CN109407150A - Based on the petrophysical shale reservoir compressibility means of interpretation of statistics and system - Google Patents
Based on the petrophysical shale reservoir compressibility means of interpretation of statistics and system Download PDFInfo
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- G01V1/301—Analysis for determining seismic cross-sections or geostructures
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Abstract
This application provides one kind based on the petrophysical shale reservoir compressibility means of interpretation of statistics and system, comprising: carries out kernel function nonparametric Multilayer networks to the log data of each lithofacies of acquisition and generates the corresponding two-dimensional probability density function of preset each earthquake combinations of attributes；Each seismic properties combination is carried out generating optimal earthquake combinations of attributes from category filter according to each two-dimensional probability density function using Bayes's classification criterion；The optimal earthquake combinations of attributes of seismic inversion generation is carried out to the seismic data of acquisition and corresponds to inverting section；Compressibility explanation is carried out to the corresponding inverting section of optimal earthquake combinations of attributes according to the corresponding two-dimensional probability density function of optimal earthquake combinations of attributes using Bayes's classification criterion.The application have take into account mineral brittleness index and elastic brittleness index respectively advantage, optimize shale compressibility evaluation criteria and improve the beneficial effect that earthquake compressibility quantifies seismic interpretation accuracy.
Description
Technical Field
The invention relates to the technical field of geophysical exploration, in particular to a shale reservoir fracturing interpretation method and system based on statistical rock physics.
Background
The shale brittleness is an important technical parameter for describing the fracturing characteristic, and the description means of the brittleness characteristic is various and can be roughly divided into three categories: hardness and strength; mass percent of brittle minerals; modulus of elasticity. Hardness and strength information of rock can provide a detailed description of brittleness, but requires elaborate experimental measurements. And the information such as the mass fraction of the brittle minerals, the elastic modulus and the like can be obtained through well logging and earthquake, so that the method is more suitable for earthquake interpretation. Brittleness Indices (BIM) based on the content of brittle minerals are proposed by Jarvie et al (2007) and Wang and Gale (2009). The brittleness of rock is related to the quartz and dolomite content, while the plasticity is related to the clay and other mineral content. Mineral content can be obtained from core analysis and well log data. The brittleness index based on the content of the brittle minerals has the advantage that the connection between brittleness and lithology can be established, so that in the case of a single mineral composition at the target horizon, the brittleness can be approximately determined by lithology interpretation. However, in addition to the mineral component, the presence of pores, fluids, etc. also has a large effect on the crushability. Thus, evaluating fracturability considering only brittle mineral content may not be efficient enough, especially where the rock microstructure is complex. A variety of elastic moduli can be used to characterize brittleness. Rickman et al (2008) propose a method for characterizing the Brittleness Index (BIE) by normalized averaging the Young's modulus and the Poisson's ratio according to their different geological effects. A high brittleness index corresponds to a high young's modulus and a low poisson's ratio. Guo et al (2012) defined the brittleness index using the lame coefficient and explored the effect of cracks and micro-formations on rock brittleness. Chen et al (2014) propose a petrophysical modeling procedure based on the ratio of young's modulus and lamel coefficient for brittleness evaluation. The elastic brittleness index has the advantage of being able to be derived from well and seismic data and is therefore more practical than the mineral brittleness index. In addition, the elastic brittleness index represents a combination of mineral composition, microstructure, and pore fluid. But has a problem in that it is difficult to have a good indication of lithology as the mineral elasticity index, since different formations of different lithology may exhibit the same elasticity parameters.
Therefore, how to provide a better seismic fracturing interpretation and evaluation method to facilitate fracturing evaluation is a technical scheme to be solved urgently at present.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a shale reservoir fracturing interpretation method and system based on statistical rock physics, which comprehensively consider the mineral components and elasticity-related brittleness index changes in the reservoir, are used for quantitatively interpreting the fracturing of different shale oil and gas reservoirs with geological characteristics, and have the beneficial effects of considering the respective advantages of the mineral brittleness index and the elasticity brittleness index, optimizing the shale fracturing evaluation standard and improving the accuracy of seismic fracturing quantitative seismic interpretation.
