CN116148924A - Shale layer density prediction method based on statistical petrophysical and related equipment - Google Patents
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Abstract
The disclosure provides a shale layer density prediction method and related equipment based on statistical petrophysics, and relates to the technical field of geophysical exploration. The method comprises the following steps: rock physical intersection analysis is carried out on the density of the tattoos in the logging data, and sensitive elastic parameters of the density of the tattoos are determined; establishing a linear relation between the layer density and the sensitive elastic parameter through statistical analysis; substituting the linear relation into a standardized azimuth anisotropic elastic impedance equation to establish a statistical petrophysical model; substituting the statistical petrophysical model into a Bayesian inversion framework, and directly inverting the tattoo density by Bayesian to obtain a final inversion result. According to the embodiment of the disclosure, the tattoo density of the shale reservoir can be directly predicted, and compared with the indirect prediction of the anisotropic parameter, the prediction precision is obviously improved.
Description
Technical Field
The disclosure relates to the technical field of geophysical exploration, in particular to a shale layer density prediction method based on statistical petrophysics and related equipment.
Background
Shale gas refers to unconventional natural gas which is stored in shale rich in organic matters, the storage state is various, and most of shale gas is stored in the adsorption state on the surfaces of rock particles and organic matters or stored in the free state in pores and cracks except for a very small amount of natural gas in a dissolved state.
The existing method for predicting the density of the tattoo is mainly realized by indirectly predicting the anisotropic parameters, but the anisotropic parameters are characterized by not only developing the tattoo, but also brittleness, crack development and the like, and it is difficult to determine which physical property is dominant.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The disclosure provides a shale layer density prediction method based on statistical petrophysics and related equipment, which at least overcomes the problems that the method for predicting the layer density in the related technology is mainly realized by indirectly predicting anisotropic parameters, but the anisotropic parameters are characterized in that not only the development of the layer exists, but also brittleness, crack development and the like are difficult to determine which physical property is dominant.
Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.
According to one aspect of the present disclosure, there is provided a shale layer density prediction method based on statistical petrophysics, comprising:
rock physical intersection analysis is carried out on the density of the tattoos in the logging data, and sensitive elastic parameters of the density of the tattoos are determined;
establishing a linear relation between the layer density and the sensitive elastic parameter through statistical analysis;
substituting the linear relation into a standardized azimuth anisotropic elastic impedance equation to establish a statistical petrophysical model;
substituting the statistical petrophysical model into a Bayesian inversion framework, and directly inverting the tattoo density by Bayesian to obtain a final inversion result.
In one embodiment of the present disclosure, petrophysical intersection analysis is performed on the formation density in the log data, determining a sensitive elastic parameter for the formation density, comprising:
and analyzing the petrophysical intersection graph of the density and the porosity of the stratum, TOC, vp, vs and the anisotropic parameter to obtain the elastic parameter with strong sensitivity of the density of the stratum.
In one embodiment of the present disclosure, the linear relationship is a linear fit.
In one embodiment of the present disclosure, substituting a statistical petrophysical model into a bayesian inversion framework, bayesian directly inverting the sheath density, results in a final inversion result, comprising:
substituting the statistical petrophysical model into a Bayesian inversion framework, and performing direct inversion of the stratum-tatum density prestack earthquake by using a Bayesian formula to obtain a final inversion result.
According to another aspect of the present disclosure, there is provided a shale layer density prediction apparatus based on statistical petrophysics, comprising:
the data analysis module is used for carrying out petrophysical intersection analysis on the density of the tattoos in the logging data and determining sensitive elastic parameters of the density of the tattoos;
the relation construction module is used for establishing a linear relation between the layer density and the sensitive elastic parameter through statistical analysis;
the model construction module is used for substituting the linear relation into a standardized azimuth anisotropic elastic impedance equation to establish a statistical petrophysical model;
and the prediction module is used for substituting the statistical rock physical model into the Bayesian inversion framework, and the Bayesian inversion is directly performed on the ridge density to obtain a final inversion result.
According to still another aspect of the present disclosure, there is provided an electronic apparatus including: a memory for storing instructions; and the processor is used for calling the instructions stored in the memory to realize the shale layer density prediction method based on the statistical petrophysical.
According to yet another aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the above-described statistical petrophysical based shale layer density prediction method.
