Shale lamination density prediction method and related equipment based on statistical rock physics

Abstract

The present disclosure provides a shale lamination density prediction method and related equipment based on statistical rock physics, which relates to the field of geophysical exploration technology. The method includes: performing rock physics intersection analysis on the lamination density in the logging data to determine the sensitive elastic parameters of the lamination density; establishing a linear relationship between the lamination density and the sensitive elastic parameters through statistical analysis; substituting the linear relationship into the normalized azimuthal anisotropic elastic impedance equation to establish a statistical rock physics model; substituting the statistical rock physics model into the Bayesian inversion framework, and directly inverting the lamination density using Bayesian to obtain the final inversion result. According to the embodiment of the present disclosure, the lamination density of the shale reservoir can be directly predicted, and the prediction accuracy is significantly improved compared to the indirect prediction of its anisotropic parameters.

Description

Technical Field

[0001] The present disclosure relates to the field of geophysical exploration technology, and in particular to a shale lamination density prediction method based on statistical rock physics and related equipment. Background Art

[0002] Shale gas refers to unconventional natural gas found in organic-rich mudstone. Its occurrence states vary. Except for a very small amount of dissolved natural gas, most of it is found in an adsorbed state on the surface of rock particles and organic matter, or in a free state in pores and cracks.

[0003] Existing lamination density prediction methods are mainly achieved by indirectly predicting anisotropy parameters. However, anisotropy parameters characterize not only lamination development, but also brittleness and crack development, making it difficult to determine which physical property is dominant.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the Invention

[0005] The present disclosure provides a shale lamination density prediction method and related equipment based on statistical rock physics, which at least to a certain extent overcomes the problem that the lamination density prediction method in the related art is mainly achieved by indirectly predicting anisotropy parameters. However, the anisotropy parameters characterize not only the lamination development, but also the brittleness and crack development, etc., making it difficult to determine which physical property is dominant.

[0006] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by practice of the present disclosure.

[0007] According to one aspect of the present disclosure, a method for predicting shale lamination density based on statistical rock physics is provided, comprising:

[0008] Conduct rock physics intersection analysis on laminae density in well logging data to determine sensitive elastic parameters of laminae density;

[0009] Through statistical analysis, a linear relationship between laminae density and sensitive elastic parameters is established;

[0010] Substitute the linear relationship into the normalized azimuthal anisotropic elastic impedance equation to establish a statistical rock physics model;

[0011] The statistical rock physics model is substituted into the Bayesian inversion framework, and the Bayesian method directly inverts the laminae density to obtain the final inversion result.

[0012] In one embodiment of the present disclosure, a rock physics intersection analysis is performed on the laminae density in the well logging data to determine the sensitive elastic parameters of the laminae density, including:

[0013] By analyzing the rock physics intersection diagram of laminae density with porosity, TOC, Vp, Vs, and anisotropy parameters, elastic parameters with strong sensitivity to laminae density are obtained.

[0014] In one embodiment of the present disclosure, the linear relationship is a linear fit.

[0015] In one embodiment of the present disclosure, the statistical rock physics model is substituted into the Bayesian inversion framework, and the Bayesian direct inversion of the laminae density is performed to obtain the final inversion result, including:

[0016] The statistical rock physics model is substituted into the Bayesian inversion framework, and the Bayesian formula is used to perform prestack seismic direct inversion of laminae density to obtain the final inversion results.

[0017] According to another aspect of the present disclosure, a shale lamination density prediction device based on statistical rock physics is provided, comprising:

[0018] Data analysis module, used to perform rock physics intersection analysis on laminae density in logging data and determine sensitive elastic parameters of laminae density;

[0019] A relationship building module is used to establish a linear relationship between laminae density and sensitive elastic parameters through statistical analysis;

[0020] A model building module is used to substitute the linear relationship into the normalized azimuthal anisotropic elastic impedance equation to build a statistical rock physics model;

[0021] The prediction module is used to substitute the statistical rock physics model into the Bayesian inversion framework, and the Bayesian directly inverts the laminae density to obtain the final inversion result.