In order to achieve the above object, the present invention provides a shale reservoir fracturability interpretation method based on statistical petrophysics, which comprises:
performing kernel function nonparametric probability density estimation on the acquired logging data of each lithofacies to generate a two-dimensional probability density function corresponding to each preset seismic attribute combination;
self-classifying screening is carried out on each seismic attribute combination according to each two-dimensional probability density function by using a Bayesian classification rule to generate an optimal seismic attribute combination;
performing seismic inversion on the acquired seismic data to generate an inversion section corresponding to the optimal seismic attribute combination;
and carrying out fracturable interpretation on the inversion section corresponding to the optimal seismic attribute combination according to the two-dimensional probability density function corresponding to the optimal seismic attribute combination by utilizing a Bayesian classification rule.
The invention also provides a shale reservoir fracturability interpretation system based on the statistical rock physics, which comprises the following components:
the estimation unit is used for carrying out kernel function nonparametric probability density estimation on the acquired logging data of each lithofacies to generate a two-dimensional probability density function corresponding to each preset seismic attribute combination;
the classification unit is used for performing self-classification screening on each seismic attribute combination according to each two-dimensional probability density function by using Bayesian classification rules to generate an optimal seismic attribute combination;
the inversion unit is used for performing seismic inversion on the acquired seismic data to generate an inversion section corresponding to the optimal seismic attribute combination;
and the interpretation unit is used for carrying out fracturable interpretation on the inversion section corresponding to the optimal seismic attribute combination according to the two-dimensional probability density function corresponding to the optimal seismic attribute combination by utilizing Bayesian classification rules.
The invention provides a shale reservoir fracturing interpretation method and system based on statistical rock physics, which comprises the following steps: performing kernel function nonparametric probability density estimation on the acquired logging data of each lithofacies to generate a two-dimensional probability density function corresponding to each preset seismic attribute combination; self-classifying screening is carried out on each seismic attribute combination according to each two-dimensional probability density function by using a Bayesian classification rule to generate an optimal seismic attribute combination; performing seismic inversion on the acquired seismic data to generate an inversion section corresponding to the optimal seismic attribute combination; and carrying out fracturable interpretation on the inversion section corresponding to the optimal seismic attribute combination according to the two-dimensional probability density function corresponding to the optimal seismic attribute combination by utilizing a Bayesian classification rule. The method has the advantages of giving consideration to respective advantages of the mineral brittleness index and the elastic brittleness index, optimizing the shale fracturing evaluation standard and improving the accuracy of the seismic fracturing quantitative seismic interpretation.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a statistical petrophysical-based shale reservoir fracturability interpretation method of the present application;
FIG. 2 is a flow chart of a statistical petrophysical-based shale reservoir fracturability interpretation method in an embodiment of the present application;
FIG. 3 is a schematic diagram of a rock facies definition based on BIM-BIE intersection in an embodiment of the present application;
FIG. 4 is a schematic diagram of comparison of Bakcus average results with raw logging data in an embodiment of the present application;
FIGS. 5a and 5b are schematic diagrams illustrating comparison of Monte Carlo simulation results and raw logging data for a type I facies in an embodiment of the present application;
FIGS. 6a and 6b are schematic diagrams of a comparison of the relative Monte Carlo simulation results of the type II facies with the original well log data in an embodiment of the present application;
FIGS. 7a and 7b are schematic diagrams of a comparison of the relative Monte Carlo simulation results of a type III facies with the original well log data in an embodiment of the present application;
FIG. 8 is a schematic diagram of EI-AI junctions of type I facies in an embodiment of the present application;
FIG. 9a is a schematic diagram illustrating a two-dimensional probability density function corresponding to elastic wave impedance EI (30 degree) -acoustic wave impedance (AI) of each facies in an embodiment of the present application;
FIG. 9b is a schematic diagram of a two-dimensional probability density function corresponding to Young's modulus (E) -Poisson's ratio (v) of each facies in an embodiment of the present application;
FIG. 9c is a schematic representation of a two-dimensional probability density function corresponding to lamda (λ) -mu (μ) for each facies in an embodiment of the present application;
FIG. 9d is a schematic representation of a two-dimensional probability density function corresponding to lamdamho (λ ρ) -murho (μ ρ) for each facies in an embodiment of the present application;
FIG. 10 is a comparison of the classification success rate of each seismic attribute combination for each facies in an embodiment of the subject application;
FIGS. 11a and 11b are schematic inversion profiles corresponding to an optimal seismic attribute combination in an embodiment of the present application;
FIG. 12 is a schematic cross-sectional view of fracability interpretation results in an embodiment of the present application;
FIG. 13 is a schematic structural diagram of a statistical petrophysical-based shale reservoir fracturability interpretation system of the present application;
FIG. 14 is a schematic diagram of an estimation unit according to an embodiment of the present application;
fig. 15 is a schematic structural diagram of a classification unit in an embodiment of the present application.
