction in an embodiment of the invention.
Step S6: and (3) according to the lithofacies curves generated in the steps S4 and S5, combining the value priori characteristics of the reservoir parameter logging curve, and randomly filling each lithofacies to obtain a physical parameter curve. The reservoir parameter logging value priori features comprise maximum value, minimum value, mean value and variance features of reservoir parameters in each rock phase. According to the characteristics, a Gaussian mixture distribution function is constructed to randomly generate a series of reservoir parameters which accord with reservoir parameter priori distribution characteristics 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, rock phase constraint statistical petrophysical modeling is adopted, and the reservoir parameter curve is converted into a corresponding elastic parameter curve; modeling adopts the following formula:
E＝f(R,F)+χ
wherein E represents an elastic parameter, R represents a physical property parameter, and F represents a lithology;
f (·) represents a petrophysical model, derived from petrophysical experiments or empirical relationships of the study area;
and χ represents the statistical error between the petrophysical model and the actual value, obeys Gaussian cut-off distribution, and is obtained by statistics of error distribution characteristics between the actually measured logging curve and the simulated logging curve.
FIG. 11 is a graph showing the elastic parameter curve generated by the statistical petrophysical modeling of lithofacies constraints according to the physical parameters of FIG. 10, in an embodiment of the present invention.
Step S8: and giving the main frequency and the length of the seismic wavelet to generate the seismic wavelet. In the embodiment of the invention, the method is generated according to the Rake wavelet formula:
wherein w (t) represents a Rake wavelet; f (f) 0 Representing the 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 invention are not limited in this regard.
Step S9: and (3) calculating reflection coefficients according to the Zoeppritz equation by utilizing the elastic parameters generated in the step S7, and performing convolution operation with the seismic wavelets in the step S8 to synthesize a prestack seismic angle trace set.
FIG. 12 is a set of synthetic pre-stack seismic angle traces in accordance with an embodiment of the invention.
Step S10: and (3) generating Gaussian noise with different intensities, adding the Gaussian noise into the prestack seismic angle channel sets synthesized in the step (S9), and simulating the prestack seismic angle channel sets with different signal to noise ratios.
Fig. 13 shows the noisy pre-stack seismic angle gather of fig. 12 with signal-to-noise ratios of 10, 6, and 4, respectively.
Step S11: taking the noisy pre-stack seismic angle trace set synthesized in the step S10 as a training sample, taking the lithofacies curves generated in the step S4 and the step S5, taking the physical property parameter curves generated in the step S6 and the elastic parameter curves generated in the step S7 as tag data, and providing deep network training learning data for reservoir parameter intelligent prediction.
Step S12: and (3) according to the requirements of the step S11 on the number of training samples and the number of label data sets, adjusting the number of curves generated by Markov chain Monte Carlo random simulation and sequential Gaussian simulation, and realizing the expandability of the label data. In a research area with severe change along with depth, increasing the number of curves generated by random simulation of Markov chain Monte Carlo; in the research area with violent change along with the transverse direction, the number of curves generated by sequential Gaussian simulation is increased.
In the specific example of the invention, 50 lithofacies curves are generated along with the depth change, and each lithofacies curve generates 50 curves along with the transverse change, so that 2500 lithofacies curves are simulated in total.
Step S13: according to the requirement of step S11 on mobility of the training sample and the label data set, the logging curves of the elastic parameters and the physical parameters in the steps S1 and S2 are replaced, the petrophysical model in the step S7 is changed, and the main frequency and the duration of the wavelet in the step S8 and the angle range of the pre-stack seismic angle gather in the step S9 are adjusted so that the generated training sample and the label data set accord with the target research area.
In the specific example of the invention, the rock physical model is KT model, the main frequency of the seismic wavelet is 35 Hz, the duration is 0.064 seconds, and the angle range of the pre-stack seismic angle trace set is 0-32 degrees.