[0022] According to another aspect of the present disclosure, an electronic device is provided, comprising: a memory for storing instructions; and a processor for calling the instructions stored in the memory to implement the above-mentioned shale lamination density prediction method based on statistical rock physics.

[0023] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which computer instructions are stored. When the computer instructions are executed by a processor, the above-mentioned shale lamination density prediction method based on statistical rock physics is implemented.

[0024] According to another aspect of the present disclosure, a computer program product is provided. The computer program product stores instructions, which, when executed by a computer, enable the computer to implement the above-mentioned shale lamination density prediction method based on statistical rock physics.

[0025] According to yet another aspect of the present disclosure, there is provided a chip comprising at least one processor and an interface;

[0026] An interface for providing program instructions or data to at least one processor;

[0027] At least one processor is configured to execute program instructions to implement the above-mentioned shale lamination density prediction method based on statistical rock physics.

[0028] The shale lamination density prediction method based on statistical rock physics provided in the embodiments of the present disclosure is based on actual logging data, utilizes statistical rock physics models, and combines Bayesian inversion to directly predict the lamination density of shale reservoirs. Compared with the indirect prediction of its anisotropy parameters, the prediction accuracy is significantly improved, providing effective geophysical technical support for shale gas exploration and development.

[0029] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0031] Obviously, the drawings described below are only some embodiments of the present disclosure. A person skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0032] Figure 1 A flow chart of a shale lamination density prediction method based on statistical rock physics in an embodiment of the present disclosure is shown;

[0033] Figure 2 A flow chart of another method for predicting shale lamination density based on statistical rock physics in an embodiment of the present disclosure is shown;

[0034] Figure 3 Shows the L-well lamination density sensitivity parameter analysis;

[0035] Figure 4 A comparison diagram showing the 2D inversion results of laminae density and the laminae density logging curve;

[0036] Figure 5 A small-angle seismic profile is shown;

[0037] Figure 6 The cross-section diagram of the three-dimensional inversion result of laminae density is shown;

[0038] Figure 7A schematic diagram of a shale lamination density prediction device based on statistical rock physics in an embodiment of the present disclosure is shown;

[0039] Figure 8 A structural block diagram of an electronic device in an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0040] Example embodiments will be described more fully hereinafter with reference to the accompanying drawings.

[0041] It should be noted that example embodiments may be implemented in many forms and should not be construed as limited to the examples set forth herein.

[0042] Shale gas refers to unconventional natural gas found in organic-rich mudstones. Its occurrence states vary. Aside from a very small amount of dissolved natural gas, most of it is found adsorbed on the surfaces of rock particles and organic matter, or in a free state within pores and fractures. Shale gas resources are abundant and offer promising exploration prospects. Compared to conventional reservoirs such as sandstone, shale gas reservoirs, as unconventional reservoirs, exhibit "self-generation and self-storage" properties. Prestack seismic inversion, used to assess the geological sweet spots of shale gas reservoirs, aims to estimate the petrophysical properties of shale reservoirs based on measured seismic and well logging data.

[0043] Rock physics models can establish the relationship between reservoir properties and elastic parameters, allowing for quantitative interpretation of seismic data and the spatial distribution of physical parameters in the study area. Due to the complexity of the subsurface medium, rock physics models are difficult to accurately describe, and the seismic data used inherently contain noise. These deviations make the inversion of reservoir parameters uncertain. Deterministic inversion only provides an optimal solution, and the method itself ignores the objective uncertainty in the inversion process. Known information (well logging, geology, and data from adjacent or similar areas) can constrain the inversion process to improve computational accuracy and efficiency, but it is difficult to integrate into deterministic inversion systems. Statistical rock physics models can effectively address the above issues.

