CN109407150A - Based on the petrophysical shale reservoir compressibility means of interpretation of statistics and system - Google Patents

Abstract

The present application provides a method and system for interpreting the fractability of a shale reservoir based on statistical petrophysics, including: performing a kernel function non-parametric probability density estimation on the acquired logging data of each lithofacies to generate preset seismic The two-dimensional probability density function corresponding to the attribute combination; the Bayesian classification criterion is used to perform self-classification and screening on each of the seismic attribute combinations according to each of the two-dimensional probability density functions to generate the optimal seismic attribute combination; The inversion profile corresponding to the optimal seismic attribute combination is generated by inversion; the fractability of the inversion profile corresponding to the optimal seismic attribute combination is interpreted according to the two-dimensional probability density function corresponding to the optimal seismic attribute combination using the Bayesian classification criterion. The present application has the beneficial effects of taking into account the respective advantages of mineral brittleness index and elastic brittleness index, optimizing the evaluation standard of shale fractability, and improving the accuracy of quantitative seismic interpretation of seismic fractability.

Description

A method and system for interpreting fractability of shale reservoirs based on statistical petrophysics

technical field

The invention relates to the technical field of geophysical exploration, in particular to a method and system for interpreting fractability of shale reservoirs based on statistical petrophysics.

Background technique

Shale brittleness is an important technical parameter to describe its fractability characteristics. There are various means of describing brittleness characteristics, which can be roughly divided into three categories: hardness and strength; percentage of brittle mineral content; elastic modulus. Rock hardness and strength information can provide a detailed description of brittleness, but requires sophisticated experimental measurements. The information such as brittle mineral content and elastic modulus can be obtained by logging and seismic, so it is more suitable for seismic interpretation. The brittleness index (BIM) based on brittle mineral content was proposed by Jarvie et al. (2007) and Wang and Gale (2009). The brittleness of rocks is related to the content of quartz and dolomite, while the plasticity is related to the content of clay and other minerals. Mineral content can be obtained from core analysis and well log data. The advantage of the brittleness index based on the content of brittle minerals is that it can establish the relationship between brittleness and lithology, so in the case of a single mineral composition in the target horizon, the brittleness can be approximately determined by lithology interpretation. However, in addition to mineral components, the presence of pores and fluids also has a great impact on fractability. Therefore, only considering the brittle mineral content to assess fractability may not be effective, especially when the rock microstructure is complex. Various elastic moduli can be used to characterize brittleness. Rickman et al. (2008) proposed a brittleness index (BIE) based on the different geological effects of Young's modulus and Poisson's ratio, which were normalized and averaged. A high brittleness index corresponds to a high Young's modulus and a low Poisson's ratio. Guo et al. (2012) used the Lame coefficient to define the brittleness index and explored the effect of fractures and microstructures on rock brittleness. Chen et al. (2014) proposed a petrophysical modeling process based on the ratio of Young's modulus and Lame coefficient for brittleness assessment. The elastic brittleness index has the advantage that it can be derived from well and seismic data and is therefore more practical than the mineral brittleness index. In addition, the elastic brittleness index represents the combined effect of mineral composition, microstructure and pore fluids. But the problem is that it is difficult to have a good indication of lithology like the mineral elasticity index, because different formations with different lithologies may show the same elastic parameters.

Therefore, how to provide a better interpretation and evaluation method of seismic fractability to facilitate the evaluation of fractability is a technical solution that needs to be solved urgently.

SUMMARY OF THE INVENTION

In order to solve the defects in the prior art, the present invention provides a method and system for interpreting the fractability of shale reservoirs based on statistical petrophysics. For the quantitative interpretation of fractability of shale oil and gas reservoirs with different geological characteristics, it has the advantages of taking into account the respective advantages of mineral brittleness index and elastic brittleness index, optimizing the evaluation standard of shale fractability and improving the quantitative seismic fractability of seismic Explain the beneficial effects of accuracy.

