Method and device for calculating equivalent skeleton modulus of polyphase mineral based on random structure cloud

Abstract

This invention relates to the field of characterization of equivalent mechanical properties of rock-physical composite minerals, specifically a method and apparatus for calculating the equivalent framework modulus of multiphase minerals based on random structure clouds. The method includes: when the current sampling count is less than the total sampling count of the random structure cloud, randomly dividing all phases of the multiphase mineral into blocks according to a probability density function, determining the sub-block parameters of each sub-block, and forming an initial structure pool; performing multiple rounds of recursive iteration by merging two phases in the structure pool until the total number of sub-blocks in the structure pool equals 1, and determining the equivalent modulus of the currently sampled sample using the sub-block parameters of the unique sub-block. This invention does not directly calculate the equivalent modulus set; instead, it calculates the equivalent mineral framework modulus by constructing a set of equivalent framework moduli that can be realized through random structure cloud computing, given the phase parameters of each phase. Furthermore, without introducing specific microstructure assumptions, it implements an adjustable structure bias parameterization mechanism, achieving effective constraints on the equivalent modulus of multiphase mineral samples.

Description

Technical Field

[0001] This invention relates to the field of rock physics composite mineral equivalent mechanical property characterization technology, and is a method and apparatus for calculating the equivalent skeleton modulus of multiphase minerals based on random structure clouds. Background Technology

[0002] Macroscopic equivalent elastic parameters (equivalent bulk modulus) of multiphase composite media (such as rock mineral framework, particle / matrix composite materials, etc.) shear modulus Young's modulus Poisson's ratio (etc.) are fundamental input parameters characterizing the strength of the formation matrix skeleton, inversion of elastic mechanical parameters, simulation of seismic elastic response, and calculation of solid-fluid coupled medium properties. In engineering, the volume content of each mineral phase can usually be obtained through X-ray diffraction (XRD) or scanning electron microscopy (SEM), and a reasonable range of reliable equivalent modulus can be calculated based on the reference elastic parameters of each individual mineral.

[0003] Existing methods for calculating equivalent modulus mainly include: (1) Single-value prediction methods, such as Mori-Tanaka method, self-consistent model optimization, and generalized differential effective medium (DEM), introduce the "inclusion-matrix" configuration or the idea of ​​successive incremental addition, and combine the corresponding integral equation or self-consistent solution to give the single-value prediction results of the equivalent modulus under specific microstructure assumptions.

[0004] The above-mentioned single-value prediction method relies on structural assumptions to predict the equivalent modulus. However, due to the uncertainty of the quantified structure, the achievable range is limited, and the method is poorly adaptable to complex / high-content / highly interconnected media.

[0005] (2) Boundary estimation method When microstructure information is lacking, the most common rough upper and lower bounds of the skeleton modulus are the Voigt iso-strain boundary and the Reuss iso-stress boundary, and the boundary is compromised by Hill averaging (i.e., Voigt-Reuss-Hill averaging, VRH). However, such methods calculate the boundary span to a great extent and cannot reflect the mutual influence of components and the geometric distribution.

[0006] Hashin & Shtrikman (Reference: HASHIN Z, SHTRIKMAN SA variational approach to the theory of the elastic behavior of multiphase materials[J]. Journal of the Mechanics and Physics of Solids, 1963, 11(2):127-140.DOI:10.1016 / 0022-5096(63)90060-7.) derived the optimal upper and lower bounds of bulk modulus and shear modulus for two-phase and multiphase generalizations through variational principles, which became the basis for a large number of subsequent equivalent medium research and engineering applications. Subsequently, the Mori-Tanaka method (reference: MORIT, TANAKA K. Average stress in matrix and average elastic energy of materials with misfitting inclusions[J]. Acta Metallurgica, 1973, 21(5): 571-574. DOI: 10.1016 / 0001-6160(73)90064-3) and self-consistent model optimization (reference: IWAKUMA T, KOYAMA S. An estimate of average elastic moduli of composites and polycrystals[J]. Mechanics of Materials, 2005, 37(4): 459-472. DOI: 10.1016 / j.mechmat.2004.03.003.) and generalized differential equivalent medium (reference: NORRIS AN, CALLEGARI AJ, SHENGP. A generalized differential effective medium theory[J]. Journal of the Mechanics and Physics of Solids, 1985, 33(6):525-543.DOI:10.1016 / 0022-5096(85)90001-8) and other methods, by introducing the idea of ​​"inclusion-matrix" or incremental addition, more specific predictions (single-value predictions) under certain structural assumptions are achieved through complex calculations. Existing modulus estimation of complex porous media and solid-fluid multiphase coupled media usually uses the calculated value of the Hashin-Shtrikman boundary (hereinafter referred to as HS boundary) as the skeleton base value input to realize the constraint of the equivalent modulus boundary.

[0007] Existing modulus estimation methods for complex porous media and solid-fluid multiphase coupled media typically use the calculated HS boundary value as the skeleton base value input to achieve the constraint of the equivalent modulus boundary.

[0008] The classical HS boundary relies on the "well-ordered" assumption, meaning that the relationship between the bulk modulus and shear modulus is consistent between the two phases (e.g., ...). Sometimes it is necessary (or vice versa). However, in actual mineral assemblages, "non-isochronous" situations often occur, that is: This makes it impossible to implement HS boundaries in applications, leading to difficulties in boundary applicability and interpretation.

[0009] Current research on the effective modulus boundary contraction and applicability extension mainly falls into two categories: one is the extension of the applicability of the HS boundary, for example, Milton & Phan-Thien (reference: MILTON GW, PHAN-THIEN N. Newbounds on effective elastic modulus of two-component materials[J]. Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences, 1982, 380(1779):305-331. DOI:10.1098 / rspa.1982.0044) proved that the HS boundary in... It is not necessary to strictly meet the requirements. Instead, it applies to all negative values ​​related to the two-phase modulus; Walpole (in the literature WALPOLE L J. Onbounds for the overall elastic moduli of inhomogeneous systems—I[J]. Journal of the Mechanics and Physics of Solids, 1966, 14(3): 151-162. DOI: 10.1016 / 0022-5096(66)90035-4.) defined the upper and lower bounds of the equivalent modulus under the non-iso-order assumption (called the HSW boundary), whose bulk modulus boundary is equivalent to the HS boundary, while the shear modulus boundary is wider than the HS boundary; however, the feasibility and compactness of the shear modulus boundary of this method have more complex conditions and limitations. Another type is boundary shrinkage work based on simplified assumptions or optimized / complexed theoretical models. For example, Berryman & Milton (Reference BERRYMAN JG, MILTON G W. Microgeometry of random composites and porous media[J]. Journal of Physics D: Applied Physics, 1988, 21(1):87.DOI:10.1088 / 0022-3727 / 21 / 1 / 013) obtained an effective boundary lower than the HS range (called BM correction) based on the key parameters of the material's micro-geometry and through the three-point correlation integral analytical method.

[0010] However, all the methods mentioned above provide a provable range of optimal inequalities at a given level of information, but do not inherently guarantee that all boundary points (especially corner points) have corresponding realizable microstructures; for example, in the two-phase and homo-order cases, only certain special structures can approximate or reach the HS boundary in the limiting sense; even if reachability holds, the bulk modulus K and shear modulus G The extreme values ​​are also strongly coupled to the same microstructure, meaning that the modulus set is usually It is a coupled domain of a plane, not the direct product of two independent intervals. And in non-isolated problems... Planar coupling leads to a more pronounced problem of clearly defined but unrealizable formal boundaries. Therefore, explicit constraints on the shear modulus require higher-order / complex expressions. For example, Chinh (CHINH P D. Bounds on the elastic moduli of statistically isotropic multicomponent materials and random cell polycrystals[J]. International Journal of Solids and Structures, 2012, 49(18):2646-2659.DOI:10.1016 / j.ijsolstr.2012.05.021) derived the elastic modulus boundary of random multicomponent crystals using the principles of minimum energy and complementary energy minimization. Milton et al. (MILTON G, BRIANE M, HARUTYUNYAN D. On the possible effective elasticity tensors of 2-dimensional and 3-dimensional printed materials[J]. Mathematics and Mechanics of Complex Systems, 2017, 5(1): 41-94. DOI: 10.2140 / memocs.2017.5.41) Based on the same principle, the effective elastic modulus set boundary of two phases (one of which is a microporous phase) was fully characterized, and the boundary value was realized by using multilayer stacked structure 3D printing materials.