[0044] Organic-rich shale is the main carrier for the generation and storage of shale oil and gas. At the same time, a large number of laminar structures are also developed in shale. In recent years, more and more scholars have found that the development of laminae in shale has an important impact on the storage performance of reservoirs, and the development characteristics of laminae have gradually been regarded as one of the development mechanisms of high-quality reservoirs. The degree of lamination development is usually expressed by laminae density, that is, the total number of laminae per unit core length (lamina / meter). In related technologies, the laminae density of shale is mostly predicted indirectly. For example, Liu Xiwu et al. (2022) decoupled the anisotropy of shale in a graded manner based on the rock physics equivalence theory, and characterized the degree of laminae structure development by inverting the anisotropic parameters of the solid matrix. Zhang Xiaodong (2022) proposed a method for predicting shale gas content and bedding structure based on anisotropic dispersion properties. Based on the bedding characteristics of shale gas reservoirs (including laminae and horizontal fractures), this method constructs an anisotropic frequency-dependent AVO expression based on the VTI medium model, expressed in terms of velocity and anisotropy parameters. This method then establishes an inversion method for shale P-wave velocity dispersion properties and anisotropic dispersion properties, thereby characterizing the gas content and bedding development of shale gas reservoirs, respectively. However, there are few studies on direct inversion of laminae density in the published literature.

[0045] In summary, current methods for predicting laminae density in shale gas reservoirs primarily rely on indirect inversion of anisotropic parameters to characterize their development, and there is a lack of research on direct laminae density prediction methods. Therefore, considering the inelastic characteristics of shale gas reservoirs due to gas and organic matter enrichment, as well as the anisotropic characteristics related to laminae structure, there is an urgent need to develop a direct laminae density prediction method based on statistical rock physics models.

[0046] This exemplary implementation is described in detail below with reference to the accompanying drawings and examples.

[0047] Figure 1 A flow chart of a shale lamination density prediction method based on statistical rock physics in an embodiment of the present disclosure is shown as follows: Figure 1 As shown, the shale lamination density prediction method based on statistical rock physics provided in the embodiment of the present disclosure includes steps S110-S140.

[0048] In S110, a rock physics intersection analysis is performed on the laminae density in the well logging data to determine the sensitive elastic parameters of the laminae density;

[0049] In S120, a linear relationship between laminae density and sensitive elastic parameters is established through statistical analysis;

[0050] In S130, the linear relationship is substituted into the normalized azimuthal anisotropic elastic impedance equation to establish a statistical rock physics model;

[0051] In S140, the statistical rock physics model is substituted into the Bayesian inversion framework, and the Bayesian method directly inverts the laminae density to obtain the final inversion result.

[0052] In some embodiments, the above S110 includes analyzing a rock physics intersection diagram of laminae density and porosity, TOC, Vp, Vs, and anisotropy parameters to obtain elastic parameters that are highly sensitive to laminae density.

[0053] In some embodiments, the above S120 includes establishing a linear relationship between the laminar density and the sensitive elastic parameter based on statistical analysis principles, where the linear relationship is a linear fit.

[0054] In some embodiments, the above S140 includes substituting the statistical rock physics model into the Bayesian inversion framework, performing pre-stack seismic direct inversion of laminae density using the Bayesian formula, and obtaining a final inversion result.

[0055] The disclosed embodiment directly predicts the lamination density of shale reservoirs based on a statistical rock physics model. Compared with the indirect prediction of its anisotropy parameters, the prediction accuracy is significantly improved. It has been tested and applied in typical areas, providing effective geophysical technical support for shale gas exploration and development.

[0056] Figure 2 A flow chart of a shale laminae density prediction method based on statistical rock physics is shown in the embodiment of the present disclosure. Figure 2 , illustrating the shale lamination density prediction method based on statistical rock physics provided by an embodiment of the present disclosure.