In order to achieve the above objects, the present invention provides a method for interpreting the fractability of shale reservoirs based on statistical petrophysics, the method comprising:

Performing non-parametric probability density estimation of kernel function on the acquired logging data of each lithofacies to generate a two-dimensional probability density function corresponding to each preset combination of seismic attributes;

Using Bayesian classification criteria to perform self-classification and screening on each of the seismic attribute combinations according to each of the two-dimensional probability density functions to generate an optimal seismic attribute combination;

Perform seismic inversion on the acquired seismic data to generate an inversion profile corresponding to the optimal seismic attribute combination;

The inversion profile corresponding to the optimal seismic attribute combination is interpreted for fractability according to the two-dimensional probability density function corresponding to the optimal seismic attribute combination using Bayesian classification criteria.

The present invention also provides a shale reservoir fractability interpretation system based on statistical petrophysics, the system comprising:

an estimation unit, configured to perform kernel function non-parametric probability density estimation on the acquired logging data of each lithofacies to generate a two-dimensional probability density function corresponding to each preset combination of seismic attributes;

A classification unit, configured to perform self-classification and screening on each of the seismic attribute combinations according to each of the two-dimensional probability density functions using Bayesian classification criteria to generate an optimal seismic attribute combination;

an inversion unit, configured to perform seismic inversion on the acquired seismic data to generate an inversion profile corresponding to the optimal seismic attribute combination;

An interpretation unit, configured to interpret 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 the Bayesian classification criterion.

The invention provides a method and system for interpreting the fractability of shale reservoirs based on statistical petrophysics, including: performing a kernel function non-parametric probability density estimation on the acquired logging data of each lithofacies to generate preset seismic The two-dimensional probability density function corresponding to the attribute combination; the Bayesian classification criterion is used to perform self-classification and screening on each of the seismic attribute combinations according to each of the two-dimensional probability density functions to generate the optimal seismic attribute combination; Inversion to generate the inversion profile corresponding to the optimal seismic attribute combination; using the Bayesian classification criterion to perform 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 Fracability interpretation. The present application has the beneficial effects of taking into account the respective advantages of mineral brittleness index and elastic brittleness index, optimizing the evaluation standard of shale fractability, and improving the accuracy of quantitative seismic interpretation of seismic fractability.

Description of drawings

In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings without creative efforts.

1 is a flow chart of a method for interpreting fractability of shale reservoirs based on statistical petrophysics of the present application;

2 is a flow chart of a method for explaining fractability of shale reservoirs based on statistical petrophysics in an embodiment of the present application;

3 is a schematic diagram of a petrographic definition based on BIM-BIE intersection in an embodiment of the present application;

FIG. 4 is a schematic diagram of the comparison between the Bakcus average result and the original logging data in an embodiment of the present application;

Fig. 5a and Fig. 5b are schematic diagrams showing the comparison between the relevant Monte Carlo simulation results of the I-type lithofacies and the original logging data in an embodiment of the present application;

6a and 6b are schematic diagrams showing the comparison between the related Monte Carlo simulation results of type II lithofacies and original logging data in an embodiment of the present application;

FIG. 7a and FIG. 7b are schematic diagrams showing the comparison between the related Monte Carlo simulation results of the Class III lithofacies and the original logging data in an embodiment of the present application;

8 is a schematic diagram of the EI-AI intersection of Type I lithofacies in an embodiment of the present application;

9a is a schematic diagram of a two-dimensional probability density function corresponding to the elastic wave impedance EI (30°)-acoustic wave impedance (AI) of each lithofacies in an embodiment of the present application;

Figure 9b is a schematic diagram of a two-dimensional probability density function corresponding to the Young's modulus (E)-Poisson's ratio (v) of each lithofacies in an embodiment of the present application;

Fig. 9c is a schematic diagram of a two-dimensional probability density function corresponding to lamda(λ)-mu(μ) of each lithofacies in an embodiment of the present application;

9d is a schematic diagram of a two-dimensional probability density function corresponding to lamdarho(λρ)-murho(μρ) of each lithofacies in an embodiment of the present application;

10 is a comparison diagram of the success rate of classification of various types of lithofacies for each seismic attribute combination in an embodiment of the present application;

11a and 11b are schematic diagrams of inversion profiles corresponding to the optimal seismic attribute combination in an embodiment of the present application;

FIG. 12 is a schematic cross-sectional view of the interpretation result of fractability in an embodiment of the present application;

13 is a schematic structural diagram of a shale reservoir fractability interpretation system based on statistical petrophysics of the present application;

14 is a schematic structural diagram of an estimation unit in 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 ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

Regarding the "first", "second", ... etc. used in this document, it does not specifically refer to the order or order, nor is it used to limit the present invention, it is only used to distinguish elements described in the same technical terms or operate.