According to yet another aspect of the present disclosure, there is provided a computer program product storing instructions that, when executed by a computer, cause the computer to implement the above-described statistical petrophysical based shale layer density prediction method.
According to yet another aspect of the present disclosure, there is provided a chip comprising at least one processor and an interface;
an interface for providing program instructions or data to at least one processor;
the at least one processor is configured to execute the program instructions to implement the statistical petrophysical based shale layer density prediction method described above.
According to the shale layer density prediction method based on statistical petrophysical, based on actual logging data, the statistical petrophysical model is utilized, bayesian inversion is combined, the layer density of the shale reservoir is directly predicted, compared with the indirect prediction of anisotropic parameters, the prediction accuracy is obviously improved, and effective geophysical technical support is provided for shale gas exploration and development.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.
FIG. 1 illustrates a flow chart of a shale formation density prediction method based on statistical petrophysical in an embodiment of the disclosure;
FIG. 2 illustrates another shale formation density prediction method flow chart based on statistical petrophysical in an embodiment of the present disclosure;
FIG. 3 shows an L-well layer density sensitive parameter analysis;
FIG. 4 shows a comparison of the results of two-dimensional inversion of the sheath density and the sheath density log;
FIG. 5 shows a low angle seismic profile;
FIG. 6 shows a cross-sectional view of the results of a three-dimensional inversion of the tattoo density;
FIG. 7 illustrates a schematic diagram of a shale formation density prediction apparatus based on statistical petrophysical in an embodiment of the disclosure;
fig. 8 shows a block diagram of an electronic device in an embodiment of the disclosure.
Detailed Description
Example embodiments will now be described more fully hereinafter with reference to the accompanying drawings.
It should be noted that the exemplary embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein.
Shale gas refers to unconventional natural gas which is stored in shale rich in organic matters, the storage state is various, and most of shale gas is stored in the adsorption state on the surfaces of rock particles and organic matters or stored in the free state in pores and cracks except for a very small amount of natural gas in a dissolved state. Shale gas resources are rich, the exploration prospect is good, and compared with conventional reservoirs such as sandstone, the shale gas reservoir has the property of self-generation and self-storage as an unconventional reservoir. Prestack seismic inversion for evaluating shale gas reservoir geologic desserts is intended to estimate petrophysical properties of shale reservoirs from measured seismic data and well log data.
The petrophysical model can establish the relation between reservoir physical properties and elastic parameters, and can quantitatively interpret seismic data to obtain the spatial distribution of physical parameters of a research area. Because of the complexity of the subsurface medium, petrophysical models are difficult to describe accurately, and the seismic data used itself contains noise, these deviations making the reservoir parameter inversion uncertain. The deterministic inversion gives only the optimal solution, and the method itself ignores the uncertainty objectively existing in the inversion. Known information (log, geologic, adjacent or similar zone data) can constrain the inversion process to improve computational accuracy and efficiency, but is difficult to integrate into deterministic inversion systems. The statistical petrophysical model can solve the problems well.
Shale rich in organic matter is the primary carrier for shale oil and gas production and storage. At the same time, a large number of tattoo structures develop in shale. In recent years, more and more scholars find that shale medium-grain development has an important influence on reservoir performance, and the development characteristics of the grain are gradually taken as one of development mechanisms of a high-quality reservoir. The degree of development of a sheath is typically expressed in terms of the sheath density, i.e., the total number of layers per unit core length (bars/meter). In the related art, for most indirect predictions of shale layer density, for example, liu Xiwu et al (2022) are based on petrophysical equivalent theory, shale anisotropies are decoupled in stages, and the degree of development of the layer structure is characterized by inverting the anisotropism parameters of the solid matrix. Zhang Xiaodong (2022) proposes a shale gas-containing property and bedding structure prediction method based on anisotropic dispersion properties, which constructs an anisotropic frequency-dependent AVO expression expressed by speed and anisotropic parameters based on a VTI medium model according to bedding development characteristics (including a tattooing structure and horizontal seams) of a shale gas reservoir, and establishes an inversion method of shale longitudinal wave speed dispersion properties and anisotropic dispersion properties so as to characterize the gas-containing property and bedding development of the shale gas reservoir respectively. However, there is less research in the literature published today on direct inversion of the tattoo density.