[0057] A rock physics intersection analysis was performed on the lamination density in the logging data to optimize the sensitive elastic parameters of the lamination density. A linear relationship between the lamination density and the sensitive elastic parameters was established through statistical analysis. The linear relationship between the lamination density and the sensitive elastic parameters was substituted into the standardized azimuthal anisotropic elastic impedance equation to establish a statistical rock physics model. The established statistical rock physics model was substituted into the Bayesian inversion framework. The Bayesian direct inversion of the lamination density was used to obtain the final inversion result.

[0058] The rock physics intersection analysis of the laminae density in the logging data is performed to select the sensitive elastic parameters of the laminae density, including:

[0059] By analyzing the rock physics intersection diagrams of laminae density with porosity, TOC, Vp, Vs, and anisotropy parameters, elastic parameters with strong sensitivity to laminae density were selected. A good linear relationship between laminae density and anisotropy parameters was found: as laminae density increases, TOC and porosity increase accordingly, indicating good reservoir oil and gas preservation conditions and good pore flow. Simultaneously, as laminae density increases, anisotropy parameters ε and δ also increase, resulting in strong reservoir anisotropy, which is consistent with the understanding of strong anisotropy in laminae density. The sensitive elastic parameters for laminae density were selected as ε, δ, and γ.

[0060] Among them, the laminae density is the total number of laminae per unit core length (laminae / meter), the porosity is the ratio of the sum of the volumes of all pore spaces in the rock sample to the volume of the rock sample (%), TOC is the mass of organic carbon per unit mass of rock (%), Vp is the velocity of longitudinal waves in the underground medium (m / s), and longitudinal waves refer to waves in which the vibration direction of particles in the medium is parallel to the propagation direction of the wave, Vs is the shear wave velocity, that is, the velocity of shear waves in the underground medium (m / s), and shear waves refer to waves in which the vibration direction of particles in the medium is perpendicular to the propagation direction of the wave, ε is the longitudinal wave anisotropy parameter, which describes the difference between the P-wave propagation speed in the horizontal and vertical directions, δ is the azimuthal anisotropy parameter, and γ is the shear wave anisotropy parameter, which describes the difference between the SH-wave propagation speed in the horizontal and vertical directions.

[0061] Through statistical analysis, a linear relationship between laminae density and sensitive elastic parameters is established, including:

[0062] like Figure 3 As shown in Figure 2, based on rock physics analysis and measured results, the laminae density and anisotropy parameters have a good relationship. Through statistics, it is found that the laminae density and anisotropy parameters can be expressed as:

[0063] wc=k1ε+k2δ+k3γ+d (1)

[0064] In formula (1), wc is the laminae density, ε, δ, γ are anisotropy parameters, d is the error, and k1, k2, k3 are fitting coefficients.

[0065] Similarly, TOC and porosity have similar characteristics on the anisotropy of shale, and the following relationship is established:

[0066]

[0067] In formula (2), n ij is the fitting coefficient, Por is the porosity, T is the TOC content, l ij is the error term.

[0068] The linear relationship between laminar density and sensitive elastic parameters is substituted into the normalized azimuthal anisotropic elastic impedance equation to establish a statistical rock physics model, which includes:

[0069] The normalized azimuthal anisotropic elastic impedance equation is:

[0070]

[0071] In order to invert the stable data volume from the elastic impedance data volume, equation (3) is linearized:

[0072]

[0073] Where EI0=α0ρ0, where α, β, and ρ are the longitudinal wave velocity, shear wave velocity, and density, respectively; α0, β0, and ρ0 are the mean values of the longitudinal wave velocity, shear wave velocity, and density, respectively; EI and EI0 are the elastic impedance and the mean value of the elastic impedance, respectively; and 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 the azimuth, δ (V) , ε (V) ,γ (V) is the anisotropy parameter.