As used herein, "comprising," "including," "having," "containing," and the like, are open-ended terms, meaning including but not limited to.

As used herein, "and/or" includes any and all combinations of the stated things.

In view of the defects existing in the prior art, the present invention provides a method for interpreting the fractability of shale reservoirs based on statistical petrophysics, the flowchart of which is shown in Figure 1, and the method includes:

S101: Perform a non-parametric probability density estimation of a kernel function on the acquired logging data of each lithofacies to generate a two-dimensional probability density function corresponding to each preset combination of seismic attributes.

S102: Use Bayesian classification criteria to perform self-classification screening on each of the seismic attribute combinations according to each of the two-dimensional probability density functions to generate an optimal seismic attribute combination.

S103: Perform seismic inversion on the acquired seismic data to generate an inversion profile corresponding to the optimal seismic attribute combination.

S104: Using Bayesian classification criteria to interpret the fractability of 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.

It can be seen from the process shown in FIG. 1 that the present application performs non-parametric probability density estimation of kernel function on the acquired logging data of each lithofacies to generate a two-dimensional probability density function corresponding to each preset combination of seismic attributes; Bayesian classification is used. The criterion is to perform self-classification and screening on each of the seismic attribute combinations according to each of the two-dimensional probability density functions to generate an optimal seismic attribute combination; perform seismic inversion on the acquired seismic data to generate an inversion profile corresponding to the optimal seismic attribute combination; According to the two-dimensional probability density function corresponding to the optimal seismic attribute combination, the Bayesian classification criterion interprets the fractability of the inversion profile corresponding to the optimal seismic attribute combination. The present application can be used for quantitative interpretation of the fractability of shale oil and gas reservoirs with different geological characteristics, so as to guide the subsequent hydraulic fracturing work in the well area. This application comprehensively considers the changes of mineral components and elasticity-related brittleness index in the reservoir, optimizes the evaluation criteria for shale fractability, and combines the evaluation criteria for fractability with the statistical petrophysical interpretation process to realize shale reservoirs. Quantitative seismic prediction of layer fractability.

In order for those skilled in the art to better understand the present invention, a more detailed embodiment is listed below. As shown in FIG. 2 , the embodiment of the present invention provides a shale reservoir fractability based on statistical petrophysics Explain the method, which includes the following steps:

S201: Perform non-parametric probability density estimation on the acquired logging data of each lithofacies to generate a two-dimensional probability density function corresponding to each preset combination of seismic attributes.

The specific execution of step S201 includes the following steps:

S301: Correct the initial logging data of each lithofacies obtained according to the brittleness index intersection method to generate each logging data.

Specifically, based on the logging data in the work area, according to the intersection analysis of the brittle mineral content-based brittleness index ( _BIM ) and the elastic parameter-based brittleness index ( _{BIE )} , as shown in Figure 3, based on the BIM- _BIE intersection analysis Lithofacies definition Use preset BIM thresholds to classify sandstone shale, and use BIE to classify _geofractable shale and high _fractability shale to obtain fractability-related lithofacies and lithofacies Corresponding logging data. Among them, lithofacies include: Type I lithofacies: low fracturability shale (low BIM and low BI _E ), Type II lithofacies: high _fractability shale (low _BIM and high BI _E ) and Type III lithofacies: tight sandstones (high _BIM and high BI _E ). The logging data of each lithofacies include: longitudinal wave velocity, shear wave velocity and ρ density.