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.
As used herein, the terms "first," "second," … …, etc. do not denote any order or order, nor are they used to limit the invention, but rather are used to distinguish one element from another element or operation described by the same technical terms.
As used herein, the terms "comprising," "including," "having," "containing," and the like are open-ended terms that mean including, but not limited to.
As used herein, "and/or" includes any and all combinations of the described items.
Aiming at the defects in the prior art, the invention provides a shale reservoir fracturability interpretation method based on statistical petrophysics, a flow chart of which is shown in figure 1, and the method comprises the following steps:
s101: and performing kernel function nonparametric probability density estimation on the acquired logging data of each lithofacies to generate a preset two-dimensional probability density function corresponding to each seismic attribute combination.
S102: and self-classifying and screening the seismic attribute combinations according to the two-dimensional probability density functions by using a Bayesian classification rule to generate optimal seismic attribute combinations.
S103: and performing seismic inversion on the acquired seismic data to generate an inversion section corresponding to the optimal seismic attribute combination.
S104: and performing fracturable interpretation on the inversion section corresponding to the optimal seismic attribute combination according to the two-dimensional probability density function corresponding to the optimal seismic attribute combination by utilizing a Bayesian classification rule.
As can be seen from the process shown in fig. 1, the present application performs kernel function nonparametric probability density estimation on the acquired logging data of each lithofacies to generate a two-dimensional probability density function corresponding to each preset seismic attribute combination; self-classifying screening is carried out on each seismic attribute combination according to each two-dimensional probability density function by using a Bayesian classification rule to generate an optimal seismic attribute combination; performing seismic inversion on the acquired seismic data to generate an inversion section corresponding to the optimal seismic attribute combination; and performing fracturable interpretation on the inversion section corresponding to the optimal seismic attribute combination according to the two-dimensional probability density function corresponding to the optimal seismic attribute combination by utilizing a Bayesian classification rule. The method can be used for quantitative interpretation of fracturing performance of different shale oil and gas reservoirs with geological characteristics so as to guide subsequent hydraulic fracturing work of a well zone. According to the method, mineral components and elasticity-related brittleness index changes in the reservoir are comprehensively considered, the shale fracturing evaluation standard is optimized, the fracturing evaluation standard is combined with the statistical rock physics explanation process, and quantitative earthquake prediction of the fracturing of the shale reservoir is achieved.
In order to make the present invention better understood by those skilled in the art, a more detailed embodiment is listed below, and as shown in fig. 2, the embodiment of the present invention provides a shale reservoir fracturability interpretation method based on statistical petrophysical, which comprises the following steps:
s201: and performing kernel function nonparametric probability density estimation on the acquired logging data of each lithofacies to generate a preset two-dimensional probability density function corresponding to each seismic attribute combination.
The step S201 specifically includes the following steps:
s301: and correcting the initial logging data of each lithofacies acquired according to the brittleness index intersection method to generate each logging data.
Specifically, based on in-zone logging data, based on Brittleness Index (BI) based on brittle mineral contentM) And Brittleness Index (BI) based on elasticity parameterE) Convergence analysis, as shown in FIG. 3, based on BIM-BIELithofacies definition of intersections with preset BIMThreshold value for sandstone shale classification while using BIEAnd classifying the earth-fracturable shale and the high-fracturable shale to obtain the lithofacies related to the fracturability and the logging data corresponding to each lithofacies. Wherein, the lithofacies includes: type I lithofacies: low fracturable shale (low BI)MAnd low BIE) Class II lithofacies: high fracturable shale (low BI)MAnd high BIE) And class III lithofacies: compacted sandstone (high BI)MAnd high BIE). The log data for each facies includes: longitudinal wave velocity, shear wave velocity, and ρ density.