In conclusion, the current prediction of the shale gas reservoir stratum density is mainly to indirectly invert the anisotropic parameter to represent the development degree of the shale gas reservoir stratum density, and the research on a method for directly predicting the stratum density is lacking. Based on the above, considering inelastic characteristics of shale gas reservoirs caused by gas and organic matter enrichment and anisotropic characteristics related to a tattoo structure, development of a tattoo density direct prediction method based on a statistical petrophysical model is urgently needed.
The present exemplary embodiment will be described in detail below with reference to the accompanying drawings and examples.
Fig. 1 shows a flowchart of a shale layer density prediction method based on statistical petrophysical in an embodiment of the disclosure, and as shown in fig. 1, the shale layer density prediction method based on statistical petrophysical provided in the embodiment of the disclosure includes steps S110-S140.
In S110, rock physical intersection analysis is carried out on the density of the tattoos in the logging data, and sensitive elastic parameters of the density of the tattoos are determined;
in S120, establishing a linear relation between the layer density and the sensitive elastic parameter through statistical analysis;
in S130, substituting the linear relation into a standardized azimuth anisotropic elastic impedance equation to establish a statistical petrophysical model;
in S140, substituting the statistical rock physical model into a Bayesian inversion framework, and directly inverting the tattoo density by Bayesian to obtain a final inversion result.
In some embodiments, S110 above includes analyzing the petrophysical intersection map of the sheath density and the porosity, TOC, vp, vs, and the anisotropic parameters to obtain the elastic parameters with strong sheath density sensitivity.
In some embodiments, S120 described above includes establishing a linear relationship between the layer density and the sensitive elastic parameter based on a statistical analysis principle, where the linear relationship is a linear fit.
In some embodiments, S140 includes substituting the statistical petrophysical model into a bayesian inversion framework, and performing direct inversion of the cascade density prestack seismic using a bayesian formula to obtain a final inversion result.
According to the embodiment of the disclosure, based on the statistical petrophysical model, the stratum density of the shale reservoir is directly predicted, compared with the indirect prediction of the anisotropic parameter, the prediction precision is obviously improved, and the method and the device provide effective geophysical technical support for shale gas exploration and development in typical areas.
Fig. 2 shows a flowchart of a shale layer density prediction method based on statistical petrophysical in an embodiment of the disclosure, and the shale layer density prediction method based on statistical petrophysical provided in the embodiment of the disclosure is described below with reference to fig. 2.
Rock physical intersection analysis is carried out on the density of the tattoos in the logging data to optimize the sensitive elastic parameters of the density of the tattoos; establishing a linear relation between the density of the tattoo and the sensitive elastic parameter through statistical analysis; substituting the linear relation between the density of the tattoo and the sensitive elastic parameter into a standardized azimuth anisotropic elastic impedance equation to establish a statistical petrophysical model; substituting the established statistical rock physical model into a Bayesian inversion framework; and directly inverting the tattoo density by Bayes to obtain a final inversion result.
Rock physical intersection analysis is carried out on the density of the tattoos in the logging data to optimize the sensitive elastic parameters of the tattoos, and the rock physical intersection analysis specifically comprises the following steps:
analyzing petrophysical intersection graphs of the density and the porosity of the tattoos, TOC, vp, vs and the anisotropic parameters, preferably obtaining elastic parameters with strong sensitivity of the tattoos, and finding that the density and the anisotropic parameters of the tattoos have a better linear relation: the density of the stratum is increased, and the corresponding TOC and porosity are increased, so that the oil and gas preservation condition of the reservoir is good, and the pore circulation is good; at the same time, the density of the stratum is increased, and the anisotropic parameters epsilon and delta are correspondingly increased, so that the strong anisotropy of the stratum is caused, which is consistent with the recognition of the characteristic of the strong anisotropy of the stratum. The sensitive elastic parameters of the density of the embossed layer are preferably epsilon, delta, gamma.