The brittleness index ( _BIM ) based on the brittle mineral content is given by equation (1):

BIM = (f _Q +f _Carb )/(f _Q +f _Carb + _{foth +} TOC) (1)

Among them, f _Q is the mass fraction of quartz, f _Carb is the mass fraction of carbonate minerals, f _oth is the mass fraction of minerals except quartz, carbonate rocks and TOC, and TOC is the total organic matter content.

The brittleness index (BI _E ) based on elastic parameters is shown in formula (2):

in, is Young's modulus, is the Poisson’s ratio, E _max is the maximum value of Young’s modulus, E _min is the minimum value of Young’s modulus, ν _max is the maximum Poisson’s ratio, ν _min is the minimum value of Poisson’s ratio, Vp is the longitudinal wave velocity, Vs is the shear wave velocity and ρ is the density.

S302: Preset each seismic attribute combination according to each well logging data.

Specifically, the preset seismic attribute combination includes: elastic wave impedance EI (30°) - acoustic wave impedance (AI), Young's modulus (E) - Poisson's ratio (v), lamda (λ) - mu (μ) and lamdarho(λρ)-murho(μρ), etc., which are not limited in this application.

S303: Use Backus average to amplify the logging data of each lithofacies to generate the amplified logging data of each lithofacies.

Specifically, the Backus average is shown in formula (3):

Among them, λ and μ are Lame coefficients, ρ is the density, is the equivalent longitudinal wave velocity, is the equivalent shear wave velocity, ρ ^* is the density parameter, <> is the weighted average within a certain time window within the window, and the length of the time window needs to be determined according to the wavelength of the seismic wave. The general practice is to take the wavelength length at the corresponding depth position as the length of the time window, and set the center point of the time window at the position where scale coarsening needs to be carried out.

The logging data of each lithofacies is scaled up by 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 original logging data (ie, the logging data obtained in S301) is a black line.

S304: Use Monte Carlo to expand the data volume of the amplified logging data of each lithofacies to generate expanded logging data of each lithofacies.

Specifically, the relevant Monte Carlo method is shown in formula (4):

Among them, ω is the ω-th lithofacies, i represents the i-th sampling, F _ω () is the cumulative probability density function of each lithofacies, _xi represents a random number from 0 to 1, and N represents the number of expansion points.

The enlarged logging data of each lithofacies is expanded by Monte Carlo to generate the expanded logging data of each lithofacies. As shown in Fig. 5a and Fig. 5b, the data volume of the compressional wave velocity Vp-shear wave velocity Vs and the compressional wave velocity Vp-density of the type I lithofacies are expanded by Monte Carlo to generate the expanded compressional wave velocity Vp-shear wave velocity of the type I lithofacies Vs and longitudinal wave velocity Vp-density. As shown in Fig. 6a and Fig. 6b, the data volume of the compressional wave velocity Vp-shear wave velocity Vs and the compressional wave velocity Vp-density of the type II lithofacies are expanded by Monte Carlo to generate the expanded compressional wave velocity Vp-shear wave velocity of the type II lithofacies Vs and longitudinal wave velocity Vp-density. As shown in Fig. 7a and Fig. 7b, using Monte Carlo to expand the data volume of the compressional wave velocity Vp-shear wave velocity Vs and compressional wave velocity Vp-density of the III lithofacies to generate the expanded compressional wave velocity Vp-shear wave velocity of the III lithofacies Vs and longitudinal wave velocity Vp-density.

S305: Perform kernel function non-parametric probability density estimation on the extended logging data of each lithofacies, respectively, to generate a two-dimensional probability density function corresponding to each seismic attribute combination.

Specifically, the kernel function is used for the logging data corresponding to the three types of smooth rocks, and the Gaussian kernel function is used as a filter template. The function value is shown in formula (5):

Among them, σ is the standard deviation, (i, j) is the coordinate point in the two-dimensional coordinate composed of seismic attribute combination, i and j are positive integers, (2k+1)*(2k+1) is the filter template size .