The embodiment shows that the device and the method for manufacturing the parameter label data of the oil and gas reservoir 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, the embodiment of the invention also provides an oil and gas reservoir parameter tag data generating device, and because the principle of the problem solved by the oil and gas reservoir parameter tag data generating device is similar to that of the oil and gas reservoir parameter tag data generating method, the implementation of the oil and gas reservoir parameter tag data generating device can be referred to the implementation of the oil and gas reservoir parameter tag data generating method, and the repetition is omitted, and the specific structure is shown in fig. 14:
The prior distribution calculation module 1401 is configured to obtain prior distribution probability of lithofacies of the research area according to logging data of the research area by using a gaussian mixture distribution function;
the monte carlo simulation module 1402 is configured to perform markov chain monte carlo random simulation according to a priori distribution probability of lithofacies in the research area to generate a plurality of lithofacies curves that vary with depth;
the sequential gaussian simulation module 1403 is configured to perform sequential gaussian simulation according to the generated plurality of lithofacies curves that vary with depth, and generate a plurality of lithofacies curves that vary with lateral direction;
the physical property filling module 1404 is configured to randomly fill each lithofacies according to a priori characteristics of values of a plurality of lithofacies curves varying with depth, a plurality of lithofacies curves varying with transverse direction, and an oil-gas reservoir parameter logging curve of the research area, so as to obtain a physical property parameter curve of the research area;
the statistical petrophysical modeling module 1405 is configured to perform petrographic constraint statistical petrophysical modeling according to the physical parameter curve of the research area, and determine an elastic parameter curve of the research area;
the hydrocarbon reservoir parameter label data generating module 1406 is configured to generate hydrocarbon reservoir parameter label data by convolving the seismic wavelet with a Zoeppritz reflection equation according to an elastic parameter curve of the research area.
In a specific embodiment, the prior distribution calculation module 1401, as shown in fig. 15, includes:
the proportionality coefficient determining unit 1501 is configured to perform multi-element gaussian mixture distribution function estimation on a physical property parameter log curve and an elastic parameter log curve of a research area by adopting an expected maximum estimation method, so as 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;
a physical property dividing unit 1502, configured to determine a type and a priori distribution probability of lithofacies based on physical property division in a research area according to a proportionality coefficient in a gaussian mixture distribution function corresponding to a physical property parameter;
the elastic dividing unit 1503 is configured to determine a kind and a priori distribution probability of lithofacies based on elastic division in the research area according to a scaling factor in the gaussian mixture distribution function corresponding to the elastic parameter;
and the prior distribution determining unit 1504 is configured to obtain the prior distribution probability of the lithofacies of the research area according to the type and prior distribution probability of the lithofacies of the research area based on the physical property partition and the type and prior distribution probability of the lithofacies of the research area based on the elastic partition.
In a specific embodiment, the sequential gaussian analog block 1403 is specifically configured to:
Taking each lithofacies curve which changes 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 which change along with the transverse direction; the variogram is obtained by carrying out elliptic function fitting by using a lithofacies sample at a logging position of a research area.
In particular, as shown in fig. 16, the physical property filling module 1404 includes:
the reservoir parameter determining unit 1601 is configured to construct a gaussian mixture distribution function according to the prior characteristic of the value of the hydrocarbon reservoir parameter logging curve of the research area, and randomly generate a plurality of reservoir parameters conforming to the prior distribution characteristic of the hydrocarbon reservoir parameter of each lithofacies interval;
and a physical property parameter curve determining unit 1602, configured to obtain a physical property parameter curve of the study area according to the plurality of reservoir parameters, the plurality of lithofacies curves varying with depth, and the plurality of lithofacies curves varying with lateral direction.