Wherein, the density of the layers is the total number of layers (strips/meter) in the unit rock core length, the porosity is the ratio (%) of the sum of all pore space volumes in the rock sample to the rock sample volume, TOC is the mass (%) of organic carbon in the unit mass rock, vp is the speed (m/s) of longitudinal waves in the underground medium, longitudinal waves refer to waves with the vibration directions of particles in the medium parallel to the wave propagation directions, vs is the speed (m/s) of transverse waves, which are the vibration directions of particles in the medium and the wave propagation directions are perpendicular to each other, epsilon is the anisotropic parameter of longitudinal waves, which describes the difference of the P wave propagation speeds in the horizontal direction and the vertical direction, delta is the anisotropic parameter of azimuth, which describes the difference of the SH wave propagation speeds in the horizontal direction and the vertical direction.
Through statistical analysis, establishing a linear relation between the density of the tattoo and the sensitive elastic parameter, which comprises the following steps:
as shown in fig. 3, based on petrophysical analysis and measured result analysis, the density of the layers and the anisotropic parameter have good relation, and the density of the layers and the anisotropic parameter can be expressed as follows by statistics:
wc＝k 1 ε+k 2 δ+k 3 γ+d (1)
in the formula (1), wc is the density of the layer, epsilon, delta, gamma is the anisotropic parameter, d is the error, k 1 ，k 2 ，k 3 Is the fitting coefficient.
The same has similar characteristics for TOC and porosity to shale anisotropism, and the following relationship is established:
in the formula (2), n ij For the fitting coefficient, por is porosity, T is TOC content, l ij Is an error term.
Substituting the linear relation between the density of the tattoo and the sensitive elastic parameter into a standardized azimuth anisotropic elastic impedance equation to establish a statistical petrophysical model, wherein the method specifically comprises the following steps of:
the normalized azimuthal anisotropic elastic impedance equation is:
in order to be able to invert a stable data volume from an elastic impedance data volume, the equation (3) is linearized:
wherein EI is 0 ＝α 0 ρ 0 Wherein alpha, beta and rho are longitudinal wave speed, transverse wave speed and density respectively, and alpha 0 、β 0 、ρ 0 Mean values of longitudinal wave velocity, transverse wave velocity and density, EI 0 Respectively elastic impedance and elastic impedanceValue, a=1+tan 2 θ，b＝-8Ksin 2 θ，c＝1-4k 2 sin 2 θ，
k＝(β/α) 2 θ is the incident angle, < >>
Is azimuth delta (V) ，ε (V) ,γ (V) Is an anisotropic parameter.
Substituting the formula (2) into a standardized azimuthal anisotropic elastic equation (3) to obtain azimuthal elastic impedance represented by the tattooing density:
where wc is the density of the layers, i.e. the total number of layers per unit core length (bars/meter), T is the TOC, i.e. the mass (%) of organic carbon in the rock per unit mass, por is the porosity, i.e. the ratio (%) of the sum of all pore space volumes in the rock sample to the volume of the rock sample, α is Vp, i.e. the velocity (m/s) of longitudinal waves propagating in the underground medium, longitudinal waves are waves with the vibration direction of the particles in the medium parallel to the wave propagation direction, β is Vs, i.e. the velocity (m/s) of transverse waves propagating in the underground medium, transverse waves are waves with the vibration direction of the particles in the medium perpendicular to the wave propagation direction, ε is the longitudinal wave anisotropy parameter, i.e. the parameter describing the difference of the propagation velocity of P waves in the horizontal and vertical directions, δ is the azimuthal anisotropy parameter, γ is the anisotropic parameter, i.e. the parameter describing the difference of the propagation velocity of SH waves in the horizontal and vertical directions.
To obtain stable inversion results of the tattoo density, at least 6 elastic impedance data volumes with different azimuth angles and different incident angles are needed, namely:
substituting the established statistical rock physical model into a Bayesian inversion framework, specifically comprising:
the obtained statistical petrophysical model is added into a Bayesian inversion framework, and under the Bayesian theory framework, the physical parameters and the elastic impedance are brought into a Bayesian formula and can be written into the following form:
wherein r= [ wc, port, T ], wc, port, T represent layer density, porosity, and TOC in order; m represents the elastic impedance at 6 different azimuth angles and different angles of incidence.
Since in practical applications, the function of the regularization factor is taken as a constant α, then (7) can be written as follows:
P([wc,Por,T]|m)＝α×P([wc,Por,T])P(m|[wc,Por,T]) (8)
the right side P ([ wc, por, T ]) of the equation is called the prior distribution of the parameter to be solved, P (m| [ wc, por, T ]) is the likelihood function, and the prior and posterior distributions act as bridges.