As shown in Fig. 8, the two-dimensional probability density function corresponding to the elastic wave impedance EI(30°)-acoustic wave impedance (AI) of the I-type lithofacies is taken as an example. Taking the elastic wave impedance EI (30°) as the abscissa and the acoustic wave impedance (AI) as the ordinate, a two-dimensional coordinate axis is constructed, and the non-parametric probability density of the kernel function is performed on the extended logging data of the I-type lithofacies according to formula (5). If estimated, the two-dimensional probability density function corresponding to the elastic wave impedance EI(30°)-acoustic wave impedance (AI) of the I-type lithofacies is generated. Then, according to formula (5), the non-parametric probability density estimation of kernel function is performed on the extended logging data of type I lithofacies, and the two-dimensional values corresponding to Young's modulus (E) - Poisson's ratio (v) of type I lithofacies are generated respectively. The probability density function, the two-dimensional probability density function corresponding to lamda(λ)-mu(μ), and the two-dimensional probability density function corresponding to lamdarho(λρ)-murho(μρ). As shown in Fig. 9a, Fig. 9b, Fig. 9c and Fig. 9d, by analogy, the two-dimensional probability density function corresponding to the elastic wave impedance EI(30°)-acoustic wave impedance (AI) of type II lithofacies, Young's The two-dimensional probability density function corresponding to modulus (E)-Poisson's ratio (v), the two-dimensional probability density function corresponding to lamda(λ)-mu(μ), and the two-dimensional probability density function corresponding to lamdarho(λρ)-murho(μρ) The probability density function, and the two-dimensional probability density function corresponding to the elastic wave impedance EI(30°)-acoustic wave impedance (AI) of the III lithofacies, and the two-dimensional probability density function corresponding to the Young's modulus (E)-Poisson's ratio (v) The probability density function, the two-dimensional probability density function corresponding to lamda(λ)-mu(μ), and the two-dimensional probability density function corresponding to lamdarho(λρ)-murho(μρ). Among them, Fig. 9a is the two-dimensional probability density function corresponding to the elastic wave impedance EI(30°)-acoustic wave impedance (AI) of each lithofacies, and Fig. 9b is the Young's modulus (E)-Poisson's ratio ( v) The corresponding two-dimensional probability density function, Fig. 9c is the two-dimensional probability density function corresponding to lamda(λ)-mu(μ) of each lithofacies, Fig. 9d is the lamdarho(λρ)-murho(μρ) of each lithofacies The corresponding two-dimensional probability density function.

S202: Use Bayesian classification criteria to perform self-classification screening on each of the seismic attribute combinations according to each of the two-dimensional probability density functions to generate an optimal seismic attribute combination.

The specific step S202 includes the following steps when executed:

S401: Generate a Bayesian classification confusion matrix according to each seismic attribute combination and a two-dimensional probability density function corresponding to each seismic attribute combination by using a Bayesian classification criterion.

In specific implementation, the expression of the Bayesian classification criterion is shown in formula (6):

ψ=argmax(p(r|ω)p(ω)) (6)

Among them, ψ is the Bayesian classification; r is any combination of seismic attributes; ω is each lithofacies; p(r|ω) is the two-dimensional probability density function corresponding to each combination of seismic attributes; p(ω) is a preset initial probability.

The expression of the Bayesian classification confusion matrix is shown in formula (7):

Among them, C _M is the Bayesian classification confusion matrix, P _st is the probability that the s-th lithofacies is classified into the t-th lithofacies, and both s and t are positive integers; when s=t, P _st is the s-th lithofacies Probability of successful lithofacies classification.

S402: Generate a classification success rate of each lithofacies for each seismic attribute combination according to the Bayesian classification confusion matrix.

Specifically, as shown in Fig. 10, according to the Bayesian classification confusion matrix _CM , the elastic wave impedance EI(30°)-acoustic wave impedance (AI) of the type I lithofacies, the type II lithofacies and the type III lithofacies are generated respectively. Classification success rate; Young's modulus (E)-Poisson's ratio (v) for the classification success rate of I-type lithofacies, II-type lithofacies and III-type lithofacies; lamda(λ)-mu(μ) for I-type lithofacies The classification success rate of lithofacies, type II lithofacies and type III lithofacies; the classification success rate of lamdarho(λρ)-murho(μρ) for type I lithofacies, type II lithofacies and type III lithofacies.