[0011] The above-mentioned boundary estimation methods can provide strict theoretical boundaries for modulus. However, such boundaries are external envelopes and cannot directly reflect the true geometry, distribution trend, and boundary reachability of the set of achievable equivalent moduli. Specific microstructure assumptions need to be introduced to calculate definite equivalent moduli that are closer to real rock physics measurements.

[0012] Existing patent literature utilizing the Hashiin-Shtrikman boundary includes: Chinese patent document CN120722431B discloses a method for simultaneously predicting the elastic parameters of shale solid skeleton and organic matter, comprising: constructing a rock physics model characterizing the elastic modulus of the shale reservoir solid skeleton and the elastic modulus of organic matter; developing a well inversion method based on the rock physics model; establishing a reflection coefficient formula linearized from the elastic modulus of the shale solid skeleton, the elastic modulus of organic matter, and density parameters by combining the rock physics model and scattering function theory; developing a seismic rock physics inversion method by combining the reflection coefficient formula and Bayesian linear inversion theory; testing and verifying the effectiveness of the seismic rock physics inversion method constructed in step E using synthetic seismic data; and then applying it to actual seismic data to predict the elastic modulus of the solid skeleton and organic matter of shale gas reservoirs in the study area. In this invention, the HS boundary model is used for the inversion prediction of the shale gas reservoir model.

[0013] Chinese patent document CN114510807B discloses a method and apparatus for predicting hydrate saturation based on microscopic occurrence morphology. The method includes: determining the proportion of various microscopic occurrence morphologies of hydrates based on resistivity or NMR logging interpretation of hydrate saturation and actual measured P-wave and S-wave velocities; using the logging-interpreted hydrate saturation as an initial value, and combining this proportion, cyclically determining the P-wave and S-wave velocity error values ​​for each cycle; obtaining the calculated P-wave and S-wave velocities for the current cycle based on the current cycle's hydrate saturation value and a pre-established unified contact cementation model; determining the error between the calculated and actual measured P-wave and S-wave velocities for the current cycle; providing the hydrate saturation value for the next cycle if the cycle termination condition is not met; and comparing the P-wave and S-wave velocity error values ​​for each cycle if the condition is met to obtain the predicted hydrate saturation value. In this invention, the effective medium model is simply used to construct the elastic parameters of the shale matrix and shale dry skeleton.

[0014] Chinese patent document CN109655940B discloses a method for modeling anisotropic rock physics models of shale. The method includes: treating the shale matrix as a mixture of brittle minerals, organic matter, and clay; treating clay particles as anisotropic elements with a fixed elastic stiffness matrix, and introducing a clay particle orientation index to characterize the degree of directional arrangement of clay particles; classifying the total shale porosity into three types: brittle pores, clay pores, and organic matter pores; obtaining the elastic parameters of the mixture of porous brittle minerals and porous organic matter; calculating the elastic parameters of the mixture of brittle minerals and organic matter; calculating the elastic parameters of the porous clay medium containing bound water; calculating the equivalent elastic parameters of the shale matrix and the dry shale rock skeleton; and obtaining the equivalent elastic parameters of fluid-saturated shale based on the equivalent elastic parameters of the shale matrix and the dry shale rock skeleton, thus completing the construction of an anisotropic rock physics model of shale under fluid saturation. This invention utilizes an improved contact cementation model and the HS boundary model to predict hydrate saturation.

[0015] US Patent No. US9482771B2 discloses a method for indicating the presence of gas hydrate and shallow gas in a deepwater environment. In this invention, the HS boundary model is used for inversion prediction of shale gas reservoir models.

[0016] Chinese patent document CN110824556B discloses a method for establishing a rock physical model of unconventional tight sandstone reservoirs. This invention utilizes a large amount of experimental test data from tight sandstone core samples and, based on the equivalent medium theory, accurately characterizes the rock physical features of unconventional tight sandstone reservoirs through four steps: establishing a rock skeleton model, establishing a mixed fluid model, establishing an isotropic saturated rock model, and establishing a rock physical model of unconventional tight sandstone reservoirs.

[0017] Chinese patent document CN111767650B discloses a method and apparatus for estimating equivalent elastic parameters of digital rock cores. The method includes obtaining digital rock core images from a scanning device, performing threshold segmentation analysis on the digital rock core images to obtain multiple mineral types, and constructing a pressureless digital rock core model based on the multiple mineral types; performing elastic wave simulation analysis on the pressureless digital rock core model to obtain an isotropic elastic wave first-order velocity-strain difference equation; processing the pressureless digital rock core model using a watershed algorithm based on the relationship between particle edges and pressure to obtain multiple pressured digital rock core models; simulating propagation in the multiple pressured digital rock core models according to the isotropic elastic wave first-order velocity-strain difference equation to obtain an equivalent velocity average value; and performing a correction analysis on the equivalent velocity average value to obtain a correction result.

[0018] None of the aforementioned patent documents provide a technical solution for controlling the structural bias of two-phase mixtures and calculating the equivalent framework modulus of multiphase minerals without introducing specific microstructure assumptions. Summary of the Invention

[0019] This invention provides a method and apparatus for calculating the equivalent framework modulus of multiphase minerals based on random structure clouds, which overcomes the shortcomings of the prior art and can effectively solve the problem of how to achieve structural bias control of two-phase mixing without introducing specific microstructure assumptions.

[0020] Without relying on explicit geometric modeling or structural constraints One of the technical solutions of this invention is achieved through the following measures: a method for calculating the equivalent framework modulus of multiphase minerals based on random structure clouds, comprising: Determine if the current sampling count is less than the total sampling count of the random structure cloud; Therefore, for the currently sampled sample, a probability density function is constructed based on the number of single-phase blocks and the Dirichlet distribution parameters. Based on the probability density function, all phases of the multiphase mineral are randomly divided into blocks within each phase. The sub-block parameters of each sub-block are determined to form an initial structure pool, where the sub-block parameters include bulk modulus, shear modulus and absolute volume fraction. The structure pool undergoes multiple rounds of recursive iteration involving two-phase merging until the total number of sub-blocks in the structure pool equals 1. The equivalent modulus of the currently sampled sample is determined using the parameters of the unique sub-block. The sampling count is incremented by 1, and the process returns to determine the sampling count. The recursive iteration involving two-phase merging involves randomly selecting two sub-blocks from different phases in the structure pool, combining the output modulus model and the corresponding sub-block parameters to construct a new equivalent sub-block, and then replacing the two randomly selected sub-blocks in the structure pool with the new equivalent sub-block. The output modulus model is shown below: in, , These are the bulk modulus and shear modulus of the new sub-block, respectively. , , , These are the upper and lower boundaries of the bulk modulus and the upper and lower boundaries of the shear modulus corresponding to two randomly selected sub-blocks, respectively. , Using the Beta probability density distribution parameters and KG The bulk modulus structural parameters and shear modulus structural parameters are determined by the coupling parameters.

[0021] The following are further optimizations and / or improvements to the above-mentioned technical solution: If the current sampling count is less than the total sampling count of the random structure cloud, then for the currently sampled sample, a probability density function is constructed based on the number of single-phase blocks and the Dirichlet distribution parameters. Based on the probability density function, all phases of the multiphase mineral are randomly divided into blocks within each phase, and the sub-block parameters of each sub-block are determined to form an initial structure pool, including: Construct a probability density function based on the number of single-phase blocks and the Dirichlet distribution parameters; Based on the probability density function, the first i The phase is randomly divided into blocks within the phase. Combined with the volume fraction of the phase, the absolute volume fraction of each sub-block in the phase is obtained, and then the sub-block parameters of each sub-block are determined. The sub-block parameters include bulk modulus.K i shear modulus G i and absolute volume fraction ; in, For the first i Xiangdi j The absolute volume fraction of each sub-block; For the first i Xiangdi j 3D random variable; For the first i Phase volume fraction; This represents the number of single-phase blocks. Repeat the above steps to randomly divide all phases of the multiphase mineral into blocks, determine the sub-block parameters of each sub-block, and form the initial structure pool.