[0074] Substituting formula (2) into the standardized azimuthal anisotropic elastic equation (3) yields the azimuthal elastic impedance characterized by laminar density:

[0075]

[0076] Among them, wc is the laminae density, that is, the total number of laminae per unit core length (laminae / meter), T is TOC, that is, the mass of organic carbon per unit mass of rock (%), Por is the porosity, that is, the ratio of the sum of the volumes of all pore spaces in the rock sample to the volume of the rock sample (%), α is Vp, that is, the velocity of longitudinal waves in the underground medium (m / s), and longitudinal waves refer to waves in which the vibration direction of particles in the medium is parallel to the propagation direction of the wave, β is Vs, that is, the velocity of transverse waves in the underground medium (m / s), and transverse waves refer to waves in which the vibration direction of particles in the medium is perpendicular to the propagation direction of the wave, ε is the longitudinal wave anisotropy parameter, that is, the parameter describing the difference between the P-wave propagation velocity in the horizontal and vertical directions, δ is the azimuthal anisotropy parameter, and γ is the transverse wave anisotropy parameter, that is, the parameter describing the difference between the SH-wave propagation velocity in the horizontal and vertical directions.

[0077] In order to obtain stable laminar density inversion results, at least six elastic impedance data volumes with different azimuths and incident angles are required, namely:

[0078]

[0079]

[0080]

[0081]

[0082]

[0083]

[0084] Substitute the established statistical rock physics model into the Bayesian inversion framework, including:

[0085] The obtained statistical rock physics model is added to the Bayesian inversion framework. Under the Bayesian theory framework, we introduce the physical parameters and elastic impedance into the Bayesian formula and write it as follows:

[0086]

[0087] Where R = [wc, Por, T], wc, Por, T represent the lamination density, porosity and TOC respectively; m represents the elastic impedance at six different azimuths and different incident angles.

[0088] Because in practical applications, ∫P(R)P(m|R)dR plays the role of a regularization factor, which can be regarded as a constant α, then (7) can be written as follows:

[0089] P([wc,Por,T]|m)=α×P([wc,Por,T])P(m|[wc,Por,T]) (8)

[0090] The right side of the equation, p([wc,Por,T]), is called the prior distribution of the parameter to be determined, and P(m|[wc,Por,T]) is the likelihood function, which acts as a bridge between the prior distribution and the posterior distribution.

[0091] Finally, the laminar density corresponding to the maximum of (8) is the final inversion result:

[0092] [wc,Por,T]=argMaxP([wc,Por,T]|m) (9)

[0093] The lamina density is directly inverted by Bayesian method to obtain the final inversion result, which specifically includes adding the obtained statistical rock physics model into the Bayesian inversion framework to perform lamina density prestack seismic prediction to obtain the lamina density inversion result.

[0094] Figure 4 The figure shows a comparison between the two-dimensional inversion results of laminae density and the laminae density logging curve, which is used to verify the accuracy of the method. The solid line is the laminae density logging curve, and the dotted line is the laminae density inversion result. It can be seen that the trends of the laminae density logging curve and the inversion result curve are generally consistent, and the high and low values of laminae density are basically corresponding. This method can be used to predict the laminae density of shale gas reservoirs.

[0095] Figure 5 is a small-angle seismic profile. Figure 6 This is a three-dimensional profile of the laminae density inversion results. The inversion results show that the laminae density at the bottom of the K2 segment ranges from 180 to 210; the laminae density at the bottom of the K5 segment ranges from 160 to 180. The laminae density at the bottom of the K2 segment is higher than that at the bottom of the K5 segment.

[0096] Furthermore, although the steps of the methods of 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 this particular order, or that all illustrated steps must be performed to achieve desired results.

[0097] In some embodiments, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0098] Based on the same inventive concept, the presently disclosed embodiments also provide a device for predicting shale laminae density based on statistical rock physics, as described in the following embodiments. Because the principles underlying the problem solved by this device embodiment are similar to those of the aforementioned method embodiment, the implementation of this device embodiment can be referenced to the implementation of the aforementioned method embodiment, and any repetitions will not be repeated.