S403: Calculate the classification success rate of each seismic attribute combination for each lithofacies and generate a total classification success rate corresponding to each seismic attribute combination.

Specifically, taking the total classification success rate of elastic wave impedance EI(30°)-acoustic wave impedance (AI) as an example, as shown in Fig. The classification success rate of facies is 0.95, the classification success rate of elastic wave impedance EI(30°)-acoustic wave impedance (AI) for II lithofacies is 0.78, elastic wave impedance EI(30°)-acoustic wave impedance (AI) for III The classification success rate of lithofacies is 0.79, then the total classification success rate of elastic wave impedance EI(30°)-acoustic wave impedance (AI) is 0.95+0.78+0.79=2.52. And so on, calculate the total classification success rate of Young's modulus (E)-Poisson's ratio (v), the total classification success rate of lamda(λ)-mu(μ), lamdarho(λρ)-murho(μρ) The overall classification success rate.

S404: Sort each classification success rate and use 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 elastic wave impedance EI(30°)-acoustic wave impedance (AI) is the highest, then elastic wave impedance EI(30°)-acoustic wave impedance (AI) is used as the optimal seismic attribute combination.

S203: Perform seismic inversion on the acquired seismic data to generate an inversion profile corresponding to the optimal seismic attribute combination.

In specific implementation, it is assumed that the optimal combination of seismic attributes is elastic wave impedance EI (30°) - acoustic wave impedance (AI), as shown in Figure 11a and Figure 11b , then perform seismic inversion on the acquired seismic data to generate elastic wave impedance EI (30°) corresponding inversion profile and acoustic impedance (AI) corresponding inversion profile.

S204: Using Bayesian classification criteria to interpret the fractability of 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.

In specific implementation, as shown in Fig. 12, it is assumed that the optimal seismic attribute combination is elastic wave impedance EI(30°)-acoustic wave impedance (AI). The corresponding two-dimensional probability density function of (AI) performs fractability interpretation on the inversion profile of elastic wave impedance EI(30°)-acoustic wave impedance (AI) to generate fractability interpretation result profile.

Based on the same application concept as the above-mentioned method for interpreting fractability of shale reservoirs based on statistical petrophysics, the present invention also provides a system for interpreting fractability of shale reservoirs based on statistical petrophysics, as shown in the following embodiments said. Because the principle of solving the problem of the shale reservoir fractability interpretation system based on statistical petrophysics is similar to that of the shale reservoir fractability interpretation method based on statistical petrophysics, the shale reservoir based on statistical petrophysics For the implementation of the fractability interpretation system, please refer to the implementation of the fractability interpretation method for shale reservoirs based on statistical petrophysics, and the repetition will not be repeated.

Fig. 13 is a schematic structural diagram of a shale reservoir fractability interpretation system based on statistical petrophysics according to an embodiment of the application. As shown in Fig. 13, the system includes: an estimation unit 101, a classification unit 102, and an inversion unit 103 and interpretation unit 104.

The estimation unit 101 is configured to perform non-parametric probability density estimation of a kernel function on the acquired logging data of each lithofacies to generate a two-dimensional probability density function corresponding to each preset combination of seismic attributes.

The classification unit 102 is configured to perform self-classification and screening on each of the seismic attribute combinations according to each of the two-dimensional probability density functions using a Bayesian classification criterion to generate an optimal seismic attribute combination.

The inversion unit 103 is configured to perform seismic inversion on the acquired seismic data to generate an inversion profile corresponding to the optimal seismic attribute combination.

The interpretation unit 104 is configured to perform fractability 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 the Bayesian classification criterion.

In one embodiment, as shown in FIG. 14 , the estimation unit 101 includes: an acquisition module 201 , a preset module 202 , an amplification module 203 , an expansion module 204 and an estimation module 205 .

The obtaining module 201 is configured to correct the initial logging data of each of the lithofacies obtained according to the brittleness index intersection method to generate each of the logging data.

The preset module 202 is configured to preset each of the seismic attribute combinations according to each of the well logging data.