[0022] The above process involves multiple rounds of recursive iterations of two-phase merging in the structure pool until the total number of sub-blocks in the structure pool equals 1. The equivalent modulus of the currently sampled sample is determined using the sub-block parameters of the unique sub-block. The sampling count is incremented by 1, and the process returns to determine the sampling count, including: Two sub-blocks of different phases are randomly selected from the structure pool, which are: in, The parameters for a sub-block are, in order, bulk modulus, shear modulus, and absolute volume fraction; For the sub-block parameters of another sub-block; Substituting the sub-block parameters of the two sub-blocks into the boundary calculation model, we obtain the upper and lower boundaries of the bulk modulus and the upper and lower boundaries of the shear modulus. The boundary calculation model is established using the equivalent boundary model. Input the upper and lower boundaries of the bulk modulus, the upper and lower boundaries of the shear modulus, the structural parameters of the bulk modulus, and the structural parameters of the shear modulus into the output modulus model to obtain the bulk modulus and shear modulus of the new sub-block. The output modulus model is shown below: in, , These are the bulk modulus and shear modulus of the new sub-block, respectively. , , , These are the upper and lower boundaries of the bulk modulus and the upper and lower boundaries of the shear modulus corresponding to two randomly selected sub-blocks, respectively. , These are the bulk modulus structural parameters and shear modulus structural parameters determined using the Beta probability density distribution parameters and the KG coupling parameters, respectively. Using the bulk modulus and shear modulus of the new sub-block, a new equivalent sub-block is constructed, with corresponding sub-block parameters as follows: Then, two randomly selected sub-blocks in the structure pool are replaced with new equivalent sub-blocks, and the structure pool is updated, where the absolute volume fraction of the new sub-blocks is... ; Determine if the total number of sub-blocks in the updated structure pool is equal to 1; If the response is negative, return to the next round of two-phase merging recursive iteration; The response then outputs the sub-block parameters of the unique sub-block in the current structure pool to determine the equivalent modulus of the sample obtained in this sampling.

[0023] The above utilizes Beta probability density distribution parameters and KG The coupling parameters determine the bulk modulus structural parameters and the shear modulus structural parameters, as shown in the following formula: in, The bulk modulus structural parameter follows a Beta probability density distribution; , These are the morphological parameters of the Beta distribution; For the Gamma function, t For integration variables; in, Shear modulus structural parameters, subject to KG Coupling parameters and Joint control.

[0024] The equivalent boundary model is shown below: in, include: in, , , , Based on the upper and lower boundaries of the bulk modulus and shear modulus corresponding to two randomly selected sub-blocks, respectively; "+" indicates the upper bound, "is the lower boundary; and These are the maximum and minimum values, respectively. This is the singularity calibration value; in, , .

[0025] The above outputs the sub-block parameters of the unique sub-block in the current structure pool. The formula for calculating the equivalent modulus of the sample obtained in this sampling is as follows: in, Equivalent bulk modulus; Equivalent shear modulus; It is the equivalent Poisson's ratio; For equivalent Young's model; , These are the bulk modulus and shear modulus of the unique sub-block in the current structure pool, respectively.

[0026] The above also includes determining the equivalent framework modulus of multiphase minerals in response to the current sampling number being not less than the total sampling number of the random structure cloud, including: The equivalent moduli of the samples obtained from each sampling are combined to form a random structure cloud; in, It is a cloud with a random structure; This represents the total number of samples taken from the random structure cloud. Let be the equivalent modulus of the sample obtained from the Sth sampling. The mean equivalent modulus of random structured clouds is used as the equivalent framework modulus of multiphase minerals, and the effective boundary of each equivalent modulus is determined. in, , , , These are the average equivalent bulk modulus, average equivalent shear modulus, average equivalent Poisson's ratio, and average equivalent Young's modulus, respectively, forming the equivalent framework modulus of multiphase minerals.

[0027] The above also includes obtaining parameters related to random structure clouds and phase parameters of each phase of multiphase minerals. The parameters related to random structure clouds include the total number of random structure cloud samplings, the number of single-phase blocks, Dirichlet distribution parameters, Beta distribution morphological parameters, and KG coupling parameters. The phase parameters include bulk modulus, shear modulus, and volume fraction.

[0028] The second technical solution of the present invention is achieved through the following measures: a multiphase mineral equivalent skeleton modulus calculation device based on random structure clouds, comprising: The sampling count determination unit determines whether the current sampling count is less than the total sampling count of the random structure cloud. If the current sampling count is less than the total sampling count of the random structure cloud, then for the currently sampled sample, a probability density function is constructed based on the number of single-phase blocks and the Dirichlet distribution parameters. Based on the probability density function, all phases of the multiphase mineral are randomly divided into blocks within each phase, and the sub-block parameters of each sub-block are determined to form the initial structure pool. The sub-block parameters include bulk modulus, shear modulus, and absolute volume fraction. The sample equivalent modulus determination unit performs multiple rounds of two-phase merging recursive iterations on the structure pool until the total number of sub-blocks in the structure pool equals 1. The equivalent modulus of the currently sampled sample is determined using the sub-block parameters of this unique sub-block. The sampling count is incremented by 1, and the process returns to determine the sampling count. The two-phase merging recursive iterations involve randomly selecting two sub-blocks of different phases from the structure pool, combining the output modulus model and the corresponding sub-block parameters to construct a new equivalent sub-block, and then replacing the two randomly selected sub-blocks in the structure pool with the new equivalent sub-block. The output modulus model is shown below: in, , These are the bulk modulus and shear modulus of the new sub-block, respectively. , , , These are the upper and lower boundaries of the bulk modulus and the upper and lower boundaries of the shear modulus corresponding to two randomly selected sub-blocks, respectively. , Using the Beta probability density distribution parameters and KG The bulk modulus structural parameters and shear modulus structural parameters are determined by the coupling parameters.

[0029] The following are further optimizations and / or improvements to the above-mentioned technical solution: The above also includes an equivalent skeleton modulus determination unit and a basic data acquisition unit; The equivalent framework modulus determination unit determines the equivalent framework modulus of multiphase minerals when the current sampling count is not less than the total sampling count of the random structure cloud, including: The equivalent moduli of the samples obtained from each sampling are combined to form a random structure cloud; in, It is a cloud with a random structure; This represents the total number of samples taken from the random structure cloud. Let be the equivalent modulus of the sample obtained from the Sth sampling. The mean equivalent modulus of random structured clouds is used as the equivalent framework modulus of multiphase minerals, and the effective boundary of each equivalent modulus is determined. in, , , , These are the average equivalent bulk modulus, average equivalent shear modulus, average equivalent Poisson's ratio, and average equivalent Young's modulus, respectively, forming the equivalent framework modulus of multiphase minerals; The basic data acquisition unit acquires parameters related to random structure clouds and phase parameters of each phase of multiphase minerals. The parameters related to random structure clouds include the total number of random structure cloud samples, the number of single-phase blocks, Dirichlet distribution parameters, and Beta distribution morphological parameters. KG Coupling parameters, including phase parameters such as bulk modulus, shear modulus, and volume fraction.

[0030] The method for calculating the equivalent framework modulus of multiphase minerals based on random structure clouds disclosed in this invention has the following beneficial effects: A set of realizable moduli is constructed using a set of computable structure assumptions. Specifically, instead of directly calculating the equivalent modulus set, the set of realizable equivalent skeleton moduli is constructed by building a set of realizable equivalent skeleton moduli using random structure cloud computing, under the condition that only the phase parameters of each phase of the multiphase mineral are known. The statistical characteristics of the set of realizable equivalent skeleton moduli are then used to constrain the realizable boundary of the mixed mineral skeleton modulus and to calculate the equivalent mineral skeleton modulus.