[0099] Figure 7 A schematic diagram of a shale lamination density prediction device based on statistical rock physics in an embodiment of the present disclosure is shown. Figure 7 As shown, the shale lamination density prediction device 500 based on statistical rock physics includes:

[0100] The data analysis module 702 is used to perform rock physics intersection analysis on the laminae density in the well logging data to determine the sensitive elastic parameters of the laminae density;

[0101] A relationship building module 704 is used to establish a linear relationship between laminae density and sensitive elastic parameters through statistical analysis;

[0102] A model building module 706 is used to substitute the linear relationship into the normalized azimuthal anisotropic elastic impedance equation to establish a statistical rock physics model;

[0103] The prediction module 708 is used to substitute the statistical rock physics model into the Bayesian inversion framework, and directly invert the laminae density by Bayesian to obtain the final inversion result.

[0104] In some embodiments, the data analysis module 702 is used to analyze the rock physics intersection diagram of laminae density and porosity, TOC, Vp, Vs, and anisotropy parameters to obtain elastic parameters that are highly sensitive to laminae density.

[0105] In some embodiments, the linear relationship is a linear fit.

[0106] In some embodiments, the normalized azimuthal anisotropic elastic impedance equation is as follows:

[0107]

[0108] Where a = 1 + tan 2 θ,b=-8Ksin 2 θ, c = 1-4k 2 sin 2 θ, k=(β / α) 2 ;

[0109] α represents the longitudinal wave velocity, β represents the shear wave velocity, ρ represents the density, α0 represents the mean value of the longitudinal wave velocity, β0 represents the mean value of the shear wave velocity, ρ0 represents the mean value of the density, EI represents the elastic impedance, EI0 represents the mean value of the elastic impedance, θ represents the angle of incidence, represents the azimuth, δ (V) , ε (V) and γ (V) represents the anisotropy parameter.

[0110] In some embodiments, the Bayesian inversion framework is formulated as follows:

[0111]

[0112] Among them, R represents the parameter to be estimated, m represents the observed sample information, p(R) represents the prior distribution, and P(m|R) represents the likelihood function.

[0113] In some embodiments, the prediction module 708 is used to substitute the statistical rock physics model into the Bayesian inversion framework, and perform pre-stack seismic direct inversion of laminae density using the Bayesian formula to obtain the final inversion result.

[0114] The concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0115] Regarding the shale lamination density prediction device based on statistical rock physics in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the shale lamination density prediction method based on statistical rock physics, and will not be elaborated here.

[0116] It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, such division is not mandatory.

[0117] In fact, according to the embodiment of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0118] Some of the blocks shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0119] Refer to the following Figure 8 To describe the electronic device provided by the embodiment of the present disclosure. Figure 8 The electronic device 800 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0120] Figure 8 FIG. 8 is a schematic diagram showing the architecture of an electronic device 800 provided by an embodiment of the present invention. Figure 8 As shown, the electronic device 800 includes but is not limited to: at least one processor 810 and at least one memory 820.

[0121] The memory 820 is used to store instructions.

[0122] In some embodiments, the memory 820 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 8201 and / or a cache memory unit 8202 , and may further include a read-only memory unit (ROM) 8203 .

[0123] In some embodiments, the 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 of which or some combination may include an implementation of a network environment.

[0124] In some embodiments, the memory 820 may store an operating system, which may be a real-time operating system (RTX), LINUX, UNIX, WINDOWS, or OS X.

[0125] In some embodiments, data may also be stored in the memory 820 .

[0126] As an example, the processor 810 may read data stored in the memory 820 . The data may be stored at the same storage address as the instruction, or the data may be stored at a different storage address than the instruction.