In particular embodiments, statistical petrophysical modeling module 1405 is specifically configured to:
according to the following formula, according to the physical property parameter curve of the research area, rock phase constraint statistical petrophysical modeling is carried out, and the elasticity parameter curve of the research area is determined:
E＝f(R,F)+χ
wherein E represents an elastic parameter, R represents a physical property parameter, and F represents a lithology;
f (·) represents a petrophysical model, derived from petrophysical experiments or empirical relationships of the study area;
and χ represents the statistical error between the petrophysical model and the actual value, obeys Gaussian cut-off distribution, and is obtained by statistics of 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:
according to the elastic parameter curve of the research area, carrying out convolution by utilizing a Zoeppritz reflection equation and the seismic wavelets, introducing random noise with different intensities, and synthesizing a prestack seismic angle trace set; the pre-stack seismic angle gather comprises a plurality of samples of the pre-stack gather, wherein the amplitude of the samples varies with the incidence angle under different noise intensities;
and extracting lithofacies, physical parameters and elastic parameters of a plurality of samples to be used as oil and gas reservoir parameter label data.
The embodiment of the invention also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the method for generating the oil and gas reservoir parameter label data 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 method for generating the oil and gas reservoir parameter label data.
In summary, the method and the device for generating the parameter tag data of the oil and gas reservoir have the following advantages:
obtaining prior distribution probability of lithofacies of the research area by utilizing a Gaussian mixture distribution function according to logging data of the research area; according to the prior distribution probability of the lithofacies in the research area, carrying out Markov chain Monte Carlo random simulation to generate a plurality of lithofacies curves which change along with the depth; according to the generated lithofacies curves which change along with the depth, carrying out sequential Gaussian simulation to generate a plurality of lithofacies curves which change along with the transverse direction; according to the prior characteristics of values of a plurality of lithofacies curves changing along with depth, a plurality of lithofacies curves changing along with transverse direction and an oil-gas reservoir parameter logging curve of a research area, randomly filling each lithofacies to obtain a physical property parameter curve of the research area; according to the physical property parameter curve of the research area, rock phase constraint statistical petrophysical modeling is carried out, and an elastic parameter curve of the research area is determined; according to the elastic parameter curve of the research area, carrying out convolution by utilizing a Zoeppritz reflection equation and the seismic wavelet to generate oil and gas reservoir parameter label data; generating a plurality of lithofacies curves which change along with depth through Markov chain Monte Carlo random simulation and a plurality of lithofacies curves which change along with transverse direction through sequential Gaussian simulation, so that sufficient tag data can be generated when the number of well logging in a research area is small or the quality is poor, and the generation of the parameter tag data of an oil and gas reservoir does not need to depend on the number and the quality of the well logging in the research area; the prior distribution probability of lithofacies of a research area, seismic wavelets and lithofacies constraint statistical petrophysical modeling are introduced in the label generation process, so that 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 tag data can be adapted to different research areas only by adaptively adjusting according to the characteristics of the research area, a large amount of logging data and well side channel seismic data are not required to be acquired again, the mobility of the generated tag data is improved, and the generation efficiency of the tag data is further improved.
It will be apparent to those skilled in the art that 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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 of the preferred embodiments of the present invention and is not intended to limit the present invention, and various modifications and variations can be made to the embodiments of the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (8)

1. A method for generating hydrocarbon reservoir parameter tag data, comprising:
according to the logging data of the research area, the prior distribution probability of the lithofacies of the research area is obtained by utilizing a Gaussian mixture distribution function;
according to the prior distribution probability of the lithofacies in the research area, carrying out Markov chain Monte Carlo random simulation to generate a plurality of lithofacies curves which change along with the depth;
according to the generated lithofacies curves which change along with the depth, carrying out sequential Gaussian simulation to generate a plurality of lithofacies curves which change along with the transverse direction;