[0031] The introduction of probabilistic structures and structure family sampling, specifically: Based on the Dirichlet distribution, a random distribution method with controllable key parameters was set up to randomly divide the volume fraction of each phase into blocks. The mechanism is that, while strictly maintaining the conservation of the overall volume fraction, equivalent sub-units of different scales and levels are generated as sub-blocks in a statistical sense for subsequent recursive iteration of merging two phases. This process is equivalent to sampling possible multi-scale structural combinations without introducing geometric information, thus avoiding the problem that the contact order, aggregation scale and mixing level between different phases in a multi-phase mineral system will affect the final equivalent response. Without introducing specific microstructure assumptions, a controllable structure bias parameterization mechanism was implemented. Specifically, multiple rounds of two-phase merging recursive iterations were performed on the structure pool. In each round of two-phase merging recursive iterations, a bulk modulus structural parameter following a Beta probability density distribution was adopted. Randomly select values ​​for bulk modulus By altering the pre-input of the Beta distribution morphological parameters, the structural parameters are influenced, thereby changing the distribution of the bulk modulus. KG coupling parameters are used to control the shear modulus structural parameters. The random value is used to determine the shear modulus of KG coupling under two-phase mixing, ultimately achieving the desired value under two-phase mixing. KGControl of coupled modulus distribution. Furthermore, in each recursive iteration of two-phase merging calculation, not only are the bulk modulus and shear modulus always guaranteed to remain within the HSW boundary interval, but structural parameters also act simultaneously on K and G, ensuring that the output modulus of the new sub-block is always synchronously located within a certain realizable structural group. This allows the present invention to effectively constrain the equivalent modulus of multiphase mineral samples without relying on explicit geometric modeling or structural constraints. It should be noted that the present invention is not affected by the selection of a specific distribution type. Using other distributions to adjust the volume fraction of each phase block, or to adjust the structural bias during two-phase mixing, or to adjust... KG Coupled parameter control can be achieved. Attached Figure Description

[0032] Appendix Figure 1 This is a schematic diagram of the process for calculating the equivalent skeleton modulus of multiphase minerals based on random structure clouds, as disclosed in Embodiment 1 of the present invention.

[0033] Appendix Figure 2 This is a schematic diagram of the initial structure pool formation method disclosed in Embodiment 2 of the present invention.

[0034] Appendix Figure 3 This is a schematic diagram of the process for obtaining the equivalent modulus of the sample obtained from each sampling, as disclosed in Embodiment 3 of the present invention.

[0035] Appendix Figure 4 This is a schematic diagram of the process for calculating the equivalent skeleton modulus of multiphase minerals based on random structure clouds, as disclosed in Embodiment 4 of the present invention.

[0036] Appendix Figure 5 The difference disclosed in Embodiment 5 of the present invention KG A schematic diagram illustrating the influence of coupling parameters on the equivalent modulus distribution of random structured clouds.

[0037] Appendix Figure 6 This is a schematic diagram illustrating the influence of the Dirichlet distribution parameters on the equivalent modulus distribution of a random structured cloud as disclosed in Embodiment 5 of the present invention.

[0038] Appendix Figure 7 This is a schematic diagram illustrating the influence of the Beta distribution morphological parameters on the equivalent modulus distribution of a random structured cloud, as disclosed in Embodiment 5 of the present invention.

[0039] Appendix Figure 8 This is a schematic diagram illustrating the influence of the number of single-phase blocks on the equivalent modulus distribution of a random structure cloud as disclosed in Embodiment 5 of the present invention.

[0040] Appendix Figure 9 This is a schematic diagram of the calculation results of the equivalent skeleton modulus of the components measured by XRD of rock fragments from sample X, as disclosed in Embodiment 6 of the present invention.

[0041] Appendix Figure 10This is a schematic diagram of the structure of the multiphase mineral equivalent skeleton modulus calculation device based on random structure cloud disclosed in Embodiment 7 of the present invention.

[0042] Appendix Figure 11 This is a schematic diagram of the multiphase mineral equivalent skeleton modulus calculation device based on random structure cloud disclosed in Embodiment 8 of the present invention. Detailed Implementation

[0043] The present invention is not limited to the following embodiments, and the specific implementation can be determined according to the technical solution of the present invention and the actual situation.

[0044] Those skilled in the art will understand that, unless specifically stated otherwise, in the embodiments of the present invention, a "module" or "unit" refers to a computer program or part of a computer program that has a predetermined function and works together with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0045] In addition, in the embodiments of the present invention, "multiple" refers to two or more, and "first" and "second" are used to distinguish descriptions and should not be construed as implying relative importance.

[0046] Based on this, the technical solution of the present invention will be described and explained below with reference to several examples.

[0047] Example 1: As shown in the attached document Figure 1 As shown in the figure, this invention discloses a method for calculating the equivalent framework modulus of multiphase minerals based on random structure clouds, including: Step S110: Determine whether the current sampling count is less than the total sampling count of the random structure cloud; Step S120, Response: Then, for the currently sampled sample, a probability density function is constructed based on the number of single-phase blocks and the Dirichlet distribution parameters. Based on the probability density function, all phases of the multiphase mineral are randomly divided into blocks within the phase, and the sub-block parameters of each sub-block are determined to form an initial structure pool. The sub-block parameters include bulk modulus, shear modulus and absolute volume fraction. Step S130: Perform multiple rounds of two-phase merging recursive iterations on the structure pool until the total number of sub-blocks in the structure pool equals 1. Determine the equivalent modulus of the currently sampled sample using the sub-block parameters of the unique sub-block. Increment the sampling count by 1 and return to perform the sampling count judgment. The two-phase merging recursive iteration includes randomly selecting two sub-blocks of different phases in the structure pool, combining the output modulus model and the corresponding sub-block parameters to construct a new equivalent sub-block, and replacing the two randomly selected sub-blocks in the structure pool with the new equivalent sub-block. The output modulus model is shown below: in, , These are the bulk modulus and shear modulus of the new sub-block, respectively. , , , These are the upper and lower boundaries of the bulk modulus and the upper and lower boundaries of the shear modulus corresponding to two randomly selected sub-blocks, respectively. , Using the Beta probability density distribution parameters and KG The bulk modulus structural parameters and shear modulus structural parameters are determined by the coupling parameters.

[0048] In this embodiment, the total number of samplings for the random structure cloud is set as needed, and each sampling will yield a corresponding sample.

[0049] In this embodiment, a probability density function is constructed based on the number of single-phase blocks and the Dirichlet distribution parameters. Based on the probability density function, all phases of the multiphase mineral are randomly divided into blocks within each phase, that is, the volume fraction of each phase is randomly divided based on the probability density function.

[0050] This invention discloses a method for calculating the equivalent framework modulus of multiphase minerals based on random structure clouds. Without introducing specific microstructure assumptions, i.e., without relying on explicit geometric modeling or structural constraints, it achieves the distribution and statistical information of equivalent modulus samples. Specifically: Dirichlet distribution is used to randomly divide each phase into blocks. While strictly maintaining the conservation of the overall integral, sub-blocks of different scales and levels are statistically generated. This allows for sampling of possible multi-scale structural combinations without introducing geometric information, avoiding the problem that the contact order, aggregation scale, and mixing level between different phases in a multiphase mineral system can affect the final equivalent response. In multiple rounds of recursive iteration for two-phase merging, upper and lower boundaries of bulk modulus and shear modulus are introduced, utilizing Beta probability density distribution parameters and... KGThe bulk modulus and shear modulus structural parameters, determined by the coupling parameters, ensure that the bulk modulus and shear modulus always remain within the boundary interval during the two-phase merging recursive iteration. This achieves structural bias control of the two-phase mixture without relying on explicit geometric modeling or structural constraints, allowing the structural parameters to act simultaneously on... K and G The output modulus is always synchronously located within a certain realizable structure group.

[0051] Example 2: As shown in the attached document Figure 2 As shown, this embodiment of the invention is a further optimization of the above embodiment. If the current sampling count is less than the total sampling count of the random structure cloud, then for the currently sampled sample, a probability density function is constructed based on the number of single-phase blocks and the Dirichlet distribution parameters. Based on the probability density function, all phases of the multiphase mineral are randomly divided into blocks within each phase, and the sub-block parameters of each sub-block are determined to form an initial structure pool, including: Step S210: Construct a probability density function based on the number of single-phase blocks and the Dirichlet distribution parameters. Specifically: For the first i Phase, generate a n b dimensional random variables , denoted as: The support domain of the above equation is: in, for n b The set of nonnegative real numbers; j For the first j Dimensions, ranging from 1 to n b , n b This represents the number of single-phase blocks. For any Its probability density function is: in, The Gamma function is defined as follows: ; As a variable; Step S220, according to the probability density function, the first... i The phase is randomly divided into blocks within the phase. Combined with the volume fraction of the phase, the absolute volume fraction of each sub-block in the phase is obtained, and then the sub-block parameters of each sub-block are determined. The sub-block parameters include bulk modulus. K i shear modulus Gi and absolute volume fraction ; In this step, the probability density function is used to determine the th sample obtained from the s-th sampling. i The expected volume fraction of each sub-block corresponding to the relative sub-block size of the phase (volume fraction of that phase) ,variance That is, the expected distribution of sub-blocks of any phase is equal, but the degree of dispersion is affected by variables. control( The smaller the value, the greater the relative difference in the volume fraction of the resulting sub-blocks may be.