[0127] The processor 810 is configured to call instructions stored in the memory 820 to implement the steps described in the "Exemplary Method" section above according to various exemplary embodiments of the present disclosure. For example, the processor 810 may perform the following steps of the aforementioned method embodiment:

[0128] Conduct rock physics intersection analysis on laminae density in well logging data to determine sensitive elastic parameters of laminae density;

[0129] Through statistical analysis, a linear relationship between laminae density and sensitive elastic parameters is established;

[0130] Substitute the linear relationship into the normalized azimuthal anisotropic elastic impedance equation to establish a statistical rock physics model;

[0131] The statistical rock physics model is substituted into the Bayesian inversion framework, and the Bayesian method directly inverts the laminae density to obtain the final inversion result.

[0132] It should be noted that the processor 810 may be a general-purpose processor or a dedicated processor. The processor 810 may include one or more processing cores, and the processor 810 executes various functional applications and data processing by running instructions.

[0133] In some embodiments, the processor 810 may include a central processing unit (CPU) and / or a baseband processor.

[0134] In some embodiments, the processor 810 may determine an instruction based on the priority identifier and / or function category information carried in each control instruction.

[0135] In the present disclosure, the processor 810 and the memory 820 may be provided separately or integrated together.

[0136] As an example, the processor 810 and the memory 820 may be integrated on a single board or a system on chip (SOC).

[0137] like Figure 8 As shown, the electronic device 800 is in the form of a general-purpose computing device. The electronic device 800 may further include a bus 830 .

[0138] The bus 830 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.

[0139] The electronic device 800 may also communicate with one or more external devices 840 (e.g., a keyboard, a pointing device, a Bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 800, and / or any device that enables the electronic device 800 to communicate with one or more other computing devices (e.g., a router, a modem, etc.). Such communication may be performed through an input / output (I / O) interface 850.

[0140] Furthermore, the electronic device 800 can also communicate with one or more networks (eg, a local area network (LAN), a wide area network (WAN) and / or a public network, such as the Internet) through the network adapter 880 .

[0141] like Figure 8 As shown, the network adapter 880 communicates with other modules of the electronic device 800 via the bus 830 .

[0142] It should be understood that although not shown in the figures, other hardware and / or software modules may be used in conjunction with the electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0143] It is understood that the structure shown in the embodiment of the present disclosure does not constitute a specific limitation on the electronic device 800. In other embodiments of the present disclosure, the electronic device 800 may include Figure 8 More or fewer components may be shown, or some components may be combined or separated, or the components may be arranged differently. Figure 8The components shown can be implemented in hardware, software, or a combination of software and hardware.

[0144] The present disclosure also provides a computer-readable storage medium having computer instructions stored thereon. When the computer instructions are executed by a processor, the shale lamination density prediction method based on statistical rock physics described in the above method embodiment is implemented.

[0145] The computer-readable storage medium in the embodiments of the present disclosure is a computer instruction that can be sent, propagated or transmitted for use by or in conjunction with an instruction execution system, apparatus or device.

[0146] As an example, computer readable storage media are non-volatile storage media.

[0147] In some embodiments, more specific examples of computer-readable storage media in the present disclosure may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, a USB flash drive, a mobile hard disk, or any suitable combination of the foregoing.

[0148] In the embodiments of the present disclosure, the computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer instructions (readable program codes).

[0149] Such a propagated data signal may take any of a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof.

[0150] In some examples, computing instructions contained on a 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.

[0151] The embodiments of the present disclosure also provide a computer program product, which stores instructions. When the instructions are executed by a computer, the computer implements the shale lamination density prediction method based on statistical rock physics described in the above method embodiment.

[0152] The above instructions may be program codes. In specific implementations, the program codes may be written in any combination of one or more programming languages.

[0153] Programming languages include object-oriented programming languages such as Java, C++, etc., and conventional procedural programming languages such as "C" or similar programming languages.

[0154] The program code may execute entirely on the user's computing device, partly on the user's computing device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device or entirely on the remote computing device or server.

[0155] Where a remote computing device is involved, the remote computing device may be connected to the user computing device through any type 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., through the Internet using an Internet service provider).

[0156] The embodiment of the present disclosure further provides a chip, comprising at least one processor and an interface;

[0157] An interface for providing program instructions or data to at least one processor;

[0158] At least one processor is used to execute program instructions to implement the shale lamination density prediction method based on statistical rock physics described in the above method embodiment.