Friability index (BI) based on friable mineral contentM) As shown in equation (1):
BIM＝(fQ+fCarb)/(fQ+fCarb+foth+TOC) (1)
wherein f isQIs the mass fraction of quartz, fCarbIs the mass fraction of carbonate mineral, fothIs the mass fraction of minerals except quartz, carbonate rock and TOC, which is the total organic matter content.
Brittleness Index (BI) based on elastic parametersE) As shown in equation (2):
wherein,is the Young's modulus of the polymer,is the Poisson's ratio, EmaxMaximum value of Young's modulus, EminIs the minimum value of Young's modulus, vmaxMaximum poisson ratio, vminIs the minimum Poisson ratio, Vp is the longitudinal wave velocity, Vs is the transverse wave velocity and ρ is the density.
S302: and presetting each seismic attribute combination according to each logging data.
Specifically, the preset seismic attribute combination includes: elastic wave impedance EI (30 degree) -acoustic wave impedance (AI), Young's modulus (E) -Poisson's ratio (v), lamda (lambda) -mu (mu), and lamdarho (lambda rho) -murho (mu rho), etc., and the present application is not limited thereto.
S303: and carrying out scale amplification on the logging data of each lithofacies by utilizing the Backus average to generate the amplified logging data of each lithofacies.
Specifically, the backsus average is shown in formula (3):
wherein, λ and μ are both Lame coefficients, ρ is density,in order to obtain an equivalent longitudinal wave velocity,is the equivalent shear wave velocity, p*In order to be a parameter of the density,<>the time window length needs to be determined according to the seismic wave wavelength for a weighted average within a certain time window within the window. The general practice is to use the wavelength length at the corresponding depth position as the time window length, and to set the center point of the time window at the position where the dimension coarsening needs to be performed.
And carrying out scale amplification on the logging data of each lithofacies by utilizing the Backus average to generate the amplified logging data of each lithofacies. As shown in fig. 4, the Bakcus average result is a gray line, and the raw logging data (i.e., the logging data obtained in S301) is a black line.
S304: and carrying out data volume expansion on the amplified logging data of each lithofacies by using Monte Carlo to generate expanded logging data of each lithofacies.
Specifically, the related monte carlo method is shown in formula (4):
where ω is the ω -th facies, i represents the i-th sample, Fω() Is a cumulative probability density function, x, of each lithofaciesiRepresenting random numbers from 0 to 1, and N representing the number of extension points.
And carrying out data volume expansion on the amplified logging data of each lithofacies by using Monte Carlo to generate expanded logging data of each lithofacies. As shown in fig. 5a and 5b, the data volume expansion of the compressional wave velocity Vp-shear wave velocity Vs and compressional wave velocity Vp-density of the type I lithofacies by monte carlo generates the compressional wave velocity Vp-shear wave velocity Vs and compressional wave velocity Vp-density of the expanded type I lithofacies. As shown in FIGS. 6a and 6b, the data volume expansion of the compressional wave velocity Vp-shear wave velocity Vs and compressional wave velocity Vp-density of the II-type facies by Monte Carlo generates the compressional wave velocity Vp-shear wave velocity Vs and compressional wave velocity Vp-density of the expanded II-type facies. As shown in fig. 7a and 7b, the data volume expansion of the compressional wave velocity Vp-shear wave velocity Vs and compressional wave velocity Vp-density of the class III lithofacies by monte carlo generates the compressional wave velocity Vp-shear wave velocity Vs and compressional wave velocity Vp-density of the expanded class III lithofacies.
S305: and respectively carrying out kernel function nonparametric probability density estimation on the extended logging data of each lithofacies to generate a two-dimensional probability density function corresponding to each seismic attribute combination.
Specifically, the kernel function is used for the well logging data corresponding to the smooth three types of rocks, the gaussian kernel function is used as a filtering template, and in a two-dimensional coordinate composed of a combination of seismic attributes, the gaussian kernel function value at the position of a coordinate point (i, j) is as shown in formula (5):
wherein, σ is standard deviation, (i, j) is a coordinate point in a two-dimensional coordinate composed of seismic attribute combination, i and j are positive integers, and (2k +1) × (2k +1) is the size of the filtering template.