The amplification module 203 is used to perform scale amplification on the logging data of each lithofacies using the Backus average to generate the amplified logging data of each lithofacies;

The expansion module 204 is used to expand the data volume of the amplified logging data of each lithofacies by using Monte Carlo to generate the expanded logging data of each lithofacies;

The estimation module 205 is configured to perform kernel function non-parametric probability density estimation on the extended logging data of each lithofacies, respectively, to generate a two-dimensional probability density function corresponding to each combination of seismic attributes.

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 .

A classification module 301, configured to generate a Bayesian classification confusion matrix according to each of the seismic attribute combinations and the two-dimensional probability density functions corresponding to each of the seismic attribute combinations by using Bayesian classification criteria;

a success rate generation module 302, configured to generate a classification success rate of each lithofacies for each seismic attribute combination according to the Bayesian classification confusion matrix;

The summation module 303 is used for summing the classification success rate of each seismic attribute combination to each lithofacies and generating the total classification success rate corresponding to each seismic attribute combination;

The sorting module 304 is configured to sort each total classification success rate and use the seismic attribute combination corresponding to the highest total classification success rate as the optimal seismic attribute combination.

The invention provides a method and system for interpreting the fractability of shale reservoirs based on statistical petrophysics, including: performing a kernel function non-parametric probability density estimation on the acquired logging data of each lithofacies to generate preset seismic The two-dimensional probability density function corresponding to the attribute combination; the Bayesian classification criterion is used to perform self-classification and screening on each of the seismic attribute combinations according to each of the two-dimensional probability density functions to generate the optimal seismic attribute combination; Inversion generates the inversion profile corresponding to the optimal seismic attribute combination; the Bayesian classification criterion is used to explain the fractability of 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 . This application comprehensively considers the changes of mineral components and elasticity-related brittleness index in the reservoir, optimizes the evaluation criteria for shale fractability, and combines the evaluation criteria for fractability with the statistical petrophysical interpretation process to realize shale reservoirs. Quantitative seismic prediction of layer fractability.

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, etc.) having computer-usable program code embodied therein.

In the present invention, the principles and implementations of the present invention are described by using specific embodiments, and the descriptions of the above embodiments are only used to help understand the method and the core idea of the present invention; The idea of the invention will have changes in the specific implementation and application scope. To sum up, the content of this specification should not be construed as a limitation to the present invention.

Claims (10)