Finally, the layer density corresponding to the maximum value of the step (8) is the final inversion result:
[wc,Por,T]＝argMaxP([wc,Por,T]|m) (9)
the Bayesian inversion method comprises the steps of directly inverting the sheath density to obtain a final inversion result, and specifically comprises the steps of adding the obtained statistical rock physical model into a Bayesian inversion framework to perform sheath density pre-stack seismic prediction to obtain the sheath density inversion result.
Fig. 4 is a comparison diagram of a two-dimensional inversion result of the sheath density and a sheath density logging curve, so that the accuracy of the method is verified, a solid line is the sheath density logging curve, a dotted line is the sheath density inversion result, the trend of the sheath density logging curve and the trend of the inversion result curve are generally consistent, the sheath density is basically corresponding to the high-low value of the sheath, and the method can be used for predicting the sheath density of the shale gas reservoir.
FIG. 5 is a small-angle seismic section, and FIG. 6 is a three-dimensional section of the results of the tattooing density inversion, the inversion results show that the density of the tattooing at the bottom of the K2 section is in the range of 180-210; the density of the tattoo layer at the bottom of the K5 section is between 160 and 180, and the density of the tattoo layer at the bottom of the K2 section is higher than that of the bottom of the K5 section.
Furthermore, although the steps of the methods in the present disclosure are depicted in a particular order in the drawings, this does not require or imply that the steps must be performed in that particular order or that all illustrated steps be performed in order to achieve desirable results.
In some embodiments, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform, etc.
Based on the same inventive concept, the embodiment of the disclosure also provides a shale layer density prediction device based on statistical petrophysical, as described in the following embodiment. Since the principle of solving the problem of the embodiment of the device is similar to that of the embodiment of the method, the implementation of the embodiment of the device can be referred to the implementation of the embodiment of the method, and the repetition is omitted.
Fig. 7 shows a schematic diagram of a shale layer density prediction apparatus based on statistical petrophysical according to an embodiment of the disclosure, as shown in fig. 7, the shale layer density prediction apparatus 500 based on statistical petrophysical includes:
the data analysis module 702 is used for carrying out petrophysical intersection analysis on the density of the tattoos in the logging data and determining sensitive elastic parameters of the density of the tattoos;
the relationship construction module 704 is configured to establish a linear relationship between the layer density and the sensitive elastic parameter through statistical analysis;
the model construction module 706 is configured to substitute the linear relationship into a standardized azimuthal anisotropic elastic impedance equation to establish a statistical petrophysical model;
the prediction module 708 is configured to substitute the statistical petrophysical model into a bayesian inversion framework, and directly invert the layer density by bayes, so as to obtain a final inversion result.
In some embodiments, the data analysis module 702 is configured to analyze the petrophysical intersection map of the tattoo density and porosity, TOC, vp, vs, and anisotropic parameters to obtain elastic parameters with strong tattoo density sensitivity.
In some embodiments, the linear relationship is a linear fit.
In some embodiments, the normalized azimuthal anisotropic elastic impedance equation is as follows:
wherein a=1+tan 2 θ，b＝-8Ksin 2 θ，c＝1-4k 2 sin 2 θ，
k＝(β/α) 2 ；
Alpha represents longitudinal wave velocity, beta represents transverse wave velocity, ρ represents density, alpha 0 Mean value of longitudinal wave velocity, beta 0 Mean value ρ of transverse wave velocity 0 Mean value of density, EI tableShow elastic impedance, EI 0 Represents the mean value of the elastic impedance, θ represents the incident angle,
representing azimuth angle delta (V) 、ε (V) And gamma (V) Indicating the anisotropic parameters.
In some embodiments, the Bayesian inversion framework is formulated as follows:
wherein R represents a parameter to be estimated, m represents observation sample information, P (R) represents a priori distribution, and P (m|r) represents a likelihood function.
In some embodiments, the prediction module 708 is configured to substitute the statistical petrophysical model into a bayesian inversion framework, and perform direct inversion of the cascade density prestack seismic using a bayesian formula to obtain a final inversion result.
The terms "first," "second," and the like in this disclosure are used solely to distinguish one from another device, module, or unit, and are not intended to limit the order or interdependence of functions performed by such devices, modules, or units.