As shown in fig. 8, a two-dimensional probability density function corresponding to the elastic wave impedance EI (30 °) -acoustic wave impedance (AI) of the type I facies is taken as an example. And (3) taking the elastic wave impedance EI (30 degrees) as an abscissa and the acoustic wave impedance (AI) as an ordinate, constructing a two-dimensional coordinate axis, and performing kernel function nonparametric probability density estimation on the extended logging data of the I-type lithofacies according to a formula (5) to generate a two-dimensional probability density function corresponding to the elastic wave impedance EI (30 degrees) -the acoustic wave impedance (AI) of the I-type lithofacies. And then, carrying out kernel function nonparametric probability density estimation on the extended logging data of the I-type facies according to a formula (5), and respectively generating a two-dimensional probability density function corresponding to the Young modulus (E) -Poisson ratio (v) of the I-type facies, a two-dimensional probability density function corresponding to lamda (lambda) -mu (mu) and a two-dimensional probability density function corresponding to lamdarho (lambda rho) -murho (mu rho). As shown in fig. 9a, 9b, 9c and 9d, a two-dimensional probability density function corresponding to the elastic wave impedance EI (30 °) -acoustic wave impedance (AI), a two-dimensional probability density function corresponding to the young modulus (E) -poisson ratio (v), a two-dimensional probability density function corresponding to lamda (λ) -mu (μ), and a two-dimensional probability density function corresponding to lamdarho (λ ρ) -murho (μ ρ) of the II-type facies are calculated, and a two-dimensional probability density function corresponding to the elastic wave impedance EI (30 DEG) -acoustic wave impedance (AI) of the class III lithofacies, a two-dimensional probability density function corresponding to the Young modulus (E) -Poisson's ratio (v), a two-dimensional probability density function corresponding to lamda (lambda) -mu (mu), and a two-dimensional probability density function corresponding to lamdarho (lambda rho) -murho (mu rho). Fig. 9a is a two-dimensional probability density function corresponding to the elastic wave impedance EI (30 °) -the acoustic wave impedance (AI) of each facies, fig. 9b is a two-dimensional probability density function corresponding to the young's modulus (E) -the poisson ratio (v) of each facies, fig. 9c is a two-dimensional probability density function corresponding to lamda (λ) -mu (μ) of each facies, and fig. 9d is a two-dimensional probability density function corresponding to lamdarho (λ ρ) -murho (μ ρ) of each facies.
S202: and self-classifying and screening the seismic attribute combinations according to the two-dimensional probability density functions by using a Bayesian classification rule to generate optimal seismic attribute combinations.
The specific step S202 includes the following steps:
s401: and generating a Bayesian classification confusion matrix according to each seismic attribute combination and the two-dimensional probability density function corresponding to each seismic attribute combination by using a Bayesian classification rule.
In specific implementation, the expression of the bayesian classification rule is shown in formula (6):
ψ＝argmax(p(r|ω)p(ω)) (6)
wherein psi is a Bayesian classification; r is any seismic attribute combination; omega is each lithofacies; p (r | ω) is a two-dimensional probability density function corresponding to each seismic attribute combination; p (ω) is a preset initial probability.
The expression of the bayesian classification confusion matrix is shown in equation (7):
wherein, CMFor Bayesian classification confusion matrices, PstThe probability of classifying the s-th lithofacies into the t-th lithofacies is shown, and s and t are positive integers; when s is t, PstAnd (5) the probability of success of classification of the s-th lithofacies.
S402: and generating the classification success rate of each seismic attribute combination to each lithofacies according to the Bayesian classification confusion matrix.
Specifically, as shown in FIG. 10, the confusion matrix C is classified according to Bayesian classificationMRespectively generating elastic wave impedance EI (30 degrees) -acoustic wave impedance (AI) to classify success rates of I type lithofacies, II type lithofacies and III type lithofacies; the classification success rate of the Young modulus (E) -Poisson ratio (v) on I type lithofacies, II type lithofacies and III type lithofacies; the success rate of the lamda (lambda) -mu (mu) for classifying the I type lithofacies, the II type lithofacies and the III type lithofacies; and the success rate of the lamdarho (lambda rho) -murho (mu rho) in classifying the I type lithofacies, the II type lithofacies and the III type lithofacies.
S403: and performing classification success rate of each seismic attribute combination on each lithofacies and generating a total classification success rate corresponding to each seismic attribute combination.