[0159] In some embodiments, the chip may further include a memory for storing program instructions and data, and the memory may be located inside or outside the processor.

[0160] Those skilled in the art will appreciate that all or part of the steps for implementing the above embodiments may be implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software, which may be collectively referred to herein as a "circuit," "module," or "system."

[0161] Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein.

[0162] This disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the appended claims.

Claims

1. A shale laminae density prediction method based on statistical rock physics, characterized in that: include: Conduct rock physics intersection analysis on laminae density in well logging data to determine sensitive elastic parameters of laminae density; Establishing a linear relationship between the laminar density and the sensitive elastic parameter through statistical analysis; Substituting the linear relationship into the normalized azimuthal anisotropic elastic impedance equation to establish a statistical rock physics model; The statistical rock physics model is substituted into the Bayesian inversion framework, and the Bayesian method directly inverts the laminae density to obtain the final inversion result.

2. The method according to claim 1, characterized in that The rock physics intersection analysis of the laminae density in the logging data to determine the sensitive elastic parameters of the laminae density includes: By analyzing the rock physics intersection diagram of laminae density with porosity, TOC, Vp, Vs, and anisotropy parameters, elastic parameters with strong sensitivity to laminae density are obtained.

3. The method according to claim 1, characterized in that The linear relationship is a linear fit.

4. The method according to claim 1, wherein The normalized azimuthal anisotropic elastic impedance equation is as follows: where a = 1 + tan 2 θ, b = -8K sin 2 θ, c = 1 - 4k 2 sin 2 θ, k = (β / α) 2 ; α represents the longitudinal wave velocity, β represents the shear wave velocity, ρ represents the density, α0 represents the mean value of the longitudinal wave velocity, β0 represents the mean value of the shear wave velocity, ρ0 represents the mean value of the density, EI represents the elastic impedance, EI0 represents the mean value of the elastic impedance, θ represents the angle of incidence, represents the azimuth, δ (V) , ε (V) and γ (V) represents the anisotropy parameter.

5. The method according to claim 1, wherein The Bayesian inversion framework is formulated as follows: Among them, R represents the parameter to be estimated, m represents the observed sample information, p(R) represents the prior distribution, and P(m|R) represents the likelihood function.

6. The method according to claim 1, characterized in that Substituting the statistical rock physics model into the Bayesian inversion framework, Bayesian direct inversion of laminae density is performed to obtain the final inversion result, including: The statistical rock physics model is substituted into the Bayesian inversion framework, and the prestack seismic direct inversion of laminae density is performed using the Bayesian formula to obtain the final inversion result.

7. A shale lamination density prediction device based on statistical rock physics, characterized in that: include: Data analysis module, used to perform rock physics intersection analysis on laminae density in logging data and determine sensitive elastic parameters of laminae density; A relationship building module, configured to establish a linear relationship between the laminar density and the sensitive elastic parameter through statistical analysis; A model building module is used to substitute the linear relationship into the normalized azimuthal anisotropic elastic impedance equation to establish a statistical rock physics model; The prediction module is used to substitute the statistical rock physics model into the Bayesian inversion framework, and directly invert the laminae density by Bayesian to obtain the final inversion result.

8. An electronic device, characterized in that: include: a memory for storing instructions; A processor is used to call the instructions stored in the memory to implement the shale lamination density prediction method based on statistical rock physics as described in any one of claims 1 to 6.

9. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the computer instructions are executed by a processor, the shale lamination density prediction method based on statistical rock physics described in any one of claims 1 to 6 is implemented.

10. A chip, characterized in that: comprising at least one processor and an interface; The interface is configured to provide program instructions or data to the at least one processor; The at least one processor is configured to execute the program instructions to implement the shale lamination density prediction method based on statistical rock physics as described in any one of claims 1 to 6.

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