1. a shale reservoir fractability interpretation method based on statistical petrophysics, is characterized in that, comprises: Performing non-parametric probability density estimation of kernel function on the acquired logging data of each lithofacies to generate a two-dimensional probability density function corresponding to each preset combination of seismic attributes; Using Bayesian classification criteria to perform self-classification and screening on each of the seismic attribute combinations according to each of the two-dimensional probability density functions to generate an optimal seismic attribute combination; Perform seismic inversion on the acquired seismic data to generate an inversion profile corresponding to the optimal seismic attribute combination; The inversion profile corresponding to the optimal seismic attribute combination is interpreted for fractability according to the two-dimensional probability density function corresponding to the optimal seismic attribute combination using Bayesian classification criteria. 2 . The method for interpreting fractability of shale reservoirs based on statistical petrophysics according to claim 1 , wherein the obtained logging data of each lithofacies is generated by performing kernel function non-parametric probability density estimation. 3 . The two-dimensional probability density functions corresponding to the preset seismic attribute combinations, including: Correcting the initial logging data of each of the lithofacies obtained according to the brittleness index intersection method to generate each of the logging data; Presetting each of the seismic attribute combinations according to each of the well logging data; Using Backus average to amplify the logging data of each of the lithofacies to generate the amplified logging data of each of the lithofacies; Using Monte Carlo to expand the data volume of the amplified logging data of each of the lithofacies to generate the expanded logging data of each of the lithofacies; The non-parametric probability density estimation of the kernel function is performed on the extended logging data of each of the lithofacies respectively to generate a two-dimensional probability density function corresponding to each combination of the seismic attributes. 3 . The method for interpreting fractability of shale reservoirs based on statistical petrophysics according to claim 1 , wherein the Bayesian classification criterion is used to interpret each of the two-dimensional probability density functions according to the two-dimensional probability density function. 4 . The seismic attribute combination is self-classified and filtered to generate the optimal seismic attribute combination, including: Using Bayesian classification criteria to generate a Bayesian classification confusion matrix according to each of the seismic attribute combinations and the two-dimensional probability density functions corresponding to each of the seismic attribute combinations; generating a classification success rate for each of the lithofacies for each of the seismic attribute combinations according to the Bayesian classification confusion matrix; Combining the classification success rates of each of the seismic attribute combinations for each of the lithofacies and generating a total classification success rate corresponding to each of the seismic attribute combinations; The total classification success rates are sorted and the seismic attribute combination corresponding to the highest total classification success rate is used as the optimal seismic attribute combination. 4 . The method for interpreting fractability of shale reservoirs based on statistical petrophysics according to claim 1 , wherein the lithofacies comprise: low fractability shale, high fractability shale and tight sandstone. 5. The method for interpreting fractability of shale reservoirs based on statistical petrophysics according to claim 1, wherein the combination of seismic attributes comprises: elastic wave impedance-acoustic wave impedance, Young's modulus-Poisson than, lamda-mu and lamdarho-murho. 6. The method for interpreting fractability of shale reservoirs 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)) Among them, ψ is the result of Bayesian classification, r is any combination of seismic attributes, ω is each of the lithofacies, p(r|ω) is the two-dimensional probability density function corresponding to the combination of seismic attributes, p( ω) is a preset initial probability. 7. The method for interpreting fractability of shale reservoirs based on statistical petrophysics according to claim 6, wherein the expression of the Bayesian classification confusion matrix is as follows: Among them, _CM is the Bayesian classification confusion matrix, P _st is the probability that the s-th lithofacies is classified into the t-th lithofacies, and s and t are both positive integers; when s=t, P _st is The probability of successful classification of the sth lithofacies. 8. A shale reservoir fractability interpretation system based on statistical petrophysics, characterized by comprising: an estimation unit, configured to perform kernel function non-parametric probability density estimation on the acquired logging data of each lithofacies to generate a two-dimensional probability density function corresponding to each preset combination of seismic attributes; A classification unit, configured to perform self-classification and screening on each of the seismic attribute combinations according to each of the two-dimensional probability density functions using Bayesian classification criteria to generate an optimal seismic attribute combination; an inversion unit, configured to perform seismic inversion on the acquired seismic data to generate an inversion profile corresponding to the optimal seismic attribute combination; An interpretation unit, configured to interpret 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 the Bayesian classification criterion. 9. The shale reservoir fractability interpretation system based on statistical petrophysics according to claim 8, wherein the estimation unit comprises: an acquisition module, configured to correct the initial logging data of each of the lithofacies acquired according to the brittleness index intersection method to generate each of the logging data; a preset module, configured to preset each of the seismic attribute combinations according to each of the well logging data; The amplification module is used to amplify the logging data of each of the lithofacies by using the Backus average to generate the amplified logging data of each of the lithofacies; an expansion module, used for expanding the data volume of the amplified logging data of each of the lithofacies by using Monte Carlo to generate the expanded logging data of each of the lithofacies; The estimation module is configured to perform kernel function non-parametric probability density estimation on the extended logging data of each lithofacies respectively to generate a two-dimensional probability density function corresponding to each combination of the seismic attributes. 10. The shale reservoir fractability interpretation system based on statistical petrophysics according to claim 8, wherein the classification unit comprises: a classification module, configured to generate a Bayesian classification confusion matrix according to each of the seismic attribute combinations and the two-dimensional probability density functions corresponding to each of the seismic attribute combinations by using the Bayesian classification criterion; a success rate generation module, configured to generate a classification success rate for each of the lithofacies for each of the seismic attribute combinations according to the Bayesian classification confusion matrix; a summation module, configured to sum the classification success rates of each of the seismic attribute combinations to the lithofacies to generate a total classification success rate corresponding to each of the seismic attribute combinations; A sorting module, configured to sort each of the total classification success rates and use the seismic attribute combination corresponding to the highest total classification success rate as the optimal seismic attribute combination.