[0052] The absolute volume fraction of each sub-block in this step is shown below: in, For the first i Xiangdi j The absolute volume fraction of each sub-block; For the first i Xiangdi j 3D random variable; For the first i Phase volume fraction; This represents the number of single-phase blocks. Step S230: Repeat the above steps to randomly divide all phases of the multiphase mineral into blocks, determine the sub-block parameters of each sub-block, and form an initial structure pool. in, This is the initial structure pool; N The total number of phases; This represents the total number of samples taken from the random structure cloud. k The number of recursive iterations for merging the two phases is initialized to . k=0 .

[0053] It should also be noted that the initial structure pool has a total of There are 3 sub-blocks, each representing a single mineral particle in the sample obtained from the s-th sampling.

[0054] In this embodiment, a random distribution method with controllable key parameters is set based on the Dirichlet distribution to randomly divide the volume fraction of each phase into blocks. The mechanism is to generate equivalent sub-units of different scales and levels as sub-blocks in a statistical sense while strictly maintaining the conservation of the overall volume fraction. These sub-blocks are used for subsequent recursive iteration of merging two phases. This process is equivalent to sampling possible multi-scale structural combinations without introducing geometric information, thus avoiding the problem that the contact order, aggregation scale, and mixing level between different phases in a multi-phase mineral system will affect the final equivalent response.

[0055] Example 3: As shown in the attached document Figure 3 As shown, this embodiment of the invention is a further optimization of the above embodiment. It involves performing multiple rounds of two-phase merging recursive iterations on the structure pool until the total number of sub-blocks in the structure pool equals 1. The equivalent modulus of the currently sampled sample is determined using the sub-block parameters of the unique sub-block. The sampling count is incremented by 1, and the process returns to determine the sampling count. This includes: Step S310: Randomly select two sub-blocks of different phases from the structure pool, i.e.: in, The parameters for a sub-block are, in order, bulk modulus, shear modulus, and absolute volume fraction; For the sub-block parameters of another sub-block.

[0056] It should also be noted that, for ease of calculation, in the selection and combination of sub-blocks, the sequence of sub-blocks with the smaller shear modulus is always set as follows. a The larger one is b That is, satisfying ; Step S320: Substitute the sub-block parameters of the two sub-blocks into the boundary calculation model to obtain the upper and lower boundaries of the bulk modulus and the upper and lower boundaries of the shear modulus. The boundary calculation model is established using the equivalent boundary model.

[0057] In this step, the equivalent boundary model can be selected as needed, and may include HSW boundary, BM correction, HS boundary, or other equivalent boundary models; in this embodiment, the boundary calculation model is illustrated using the HSW boundary as an example, as follows: in, include: in, , , , Based on the upper and lower boundaries of the bulk modulus and shear modulus corresponding to two randomly selected sub-blocks, respectively; "+" indicates the upper bound, "is the lower boundary; and These are the maximum and minimum values, respectively. This is the singularity calibration value; in, , .

[0058] It should be noted that "+" indicates the upper bound; " "Indicates the lower bound; and These represent taking the maximum / minimum value respectively; The singularity calibration value can be set to [value] in this embodiment. .

[0059] The above ,Right now When this happens, the above equation degenerates into the original HS boundary.

[0060] Step S330, using the Beta probability density distribution parameters and KG The coupling parameters determine the bulk modulus structural parameters and the shear modulus structural parameters, as shown in the following formula: in, The bulk modulus structural parameter follows a Beta probability density distribution; , These are the morphological parameters of the Beta distribution; For the Gamma function, t For integration variables; in, Shear modulus structural parameters, subject to KG Coupling parameters and Joint control.

[0061] Step S340: Input the upper and lower boundaries of the bulk modulus, the upper and lower boundaries of the shear modulus, the structural parameters of the bulk modulus, and the structural parameters of the shear modulus into the output modulus model to obtain the bulk modulus and shear modulus of the new sub-block. The output modulus model is shown below: in, , These are the bulk modulus and shear modulus of the new sub-block, respectively. , , , These are the upper and lower boundaries of the bulk modulus and the upper and lower boundaries of the shear modulus corresponding to two randomly selected sub-blocks, respectively. , These are the bulk modulus structural parameters and shear modulus structural parameters determined using the Beta probability density distribution parameters and the KG coupling parameters, respectively. Step S350: Using the bulk modulus and shear modulus of the new sub-block, construct a new equivalent sub-block with corresponding sub-block parameters as follows. Then, two randomly selected sub-blocks in the structure pool are replaced with new equivalent sub-blocks, and the structure pool is updated, where the absolute volume fraction of the new sub-blocks is... ; In this step, two randomly selected sub-blocks in the structure pool are replaced with new equivalent sub-blocks. That is, the two randomly selected sub-blocks are deleted from the current structure pool, and after adding the new equivalent sub-blocks, the structure pool after the two-phase merging recursive iteration update for this round is obtained. ,Right now: The above-mentioned updated structure pool Compared to the structure pool used in the previous round of two-phase merging recursive iterations, the total number of sub-blocks has decreased by 1, that is... .

[0062] Step S360: Determine whether the total number of sub-blocks in the updated structure pool is equal to 1; If the response is negative in step S370, return to step S310 and proceed to the next round of two-phase merging recursive iteration. In step S380, the response is as follows: the sub-block parameters of the unique sub-block in the current structure pool are output to determine the equivalent modulus of the sample obtained in this sampling. This step outputs the sub-block parameters of the unique sub-block in the current structure pool. After obtaining the equivalent bulk modulus and equivalent shear modulus of the sample obtained in this sampling, other rock physical parameters can be calculated based on the equivalent bulk modulus and equivalent shear modulus. These other rock physical parameters include Poisson's ratio, Young's modulus, etc.

[0063] This embodiment can set the equivalent modulus, including the equivalent bulk modulus. Equivalent shear modulus Equivalent Poisson ratio Equivalent Young's Model The corresponding calculation formula is shown below: In this embodiment, the structure pool undergoes multiple rounds of two-phase merging recursive iterations. In each round of the two-phase merging recursive iterations, bulk modulus structural parameters that follow a Beta probability density distribution are used. Randomly select values ​​for bulk modulus By changing the pre-input of the Beta distribution morphological parameters, the structural parameters are affected, thus altering the distribution of the bulk modulus values; [the method employed is described in the original text, but the provided text is incomplete and requires further context.] KG Coupling parameters control shear modulus structural parameters Random values ​​are selected to achieve two-phase mixing. KG The value of the coupling shear modulus is ultimately determined to achieve the desired effect in two-phase mixing. KGControl of the coupling modulus distribution. Thus, this embodiment achieves a tunable structural bias parameterization mechanism without introducing specific microstructure assumptions.

[0064] Example 4: As shown in the appendix Figure 4 As shown in the figure, this invention discloses a method for calculating the equivalent framework modulus of multiphase minerals based on random structure clouds, including: Step S410: Obtain the relevant parameters of the random structure cloud and the phase parameters of each phase of the multiphase mineral. The relevant parameters of the random structure cloud include the total number of random structure cloud samples, the number of single-phase blocks, the Dirichlet distribution parameters, and the Beta distribution morphology parameters. KG Coupling parameters, including phase parameters such as bulk modulus, shear modulus, and volume fraction.

[0065] In this step, the phase parameters of each phase of the multiphase mineral are: N represents the total number of phases, and the total number of samplings for the random structure cloud. (positive integer), number of single-phase blocks (Positive integers), Dirichlet distribution parameters (Positive real numbers), morphological parameters of the Beta distribution and (positive real number) KG Coupling parameters (Positive real number). If the total volume fraction is not equal to one, normalize the volume fraction to ensure that the HSW boundary can be correctly calculated in each iteration. The normalization is as follows: Step S420: Determine whether the current number of samples is less than the total number of samples in the random structure cloud.

[0066] In step S430, the response is as follows: for the currently sampled sample, a probability density function is constructed based on the number of single-phase blocks and the Dirichlet distribution parameters. Based on the probability density function, all phases of the multiphase mineral are randomly divided into blocks within each phase. The sub-block parameters of each sub-block are determined to form an initial structure pool, wherein the sub-block parameters include bulk modulus, shear modulus and absolute volume fraction.