The specific manner in which the respective modules perform the operations in the above-described embodiments of the statistical petrophysical based shale layer density prediction apparatus has been described in detail in the embodiments of the statistical petrophysical based shale layer density prediction method, and will not be described in detail herein.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory.
Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.
An electronic device provided by an embodiment of the present disclosure is described below with reference to fig. 8. The electronic device 800 shown in fig. 8 is merely an example and should not be construed to limit the functionality and scope of use of embodiments of the present disclosure in any way.
Fig. 8 shows a schematic architecture diagram of an electronic device 800 according to the present disclosure. As shown in fig. 8, the electronic device 800 includes, but is not limited to: at least one processor 810, at least one memory 820.
In some embodiments, memory 820 may include readable media in the form of volatile memory units, such as random access memory unit (RAM) 8201 and/or cache memory unit 8202, and may further include read only memory unit (ROM) 8203.
In some embodiments, memory 820 may also include a program/utility 8204 having a set (at least one) of program modules 8205, such program modules 8205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
In some embodiments, memory 820 may store an operating system. The operating system may be a real-time operating system (Real Time eXecutive, RTX), LINUX, UNIX, WINDOWS or OS X like operating systems.
In some embodiments, memory 820 may also have data stored therein.
As one example, processor 810 may read data stored in memory 820, which may be stored at the same memory address as the instructions, or which may be stored at a different memory address than the instructions.
rock physical intersection analysis is carried out on the density of the tattoos in the logging data, and sensitive elastic parameters of the density of the tattoos are determined;
establishing a linear relation between the layer density and the sensitive elastic parameter through statistical analysis;
substituting the linear relation into a standardized azimuth anisotropic elastic impedance equation to establish a statistical petrophysical model;
substituting the statistical petrophysical model into a Bayesian inversion framework, and directly inverting the tattoo density by Bayesian to obtain a final inversion result.
It should be noted that, the processor 810 may be a general-purpose processor or a special-purpose processor. Processor 810 may include one or more processing cores, with processor 810 executing various functional applications and data processing by executing instructions.
In some embodiments, the processor 810 may include a central processing unit (central processing unit, CPU) and/or a baseband processor.
In some embodiments, processor 810 may determine an instruction based on priority identification and/or functional class information carried in each control instruction.
In this disclosure, the processor 810 and the memory 820 may be provided separately or may be integrated together.
As one example, processor 810 and memory 820 may be integrated on a single board or System On Chip (SOC).
As shown in fig. 8, the electronic device 800 is embodied in the form of a general purpose computing device. The electronic device 800 may also include a bus 830.
The electronic device 800 may also communicate with one or more external devices 840 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 800, and/or any device (e.g., router, modem, etc.) that enables the electronic device 800 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 850.
Also, electronic device 800 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 880.
As shown in fig. 8, network adapter 880 communicates with other modules of electronic device 800 over bus 830.
It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 800, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
It is to be understood that the illustrated structure of the presently disclosed embodiments does not constitute a particular limitation of the electronic device 800. In other embodiments of the present disclosure, electronic device 800 may include more or fewer components than shown in FIG. 8, or may combine certain components, or split certain components, or a different arrangement of components. The components shown in fig. 8 may be implemented in hardware, software, or a combination of software and hardware.
The present disclosure also provides a computer readable storage medium having stored thereon computer instructions that, when executed by a processor, implement the statistical petrophysical based shale layer density prediction method described in the above method embodiments.
A computer-readable storage medium in an embodiment of the present disclosure is a computer instruction that can be transmitted, propagated, or transmitted for use by or in connection with an instruction execution system, apparatus, or device.
As one example, the computer-readable storage medium is a non-volatile storage medium.
In some embodiments, more specific examples of the computer readable storage medium in the present disclosure may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, a U disk, a removable hard disk, or any suitable combination of the foregoing.
In an embodiment of the present disclosure, a computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with computer instructions (readable program code) carried therein.
Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing.
In some examples, the computing instructions contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
The disclosed embodiments also provide a computer program product storing instructions that, when executed by a computer, cause the computer to implement the statistical petrophysical based shale layer density prediction method described in the above method embodiments.
The instructions may be program code. In particular implementations, the program code can be written in any combination of one or more programming languages.