Specifically, taking the total success rate of classification of the elastic wave impedance EI (30 °) -the acoustic wave impedance (AI) as an example, as shown in fig. 10, the success rate of classification of the elastic wave impedance EI (30 °) -the acoustic wave impedance (AI) into the I-type facies is 0.95, the success rate of classification of the elastic wave impedance EI (30 °) -the acoustic wave impedance (AI) into the II-type facies is 0.78, and the success rate of classification of the elastic wave impedance EI (30 °) -the acoustic wave impedance (AI) into the III-type facies is 0.79, and the total success rate of classification of the elastic wave impedance EI (30 °) -the acoustic wave impedance (AI) is 0.95+0.78+0.79 — 2.52. By analogy, the total classification success rate of the Young modulus (E) -Poisson ratio (v), the total classification success rate of lamda (lambda) -mu (mu), and the total classification success rate of lamdarho (lambda rho) -murho (mu rho) are respectively calculated.
S404: and sequencing the classification success rates, and taking the seismic attribute combination corresponding to the highest classification success rate as the optimal seismic attribute combination.
Specifically, assuming that the total classification success rate of the elastic wave impedance EI (30 °) -acoustic wave impedance (AI) is the highest, the elastic wave impedance EI (30 °) -acoustic wave impedance (AI) is taken as the optimal seismic attribute combination.
S203: and performing seismic inversion on the acquired seismic data to generate an inversion section corresponding to the optimal seismic attribute combination.
In specific implementation, assuming that the optimal seismic attribute combination is elastic wave impedance EI (30 °) -acoustic wave impedance (AI), as shown in fig. 11a and 11b, the acquired seismic data is subjected to seismic inversion to generate an inversion section corresponding to the elastic wave impedance EI (30 °) and an inversion section corresponding to the acoustic wave impedance (AI).
S204: and performing fracturable interpretation on the inversion section corresponding to the optimal seismic attribute combination according to the two-dimensional probability density function corresponding to the optimal seismic attribute combination by utilizing a Bayesian classification rule.
In specific implementation, as shown in fig. 12, assuming that the optimal seismic attribute combination is elastic wave impedance EI (30 °) -acoustic wave impedance (AI), the fracturability interpretation is performed on the inverted profile of the elastic wave impedance EI (30 °) -acoustic wave impedance (AI) according to the two-dimensional probability density function corresponding to the elastic wave impedance EI (30 °) -acoustic wave impedance (AI) by using the bayesian classification criterion to generate a fracturability interpretation result profile.
Based on the same application concept as the shale reservoir fracturing interpretation method based on the statistical rock physics, the invention also provides a shale reservoir fracturing interpretation system based on the statistical rock physics, and the method is described in the following embodiment. Because the principle of solving the problems of the shale reservoir fracturability interpretation system based on the statistical petrophysics is similar to the shale reservoir fracturability interpretation method based on the statistical petrophysics, the implementation of the shale reservoir fracturability interpretation system based on the statistical petrophysics can refer to the implementation of the shale reservoir fracturability interpretation method based on the statistical petrophysics, and repeated parts are not repeated.
Fig. 13 is a schematic structural diagram of a statistical petrophysical-based shale reservoir fracturability interpretation system according to an embodiment of the present application, and as shown in fig. 13, the system includes: an estimation unit 101, a classification unit 102, an inversion unit 103 and an interpretation unit 104.
The estimating unit 101 is configured to perform kernel function nonparametric probability density estimation on the acquired logging data of each lithofacies to generate a preset two-dimensional probability density function corresponding to each seismic attribute combination.
And the classification unit 102 is configured to perform self-classification screening on each seismic attribute combination according to each two-dimensional probability density function by using a bayesian classification criterion to generate an optimal seismic attribute combination.
And the inversion unit 103 is used for performing seismic inversion on the acquired seismic data to generate an inversion section corresponding to the optimal seismic attribute combination.
And the interpretation unit 104 is configured to perform fracturable interpretation on the inversion profile corresponding to the optimal seismic attribute combination according to the two-dimensional probability density function corresponding to the optimal seismic attribute combination by using a bayesian classification criterion.
In one embodiment, as shown in fig. 14, the estimation unit 101 includes: the device comprises an acquisition module 201, a preset module 202, an amplification module 203, an expansion module 204 and an estimation module 205.