[0067] Step S440: Perform multiple rounds of two-phase merging recursive iterations on the structure pool until the total number of sub-blocks in the structure pool equals 1. Determine the equivalent modulus of the currently sampled sample using the sub-block parameters of the unique sub-block. Increment the sampling count by 1 and return to step S420. The two-phase merging recursive iteration includes randomly selecting two sub-blocks of different phases in the structure pool, combining the output modulus model and the corresponding sub-block parameters to construct a new equivalent sub-block, and replacing the two randomly selected sub-blocks in the structure pool with the new equivalent sub-block. The output modulus model is shown below: in, , These are the bulk modulus and shear modulus of the new sub-block, respectively. , , , These are the upper and lower boundaries of the bulk modulus and the upper and lower boundaries of the shear modulus corresponding to two randomly selected sub-blocks, respectively. , These are the bulk modulus structural parameters and shear modulus structural parameters determined using the Beta probability density distribution parameter and the KG coupling parameter, respectively.

[0068] In step S450, if the response is no, the equivalent moduli of the samples obtained from each sampling are combined to form a random structure cloud; in, It is a cloud with a random structure; This represents the total number of samples taken from the random structure cloud. Let be the equivalent modulus of the sample obtained from the Sth sampling.

[0069] Step S460: Take the mean of the equivalent modulus of the random structure cloud as the equivalent framework modulus of the multiphase mineral, and determine the effective boundary of each equivalent modulus. in, , , , These are the average equivalent bulk modulus, average equivalent shear modulus, average equivalent Poisson's ratio, and average equivalent Young's modulus, respectively, forming the equivalent framework modulus of multiphase minerals.

[0070] The effective boundaries of each of the above equivalent moduli are characterized using the maximum / minimum values ​​of the equivalent moduli. For example, the effective boundary for the equivalent bulk modulus is shown below: It should also be noted that due to the randomness of the sub-block construction and equivalent modulus calculation after each sampling, the final result pool is... or A point cloud is formed on a plane, which is named "random structure cloud" in this embodiment of the invention. When sufficiently large, this random structure cloud can be used to approximate the set of realizable equivalent moduli. The statistical coverage includes: in, A support is a closed, continuous region formed by all result points in the result pool.

[0071] The method for calculating the equivalent framework modulus of multiphase minerals based on random structure clouds disclosed in this embodiment no longer directly calculates the equivalent modulus set. Instead, given only the phase parameters of each phase in a multiphase mineral, a set of realizable equivalent skeleton moduli is constructed using random structure cloud computing. The statistical characteristics of this set are then used to constrain the realizable boundaries of the mixed mineral skeleton moduli and calculate the equivalent mineral skeleton moduli. Since the equivalent moduli of each phase in a multiphase mineral are not single, definite values, but rather correspond to a set of realizable moduli corresponding to a class of realizable microstructures, a set of realizable equivalent skeleton moduli is constructed using random structure cloud computing. That is, a set of realizable equivalent skeleton moduli that satisfies all phase parameters is constructed. microstructure collection Furthermore, by inducing an equivalent framework modulus using this microstructure assembly, the set can be realized. ,Right now: Furthermore, through the study of Statistical sampling / characterization can increase the boundary constraints and acquisition of the realizable domain without introducing explicit geometric modeling.

[0072] Example 5: This example illustrates the effectiveness of the invention and the control of key parameters in a theoretical quartz-calcite two-phase medium mixing scenario. Specifically: Taking a mixture of 50% quartz and 50% calcite by volume as an example, the controlled variable method was used to calculate and compare the effects of the invention and parameter control. The bulk modulus and shear modulus of the quartz mineral used were set to 37.0 GPa and 44.0 GPa, respectively, based on the relevant research of Carmichael (1989); the bulk modulus and shear modulus of the calcite used were set to 76.8 GPa and 32.0 GPa, respectively, based on the relevant research of Simmons (1965). Since the quartz-calcite two-phase mixture is a non-isotropic case, the total number of random structure cloud samplings was set in the calculation. It is 20,000.

[0073] Theoretically, based on the setting formula of this invention, KG Coupling parameters The impact on the final structured cloud is that... The larger, KG The stronger the coupling relationship, the more likely the random structure cloud corresponds to the same arrangement structure, which manifests in... or The closer a relationship is to a fixed curve or straight line within a plane; The smaller the value, the worse the volume-shear modulus coupling in each equivalent calculation, and the more discrete the structure cloud becomes.

[0074] In this embodiment, the Beta distribution morphology parameter is set. Single-phase block number Dirichlet distribution parameters The calculations and comparisons of different KG Coupling parameters The impact of (values ​​of 0.1, 1, and 10) on the final random structure cloud distribution. Results are in low... It exhibits a highly discrete equivalent modulus distribution at times, but near... Or The extreme points exhibit clear impossibility; at high... At times, it manifests as a highly concentrated, random structure cloud. KG The relationship is approximately linear (see appendix). Figure 5 The implementation results are highly consistent with the theory.

[0075] Similarly, as examples, the differences in the response of the present invention to different Dirichlet distribution parameters, Beta distribution morphological parameters, and the number of single-phase blocks are demonstrated (see Appendix). Figures 6 to 8 Theoretically, larger Dirichlet distribution parameters, larger Beta distribution morphological parameters, or more single-phase blocks all lead to a reduction in the boundary of the realizable set (larger Dirichlet parameters result in more uniform sub-blocks, more single-phase sub-blocks lead to a stronger averaging effect, while larger Beta distribution morphological parameters result in a smaller boundary of randomly selected sub-blocks). (The closer it is to 0.5). The results of the examples show that they are in perfect agreement with the theory.

[0076] The above examples of quartz-calcite two-phase implementations comprehensively verify the physical effectiveness of this invention for calculating the equivalent modulus set. In practical applications, key parameters can be adjusted using prior information (e.g., the number of single-phase blocks, particle size dispersion, and relative arrangement obtained from microscopic SEM / rock sample CT) to constrain the construction and boundary acquisition of the equivalent modulus set.

[0077] Example 6: This example demonstrates the engineering application of the present invention in conjunction with XRD, showcasing the applicability of the invention. Specifically: In this embodiment, the volume fraction results obtained from X-ray diffraction (XRD) of an X sample from a certain well were compared with a reference mineral modulus database to calculate the random structure cloud and its statistical estimation results, and then compared with the traditional HSW boundary.

[0078] The mineral composition, content, and reference modulus of the rock fragments from sample X are shown in Table 1. Table 1. Mineral composition, content, and reference modulus obtained from XRD analysis of rock cuttings from the X sample. .

[0079] During implementation, amorphous materials are removed, and it is assumed that all remaining minerals constitute the stratigraphic framework. The normalized volume fraction of each remaining mineral is calculated; and then... The parameters are subjected to randomized cloud computing and statistics to ensure sufficient sampling while avoiding... The strong coupling effect. The calculated random structure cloud is shown in the attached figure. Figure 8 The corresponding mean equivalent bulk modulus Mean equivalent shear modulus Mean of equivalent Young's modulus Equivalent Poisson's ratio mean The statistical 99.73% confidence intervals (corresponding to 3 standard deviations) of each equivalent modulus and their reduction compared to the original HSW boundary are as follows: equivalent bulk modulus [49.50, 49.77] (81.15%), equivalent shear modulus [25.78, 26.11] (65.51%), equivalent Young's modulus [65.93, 66.65] (69.03%), and equivalent Poisson's ratio [0.2762, 0.2785] (80.60%). It can be seen that the effective boundary of the equivalent modulus is significantly reduced compared to the original HSW; and even when avoiding strong coupling, the distribution of "high K + low G" or "low K + high G" after random mineral fragmentation and combined averaging is still significantly lower, which is consistent with many conclusions of previous studies.

[0080] Furthermore, the examples demonstrate the recalculated equivalent framework modulus structure distribution after removing clay minerals (typically with low modulus, see the gray area in Table 1) that are not generally considered part of the matrix framework. The mineral moduli exhibit a significantly biased distribution compared to the HSW boundary, with their center points deviating considerably from the center points calculated from the HSW boundary. The 99.73% confidence intervals for the equivalent bulk modulus (the degree of reduction compared to the HSW boundary) are as follows: equivalent bulk modulus [53.25, 53.74] (71.22%), equivalent shear modulus [31.33, 31.64] (55.05%), equivalent Young's modulus [78.85, 79.32] (63.48%), and equivalent Poisson's ratio [0.2529, 0.2552] (80.00%). This indicates that the overall feasibility of the lower boundary portion of the traditional HSW is poor.