according to the prior characteristics of values of a plurality of lithofacies curves changing along with depth, a plurality of lithofacies curves changing along with transverse direction and an oil-gas reservoir parameter logging curve of a research area, randomly filling each lithofacies to obtain a physical property parameter curve of the research area; physical parameters include hydrocarbon saturation, porosity and mineral content of the investigation region;
according to the physical property parameter curve of the research area, rock phase constraint statistical petrophysical modeling is carried out, and an elastic parameter curve of the research area is determined, wherein the elastic parameters comprise longitudinal wave speed, transverse wave speed and density;
according to the elastic parameter curve of the research area, carrying out convolution by utilizing a Zoeppritz reflection equation and the seismic wavelet to generate oil and gas reservoir parameter label data; the main frequency and the length of the seismic wavelets are determined according to the main frequency and the thickness of a target layer of the seismic data of the research area;
According to the logging data of the research area, the prior distribution probability of the lithofacies of the research area is obtained by using a Gaussian mixture distribution function, and the method comprises the following steps:
adopting an expected maximum estimation method to perform multi-element Gaussian mixture distribution function estimation on a physical property parameter logging curve and an elastic parameter logging curve of a research area 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;
determining the type and prior distribution probability of lithofacies 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 parameter;
determining the type and prior distribution probability of lithofacies based on elastic partitioning in a research area according to a proportionality coefficient in a Gaussian mixture distribution function corresponding to the elastic parameter;
obtaining the prior distribution probability of the lithofacies of the research area according to the category and prior distribution probability of the lithofacies of the research area based on physical partitioning and the category and prior distribution probability of the lithofacies of the research area based on elastic partitioning;
selecting the prior distribution probability corresponding to the rock facies with the largest category number as the prior distribution probability of the rock facies in the research area; when the types of the lithofacies are equal in number, taking the average value of the prior distribution probabilities of the lithofacies as the prior distribution probability of the lithofacies in the research area;
According to the following formula, according to the physical property parameter curve of the research area, rock phase constraint statistical petrophysical modeling is carried out, and the elasticity parameter curve of the research area is determined:
E＝f(R,F)+c
wherein E represents an elastic parameter, R represents a physical property parameter, and F represents a lithology;
f (·) represents a petrophysical model, derived from petrophysical experiments or empirical relationships of the study area;
c represents the statistical error between the petrophysical model and the actual value, obeys Gaussian cut-off distribution, and is obtained by statistics of error distribution characteristics between the actually measured logging curve and the simulated logging curve;
according to the elastic parameter curve of the research area, the Zoeppritz reflection equation is utilized to carry out convolution with the seismic wavelet, and the oil and gas reservoir parameter label data is generated, which comprises the following steps:
according to the elastic parameter curve of the research area, carrying out convolution by utilizing a Zoeppritz reflection equation and the seismic wavelets, introducing random noise with different intensities, and synthesizing a prestack seismic angle trace set; wherein the pre-stack seismic angle gather comprises a plurality of samples of the pre-stack gather whose amplitudes vary with the angle of incidence at different noise intensities;
and extracting lithofacies, physical parameters and elastic parameters of the sample to be used as oil and gas reservoir parameter label data.
2. The method of claim 1, wherein performing a sequential gaussian simulation from the generated plurality of depth-dependent facies curves to generate a plurality of lateral-dependent facies curves comprises:
Taking each lithofacies curve which changes 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 which change along with the transverse direction;
the variogram is obtained by carrying out elliptic function fitting by using a lithofacies sample at a logging position of a research area.
3. The method of claim 1, wherein randomly filling each facies based on a priori characterization of values of a plurality of depth-dependent facies curves, a plurality of lateral-dependent facies curves, and a hydrocarbon reservoir parameter log of the investigation region to obtain a property parameter curve of the investigation region, comprising:
constructing a Gaussian mixture distribution function according to the priori characteristics of the values of the oil and gas reservoir parameter logging curves of the research area, and randomly generating a plurality of reservoir parameters which accord with the priori 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 reservoir parameters, the lithofacies curves changing along with the depth and the lithofacies curves changing along with the transverse direction.