An obtaining module 201, configured to correct the initial logging data of each lithofacies obtained according to the brittleness index intersection method to generate each logging data.
A presetting module 202, configured to preset each seismic attribute combination according to each log data.
The amplifying module 203 is used for carrying out scale amplification on the logging data of each lithofacies by utilizing the backup average to generate the amplified logging data of each lithofacies;
the expansion module 204 is configured to perform data volume expansion on the amplified logging data of each lithofacies by using monte carlo to generate expanded logging data of each lithofacies;
and the estimation module 205 is configured to perform kernel function nonparametric probability density estimation on the extended well log data of each facies to generate a two-dimensional probability density function corresponding to each seismic attribute combination.
In one embodiment, as shown in fig. 15, the classification unit 102 includes: a classification module 301, a success rate generation module 302, a summation module 303, and a sorting module 304.
The classification module 301 is configured to generate a bayesian classification confusion matrix according to each seismic attribute combination and the two-dimensional probability density function corresponding to each seismic attribute combination by using a bayesian classification criterion;
a success rate generation module 302, configured to generate a classification success rate of each seismic attribute combination for each lithofacies according to a bayesian classification confusion matrix;
the summing module 303 is configured to perform classification success rates of each seismic attribute combination on each lithofacies and generate a total classification success rate corresponding to each seismic attribute combination;
and the sorting module 304 is used for sorting the total classification success rates and taking the seismic attribute combination corresponding to the highest total classification success rate as the optimal seismic attribute combination.
The invention provides a shale reservoir fracturing interpretation method and system based on statistical rock physics, which comprises the following steps: performing kernel function nonparametric probability density estimation on the acquired logging data of each lithofacies to generate a two-dimensional probability density function corresponding to each preset seismic attribute combination; self-classifying screening is carried out on each seismic attribute combination according to each two-dimensional probability density function by using a Bayesian classification rule to generate an optimal seismic attribute combination; performing seismic inversion on the acquired seismic data to generate an inversion section corresponding to the optimal seismic attribute combination; and performing fracturable interpretation on the inversion section corresponding to the optimal seismic attribute combination according to the two-dimensional probability density function corresponding to the optimal seismic attribute combination by utilizing a Bayesian classification rule. According to the method, mineral components and elasticity-related brittleness index changes in the reservoir are comprehensively considered, the shale fracturing evaluation standard is optimized, the fracturing evaluation standard is combined with the statistical rock physics explanation process, and quantitative earthquake prediction of the fracturing of the shale reservoir is achieved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A shale reservoir fracturing interpretation method based on statistical rock physics is characterized by comprising the following steps:
performing kernel function nonparametric probability density estimation on the acquired logging data of each lithofacies to generate a two-dimensional probability density function corresponding to each preset seismic attribute combination;
self-classifying screening is carried out on each seismic attribute combination according to each two-dimensional probability density function by using a Bayesian classification rule to generate an optimal seismic attribute combination;
performing seismic inversion on the acquired seismic data to generate an inversion section corresponding to the optimal seismic attribute combination;
and carrying out fracturable interpretation on the inversion section corresponding to the optimal seismic attribute combination according to the two-dimensional probability density function corresponding to the optimal seismic attribute combination by utilizing a Bayesian classification rule.
2. The statistical petrophysical-based shale reservoir fracturability interpretation method of claim 1, wherein said performing a kernel function nonparametric probability density estimation on the obtained log data of each lithofacies generates a two-dimensional probability density function corresponding to each preset seismic attribute combination, comprising:
correcting the initial logging data of each lithofacies acquired according to the brittleness index intersection method to generate each logging data;
presetting each seismic attribute combination according to each logging data;
carrying out scale amplification on the logging data of each lithofacies by utilizing the Backus average to generate amplified logging data of each lithofacies;
carrying out data volume expansion on the amplified logging data of each lithofacies by using Monte Carlo to generate expanded logging data of each lithofacies;
and performing kernel function nonparametric probability density estimation on the extended well logging data of each lithofacies respectively to generate a two-dimensional probability density function corresponding to each seismic attribute combination.