[0081] In summary, this invention is applicable to the rapid assessment of formation elasticity and mechanical parameters, particularly in oil and gas exploration, drilling, and underground engineering scenarios involving real-time / drilling analysis and multi-source information integration. By combining techniques such as X-ray diffraction (XRD), it rapidly estimates the equivalent modulus of the formation matrix based on real-time mineral composition data, avoiding the shortcomings of traditional single-modulus value algorithms or algorithms with excessively wide equivalent modulus boundaries. Furthermore, the achievable equivalent modulus distribution calculated by this invention provides effective constraints for subsequent formation strength assessment, wellbore stability analysis, and pore structure deduction. Especially in dynamically changing geological environments, the calculation results of this invention can provide more diverse and controllable predictions, effectively avoiding the risks associated with simple estimations.

[0082] Furthermore, the calculation results of this invention can be used as a pre-input for various rock physics models (such as the Differential Effective Medium (DEM) model, the Dvorkin-Nur cementation model, etc.) for fine inversion of fluid-structure interaction porosity elasticity based on porosity and cementation degree. By using the output results of this invention as prior constraints, inversion errors caused by unreasonable modulus assumptions can be effectively reduced, enhancing the reliability of model predictions.

[0083] Example 7: As attached Figure 10 As shown, this embodiment of the invention discloses a device for calculating the equivalent framework modulus of multiphase minerals based on random structure clouds, comprising: The sampling count determination unit determines whether the current sampling count is less than the total sampling count of the random structure cloud. If the current sampling count is less than the total sampling count of the random structure cloud, then for the currently sampled sample, a probability density function is constructed based on the number of single-phase blocks and the Dirichlet distribution parameters. Based on the probability density function, all phases of the multiphase mineral are randomly divided into blocks within each phase, and the sub-block parameters of each sub-block are determined to form the initial structure pool. The sub-block parameters include bulk modulus, shear modulus, and absolute volume fraction. The sample equivalent modulus determination unit performs multiple rounds of two-phase merging recursive iterations on the structure pool until the total number of sub-blocks in the structure pool equals 1. The equivalent modulus of the currently sampled sample is determined using the sub-block parameters of this unique sub-block. The sampling count is incremented by 1, and the process returns to determine the sampling count. The two-phase merging recursive iterations involve randomly selecting two sub-blocks of different phases from the structure pool, combining the output modulus model and the corresponding sub-block parameters to construct a new equivalent sub-block, and then replacing the two randomly selected sub-blocks in the structure pool with the new equivalent sub-block. The output modulus model is shown below: in, , These are the bulk modulus and shear modulus of the new sub-block, respectively. , , , These are the upper and lower boundaries of the bulk modulus and the upper and lower boundaries of the shear modulus corresponding to two randomly selected sub-blocks, respectively. , Using the Beta probability density distribution parameters and KG The bulk modulus structural parameters and shear modulus structural parameters are determined by the coupling parameters.

[0084] Example 8: As attached Figure 11 As shown, this embodiment of the invention discloses a device for calculating the equivalent framework modulus of multiphase minerals based on random structure clouds, comprising: The basic data acquisition unit acquires parameters related to random structure clouds and phase parameters of each phase of multiphase minerals. The parameters related to random structure clouds include the total number of random structure cloud samplings, the number of single-phase blocks, Dirichlet distribution parameters, Beta distribution morphological parameters, and KG coupling parameters. The phase parameters include bulk modulus, shear modulus, and volume fraction. The sampling count determination unit determines whether the current sampling count is less than the total sampling count of the random structure cloud. If the current sampling count is less than the total sampling count of the random structure cloud, then for the currently sampled sample, a probability density function is constructed based on the number of single-phase blocks and the Dirichlet distribution parameters. Based on the probability density function, all phases of the multiphase mineral are randomly divided into blocks within each phase, and the sub-block parameters of each sub-block are determined to form the initial structure pool. The sub-block parameters include bulk modulus, shear modulus, and absolute volume fraction. The sample equivalent modulus determination unit performs multiple rounds of two-phase merging recursive iterations on the structure pool until the total number of sub-blocks in the structure pool equals 1. The equivalent modulus of the currently sampled sample is determined using the sub-block parameters of this unique sub-block. The sampling count is incremented by 1, and the process returns to determine the sampling count. The two-phase merging recursive iterations involve randomly selecting two sub-blocks of different phases from the structure pool, combining the output modulus model and the corresponding sub-block parameters to construct a new equivalent sub-block, and then replacing the two randomly selected sub-blocks in the structure pool with the new equivalent sub-block. The output modulus model is shown below: in, , These are the bulk modulus and shear modulus of the new sub-block, respectively. , , , These are the upper and lower boundaries of the bulk modulus and the upper and lower boundaries of the shear modulus corresponding to two randomly selected sub-blocks, respectively. , Using the Beta probability density distribution parameters and KG The bulk modulus structural parameters and shear modulus structural parameters are determined by the coupling parameters; The equivalent framework modulus determination unit determines the equivalent framework modulus of multiphase minerals when the current sampling count is not less than the total sampling count of the random structure cloud, including: The equivalent moduli of the samples obtained from each sampling are combined to form a random structure cloud; in, It is a cloud with a random structure; This represents the total number of samples taken from the random structure cloud. Let be the equivalent modulus of the sample obtained from the Sth sampling. The mean equivalent modulus of random structured clouds is used as the equivalent framework modulus of multiphase minerals, and the effective boundary of each equivalent modulus is determined. in, , , , These are the average equivalent bulk modulus, average equivalent shear modulus, average equivalent Poisson's ratio, and average equivalent Young's modulus, respectively, forming the equivalent framework modulus of multiphase minerals.

[0085] The execution steps of each unit in the above embodiments are the same as those in the method embodiments, and will not be repeated here.

[0086] The above content is only a specific embodiment of the present invention, which has strong adaptability and implementation effect. However, the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be covered within the protection scope of the present invention. Therefore, equivalent changes made in accordance with the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for calculating the equivalent framework modulus of multiphase minerals based on random structure clouds, comprising: Determine if the current sampling count is less than the total sampling count of the random structure cloud; Therefore, for the currently sampled sample, a probability density function is constructed based on the number of single-phase blocks and the Dirichlet distribution parameters. Based on the probability density function, all phases of the multiphase mineral are randomly divided into blocks within each phase. The sub-block parameters of each sub-block are determined to form an initial structure pool, where the sub-block parameters include bulk modulus, shear modulus and absolute volume fraction. The structure pool undergoes multiple rounds of recursive iteration involving two-phase merging until the total number of sub-blocks in the structure pool equals 1. The equivalent modulus of the currently sampled sample is determined using the parameters of the unique sub-block. The sampling count is incremented by 1, and the process returns to determine the sampling count. The recursive iteration involving two-phase merging involves randomly selecting two sub-blocks from different phases in the structure pool, combining the output modulus model and the corresponding sub-block parameters to construct a new equivalent sub-block, and then replacing the two randomly selected sub-blocks in the structure pool with the new equivalent sub-block. The output modulus model is shown below: in, , These are the bulk modulus and shear modulus of the new sub-block, respectively. , , , These are the upper and lower boundaries of the bulk modulus and the upper and lower boundaries of the shear modulus corresponding to two randomly selected sub-blocks, respectively. , Using the Beta probability density distribution parameters and KG The bulk modulus structural parameters and shear modulus structural parameters are determined by the coupling parameters.

2. The method for calculating the equivalent framework modulus of multiphase minerals based on random structure clouds according to claim 1, characterized in that, If the current sampling count is less than the total sampling count of the random structure cloud, then for the currently sampled sample, a probability density function is constructed based on the number of single-phase blocks and the Dirichlet distribution parameters. Based on the probability density function, all phases of the multiphase mineral are randomly divided into blocks within each phase, and the sub-block parameters of each sub-block are determined to form an initial structure pool, including: Construct a probability density function based on the number of single-phase blocks and the Dirichlet distribution parameters; Based on the probability density function, the first i The phase is randomly divided into blocks within the phase. Combined with the volume fraction of the phase, the absolute volume fraction of each sub-block in the phase is obtained, and then the sub-block parameters of each sub-block are determined. The sub-block parameters include bulk modulus. K i shear modulus G i and absolute volume fraction ; in, For the first i Xiangdi j The absolute volume fraction of each sub-block; For the first i Xiangdi j 3D random variable; For the first i Phase volume fraction; This represents the number of single-phase blocks. Repeat the above steps to randomly divide all phases of the multiphase mineral into blocks, determine the sub-block parameters of each sub-block, and form the initial structure pool.