The programming languages include object oriented programming languages such as Java, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages.
The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server.
In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
The embodiment of the disclosure also provides a chip comprising at least one processor and an interface;
an interface for providing program instructions or data to at least one processor;
the at least one processor is configured to execute program instructions to implement the statistical petrophysical based shale layer density prediction method described in the above method embodiments.
In some embodiments, the chip may also include a memory for holding program instructions and data, the memory being located either within the processor or external to the processor.
Those of ordinary skill in the art will appreciate that all or a portion of the steps implementing the above embodiments may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein.
This disclosure is intended to cover any adaptations, uses, or adaptations of the disclosure following the general principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (10)
1. A shale layer density prediction method based on statistical petrophysics is characterized by comprising the following steps:
rock physical intersection analysis is carried out on the density of the tattoos in the logging data, and sensitive elastic parameters of the density of the tattoos are determined;
establishing a linear relation between the tattoo density and the sensitive elastic parameter through statistical analysis;
substituting the linear relation into a standardized azimuth anisotropic elastic impedance equation to establish a statistical petrophysical model;
substituting the statistical rock physical model into a Bayesian inversion framework, and directly inverting the ridge density by Bayesian to obtain a final inversion result.
2. The method of claim 1, wherein the petrophysical intersection analysis of the formation density in the well log data to determine the sensitive elastic parameter of the formation density comprises:
and analyzing the petrophysical intersection graph of the density and the porosity of the stratum, TOC, vp, vs and the anisotropic parameter to obtain the elastic parameter with strong sensitivity of the density of the stratum.
3. The method of claim 1, wherein the linear relationship is a linear fit.
4. The method of claim 1, wherein the normalized azimuthal anisotropic elastic impedance equation is as follows:
wherein a=1+tan 2 θ，b＝-8K sin 2 θ，c＝1-4k 2 sin 2 θ，
k＝(β/α) 2 ；
Alpha represents longitudinal wave velocity, beta represents transverse wave velocity, ρ represents density, alpha 0 Mean value of longitudinal wave velocity, beta 0 Mean value ρ of transverse wave velocity 0 Mean value of density, EI represents elastic impedance, EI 0 Represents the mean value of the elastic impedance, θ represents the incident angle,
representing azimuth angle delta (V) 、ε (V) And gamma (V) Indicating the anisotropic parameters.
5. The method of claim 1, wherein the bayesian inversion framework is formulated as follows:
wherein R represents a parameter to be estimated, m represents observation sample information, P (R) represents a priori distribution, and P (m|r) represents a likelihood function.
6. The method of claim 1, wherein substituting the statistical petrophysical model into a bayesian inversion framework, bayesian directly inverting the sheath density, results in a final inversion result, comprises:
substituting the statistical rock physical model into a Bayesian inversion framework, and performing direct inversion of the deck density prestack earthquake by using a Bayesian formula to obtain a final inversion result.
7. Shale layer density prediction device based on statistics petrophysics, characterized by comprising:
the data analysis module is used for carrying out petrophysical intersection analysis on the density of the tattoos in the logging data and determining sensitive elastic parameters of the density of the tattoos;
the relation construction module is used for establishing a linear relation between the layer density and the sensitive elastic parameter through statistical analysis;
the model construction module is used for substituting the linear relation into a standardized azimuth anisotropic elastic impedance equation to establish a statistical petrophysical model;
and the prediction module is used for substituting the statistical rock physical model into a Bayesian inversion framework, and the Bayesian inversion is directly performed on the layer density to obtain a final inversion result.
8. An electronic device, comprising:
a memory for storing instructions;
a processor for invoking instructions stored in the memory to implement the statistical petrophysical based shale layer density prediction method of any one of claims 1-6.
9. A computer readable storage medium having stored thereon computer instructions, which when executed by a processor, implement the statistical petrophysical based shale layer density prediction method of any of claims 1-6.
10. A chip comprising at least one processor and an interface;
the interface is used for providing program instructions or data for the at least one processor;
the at least one processor is configured to execute the program instructions to implement the statistical petrophysical based shale layer density prediction method of any one of claims 1-6.
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| CN117331123B (en) * | 2023-11-06 | 2024-04-02 | 成都理工大学 | Fracture-cavity density inversion method and fracture-cavity reservoir prediction system |
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