3. The statistical petrophysical-based shale reservoir fracturability interpretation method of claim 1, wherein said self-classification screening each said seismic attribute combination according to each said two-dimensional probability density function using bayesian classification criterion to generate an optimal seismic attribute combination comprises:
generating a Bayesian classification confusion matrix according to each seismic attribute combination and the two-dimensional probability density function corresponding to each seismic attribute combination by using a Bayesian classification rule;
generating the classification success rate of each seismic attribute combination to each lithofacies according to the Bayesian classification confusion matrix;
performing classification success rate of each seismic attribute combination on each lithofacies to generate a total classification success rate corresponding to each seismic attribute combination;
and sequencing the total classification success rates and taking the seismic attribute combination corresponding to the highest total classification success rate as the optimal seismic attribute combination.
4. The statistical petrophysical-based shale reservoir fracturability interpretation method of claim 1, wherein said facies comprises: low fracturable shale, high fracturable shale, and tight sandstone.
5. The statistical petrophysical-based shale reservoir fracturability interpretation method of claim 1, wherein said combination of seismic attributes comprises: elastic wave impedance-acoustic wave impedance, Young's modulus-Poisson's ratio, lamda-mu, and lamdarho-murho.
6. The shale reservoir fracturability interpretation method based on statistical petrophysics according to claim 3, wherein the expression of the Bayesian classification criterion is as follows:
ψ＝argmax(p(r|ω)p(ω))
and psi is a Bayesian classification result, r is any one seismic attribute combination, omega is each lithofacies, p (r | omega) is a two-dimensional probability density function corresponding to the seismic attribute combination, and p (omega) is a preset initial probability.
7. The shale reservoir fracturability interpretation method based on statistical petrophysics according to claim 6, wherein the expression of the Bayesian classification confusion matrix is as follows:
wherein, CMFor the Bayesian classification confusion matrix, PstThe probability of classifying the s-th lithofacies into the t-th lithofacies is shown, and s and t are positive integers; when s is t, PstAnd (5) the probability of success of classification of the s-th lithofacies.
8. A shale reservoir fracturability interpretation system based on statistical petrophysics, comprising:
the estimation unit is used for carrying out kernel function nonparametric probability density estimation on the acquired logging data of each lithofacies to generate a two-dimensional probability density function corresponding to each preset seismic attribute combination;
the classification unit is used for performing self-classification screening on each seismic attribute combination according to each two-dimensional probability density function by using Bayesian classification rules to generate an optimal seismic attribute combination;
the inversion unit is used for performing seismic inversion on the acquired seismic data to generate an inversion section corresponding to the optimal seismic attribute combination;
and the interpretation unit is used for carrying out fracturable interpretation on the inversion section corresponding to the optimal seismic attribute combination according to the two-dimensional probability density function corresponding to the optimal seismic attribute combination by utilizing Bayesian classification rules.
9. The statistical petrophysical-based shale reservoir fracturability interpretation system of claim 8, said estimation unit comprising:
the acquisition module is used for correcting the initial logging data of each lithofacies acquired according to the brittleness index intersection method to generate each logging data;
the presetting module is used for presetting each seismic attribute combination according to each logging data;
the amplification module is used for carrying out scale amplification on the logging data of each lithofacies by utilizing the backup average to generate the amplified logging data of each lithofacies;
the expansion module is used for carrying out data volume expansion on the amplified logging data of each lithofacies by using Monte Carlo to generate the expanded logging data of each lithofacies;
and the estimation module is used for respectively carrying out kernel function nonparametric probability density estimation on the extended logging data of each lithofacies to generate a two-dimensional probability density function corresponding to each seismic attribute combination.
10. The statistical petrophysical-based shale reservoir fracturability interpretation system of claim 8, said classification unit comprising:
the classification module is used for generating a Bayesian classification confusion matrix according to each seismic attribute combination and the two-dimensional probability density function corresponding to each seismic attribute combination by utilizing a Bayesian classification rule;
the success rate generation module is used for generating the classification success rate of each seismic attribute combination to each lithofacies according to the Bayesian classification confusion matrix;
the summation module is used for making the classification success rate of each seismic attribute combination on each lithofacies and generating a total classification success rate corresponding to each seismic attribute combination;
and the sequencing module is used for sequencing the total classification success rates and taking the seismic attribute combination corresponding to the highest total classification success rate as the optimal seismic attribute combination.
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