4. An oil and gas reservoir parameter tag data generating device, comprising:
the prior distribution calculation module is used for obtaining prior distribution probability of lithofacies of the research area by utilizing a Gaussian mixture distribution function according to 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 in the research area to generate a plurality of lithofacies curves which change along with the depth;
the sequential Gaussian simulation module is used for carrying out sequential Gaussian simulation according to the generated plurality of rock phase curves which change along with depth and generating a plurality of rock phase curves which change along with 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 depth, a plurality of lithofacies curves changing along with transverse direction and an oil-gas reservoir parameter logging curve of the research area to obtain a physical property parameter curve of the research area, wherein the physical property parameters comprise the oil-gas saturation, the porosity and the mineral content of the research area;
the statistical petrophysical modeling module is used for carrying out petrographic constraint statistical petrophysical modeling according to the physical parameter curve of the research area, and determining an elastic parameter curve of the research area, wherein the elastic parameters comprise longitudinal wave speed, transverse wave speed and density;
the oil and gas reservoir parameter label data generation module is used for generating oil and gas reservoir parameter label data by carrying out convolution on the Zoeppritz reflection equation and the seismic wavelet according to the elastic parameter curve of the research area; the main frequency and the length of the seismic wavelets are determined according to the main frequency and the thickness of a target layer of the seismic data of the research area;
The prior distribution calculation module comprises:
the proportional coefficient determining unit is used for estimating the physical property parameter logging curve and the elastic parameter logging curve of the research area by adopting an expected maximum estimation method, and obtaining the proportional coefficient in the Gaussian mixture distribution function corresponding to the physical property parameter and the proportional coefficient in the Gaussian mixture distribution function corresponding to the elastic parameter;
the physical property dividing unit is used for determining the type and priori distribution probability of lithofacies based on physical property division in the research area according to the proportionality coefficient in the Gaussian mixture distribution function corresponding to the physical property parameter;
the elastic dividing unit is used for determining the type and priori distribution probability of lithofacies based on elastic division in the research area according to the proportionality coefficient in the Gaussian mixture distribution function corresponding to the elastic parameter;
the prior distribution determining unit is used for obtaining the prior distribution probability of the lithofacies of the research area according to the type and prior distribution probability of the lithofacies of the research area based on physical property division and the type and prior distribution probability of the lithofacies of the research area based on elastic division;
selecting the prior distribution probability corresponding to the rock facies with the largest category number as the prior distribution probability of the rock facies in the research area; when the types of the lithofacies are equal in number, taking the average value of the prior distribution probabilities of the lithofacies as the prior distribution probability of the lithofacies in the research area;
The statistical petrophysical modeling module is specifically used for:
according to the following formula, according to the physical property parameter curve of the research area, rock phase constraint statistical petrophysical modeling is carried out, and the elasticity parameter curve of the research area is determined:
E＝f(R,F)+c
wherein E represents an elastic parameter, R represents a physical property parameter, and F represents a lithology;
f (·) represents a petrophysical model, derived from petrophysical experiments or empirical relationships of the study area;
c represents the statistical error between the petrophysical model and the actual value, obeys Gaussian cut-off distribution, and is obtained by statistics of error distribution characteristics between the actually measured logging curve and the simulated logging curve;
the oil and gas reservoir parameter label data generation module is specifically used for:
according to the elastic parameter curve of the research area, carrying out convolution by utilizing a Zoeppritz reflection equation and the seismic wavelets, introducing random noise with different intensities, and synthesizing a prestack seismic angle trace set; wherein the pre-stack seismic angle gather comprises a plurality of samples of the pre-stack gather whose amplitudes vary with the angle of incidence at different noise intensities;
and extracting lithofacies, physical parameters and elastic parameters of the sample to be used as oil and gas reservoir parameter label data.
5. The apparatus of claim 4, wherein the sequential gaussian analog module is specifically configured to:
Taking each lithofacies curve which changes 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 which change along with the transverse direction;
the variogram is obtained by carrying out elliptic function fitting by using a lithofacies sample at a logging position of a research area.
6. The apparatus of claim 4, wherein the physical property filling module comprises:
the reservoir parameter determining unit is used for constructing a Gaussian mixture distribution function according to the priori characteristics of the values of the oil and gas reservoir parameter logging curves of the research area and randomly generating a plurality of reservoir parameters which accord with the priori distribution characteristics of the oil and gas reservoir parameters of each lithofacies interval;
and the physical property parameter curve determining unit is used for obtaining the physical property parameter curve of the research area according to the reservoir parameters, the lithofacies curves which change along with the depth and the lithofacies curves which change along with the transverse direction.
7. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 3 when executing the computer program.
8. 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 3.
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