3. The method for calculating the equivalent framework modulus of multiphase minerals based on random structure clouds according to claim 1 or 2, characterized in that, The structure pool is recursively iterated through multiple rounds of two-phase merging until the total number of sub-blocks in the structure pool equals 1. The equivalent modulus of the currently sampled sample is determined by the sub-block parameters of the unique sub-block. The sampling count is incremented by 1, and the process returns to determine the sampling count, including: Two sub-blocks of different phases are randomly selected from the structure pool, which are: in, The parameters for a sub-block are, in order, bulk modulus, shear modulus, and absolute volume fraction; For the sub-block parameters of another sub-block; Substituting the sub-block parameters of the two sub-blocks into the boundary calculation model, we obtain the upper and lower boundaries of the bulk modulus and the upper and lower boundaries of the shear modulus. The boundary calculation model is established using the equivalent boundary model. Input the upper and lower boundaries of the bulk modulus, the upper and lower boundaries of the shear modulus, the structural parameters of the bulk modulus, and the structural parameters of the shear modulus into the output modulus model to obtain the bulk modulus and shear modulus of the new sub-block. The output modulus model is shown below: in, , These are the bulk modulus and shear modulus of the new sub-block, respectively. , , , These are the upper and lower boundaries of the bulk modulus and the upper and lower boundaries of the shear modulus corresponding to two randomly selected sub-blocks, respectively. , These are the bulk modulus structural parameters and shear modulus structural parameters determined using the Beta probability density distribution parameters and the KG coupling parameters, respectively. Using the bulk modulus and shear modulus of the new sub-block, a new equivalent sub-block is constructed, with corresponding sub-block parameters as follows: Then, two randomly selected sub-blocks in the structure pool are replaced with new equivalent sub-blocks, and the structure pool is updated, where the absolute volume fraction of the new sub-blocks is... ; Determine if the total number of sub-blocks in the updated structure pool is equal to 1; If the response is negative, return to the next round of two-phase merging recursive iteration; The response then outputs the sub-block parameters of the unique sub-block in the current structure pool to determine the equivalent modulus of the sample obtained in this sampling.

4. The method for calculating the equivalent framework modulus of multiphase minerals based on random structure clouds according to claim 3, characterized in that, The acquisition of structural parameters of bulk modulus and shear modulus includes: Using Beta probability density distribution parameters and KG The coupling parameters determine the bulk modulus structural parameters and the shear modulus structural parameters, as shown in the following formula: in, The bulk modulus structural parameter follows a Beta probability density distribution; , These are the morphological parameters of the Beta distribution; For the Gamma function, t For integration variables; in, Shear modulus structural parameters, subject to KG Coupling parameters and Joint control.

5. The method for calculating the equivalent framework modulus of multiphase minerals based on random structure clouds according to claim 3 or 4, characterized in that, The equivalent boundary model is shown below: in, include: in, , , , The upper and lower boundaries of the bulk modulus and shear modulus are respectively based on the two randomly selected sub-blocks; + is the upper boundary and - is the lower boundary; and These are the maximum and minimum values, respectively. This is the singularity calibration value; in, , .

6. The method for calculating the equivalent framework modulus of multiphase minerals based on random structure clouds according to claim 3 or 4, characterized in that, Output the sub-block parameters of the unique sub-block in the current structure pool. The formula for calculating the equivalent modulus of the sample obtained in this sampling is as follows: in, Equivalent bulk modulus; Equivalent shear modulus; It is the equivalent Poisson's ratio; For equivalent Young's model; 、 These are the bulk modulus and shear modulus of the unique sub-block in the current structure pool, respectively.

7. The method for calculating the equivalent framework modulus of multiphase minerals based on random structure clouds according to claim 1, characterized in that, This also includes determining the equivalent framework modulus of multiphase minerals in response to the current sampling number being no less than the total sampling number of the random structure cloud, including: The equivalent moduli of the samples obtained from each sampling are combined to form a random structure cloud; in, It is a cloud with a random structure; This represents the total number of samples taken from the random structure cloud. Let be the equivalent modulus of the sample obtained from the Sth sampling. The mean equivalent modulus of random structured clouds is used as the equivalent framework modulus of multiphase minerals, and the effective boundary of each equivalent modulus is determined. in, , , , These are the average equivalent bulk modulus, average equivalent shear modulus, average equivalent Poisson's ratio, and average equivalent Young's modulus, respectively, forming the equivalent framework modulus of multiphase minerals.

8. The method for calculating the equivalent framework modulus of multiphase minerals based on random structure clouds according to claim 1 or 7, characterized in that, This also includes acquiring parameters related to random structure clouds and phase parameters of each phase of multiphase minerals. The parameters related to random structure clouds include the total number of random structure cloud samples, the number of single-phase blocks, Dirichlet distribution parameters, and Beta distribution morphological parameters. KG Coupling parameters, including phase parameters such as bulk modulus, shear modulus, and volume fraction.

9. A device for calculating the equivalent framework modulus of multiphase minerals based on random structure clouds using the method described in any one of claims 1 to 8, comprising: The sampling count determination unit determines whether the current sampling count is less than the total sampling count of the random structure cloud. If the current sampling count is less than the total sampling count of the random structure cloud, then for the currently sampled sample, a probability density function is constructed based on the number of single-phase blocks and the Dirichlet distribution parameters. Based on the probability density function, all phases of the multiphase mineral are randomly divided into blocks within each phase, and the sub-block parameters of each sub-block are determined to form the initial structure pool. The sub-block parameters include bulk modulus, shear modulus, and absolute volume fraction. The sample equivalent modulus determination unit performs multiple rounds of two-phase merging recursive iterations on the structure pool until the total number of sub-blocks in the structure pool equals 1. The equivalent modulus of the currently sampled sample is determined using the sub-block parameters of this unique sub-block. The sampling count is incremented by 1, and the process returns to determine the sampling count. The two-phase merging recursive iterations involve randomly selecting two sub-blocks of different phases from the structure pool, combining the output modulus model and the corresponding sub-block parameters to construct a new equivalent sub-block, and then replacing the two randomly selected sub-blocks in the structure pool with the new equivalent sub-block. The output modulus model is shown below: in, , These are the bulk modulus and shear modulus of the new sub-block, respectively. , , , These are the upper and lower boundaries of the bulk modulus and the upper and lower boundaries of the shear modulus corresponding to two randomly selected sub-blocks, respectively. , Using the Beta probability density distribution parameters and KG The bulk modulus structural parameters and shear modulus structural parameters are determined by the coupling parameters.

10. The multiphase mineral equivalent framework modulus calculation device based on random structure clouds according to claim 9, characterized in that, It also includes an equivalent skeleton modulus determination unit and a basic data acquisition unit; The equivalent framework modulus determination unit determines the equivalent framework modulus of multiphase minerals when the current sampling count is not less than the total sampling count of the random structure cloud, including: The equivalent moduli of the samples obtained from each sampling are combined to form a random structure cloud; in, It is a cloud with a random structure; This represents the total number of samples taken from the random structure cloud. Let be the equivalent modulus of the sample obtained from the Sth sampling. The mean equivalent modulus of random structured clouds is used as the equivalent framework modulus of multiphase minerals, and the effective boundary of each equivalent modulus is determined. in, , , , These are the average equivalent bulk modulus, average equivalent shear modulus, average equivalent Poisson's ratio, and average equivalent Young's modulus, respectively, forming the equivalent framework modulus of multiphase minerals; The basic data acquisition unit acquires parameters related to random structure clouds and phase parameters of each phase of multiphase minerals. The parameters related to random structure clouds include the total number of random structure cloud samples, the number of single-phase blocks, Dirichlet distribution parameters, and Beta distribution morphological parameters. KG Coupling parameters, including phase parameters such as bulk modulus, shear modulus, and volume fraction.

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