Anisotropic parameter calculation method, device and storage medium

Abstract

The embodiments of this specification provide a method, device and storage medium for calculating anisotropic parameters. The method includes: calculating the predicted velocity value corresponding to each candidate geological parameter through a pre-constructed rock physics model; if the comparison result between the predicted velocity value and the measured velocity value meets the error judgment condition, retaining the corresponding candidate geological parameter; using the rock physics model to perform inversion to obtain the inversion parameter for the retained candidate geological parameter; lowering the ambient temperature and repeating the above operations of calculating the predicted velocity value, judging the error judgment condition, and inversion until the ambient temperature drops to the preset minimum temperature; and calculating the anisotropic parameter by combining the inversion parameters at different ambient temperatures. The above method stably and efficiently predicts the content of different types of pores, fracture aspect ratio and elastic anisotropy in the target area, thereby providing instructions and references for subsequent oil and gas storage capacity assessment, accurate velocity modeling, and analysis of geological sweet spots and engineering sweet spots.

Description

Technical Field

[0001] The embodiments of this specification relate to the field of geological exploration and development technology, and in particular to a method, device, and storage medium for calculating anisotropic parameters. Background Art

[0002] In recent years, interest in shale oil and gas exploration and development has continued to grow. Compared to conventional oil and gas reservoirs, shale reservoirs exhibit greater heterogeneity, complex mineralogy, diverse pore types, and strong elastic anisotropy. Accurately determining the anisotropy parameters of the corresponding rock formations provides a basis for precise seismic imaging, well logging interpretation, and quantitative seismic prediction.

[0003] Although rock physics experiments, modeling, and inversion research for shale reservoirs have become increasingly in-depth, practical applications still primarily rely on isotropic models and theories. Current theories for anisotropic inversion are generally complex and involve numerous modeling parameters. This makes it difficult to guarantee inversion speed and convergence in practical applications, significantly limiting the feasibility of anisotropic inversion. For example, traditional simulated annealing, when evaluating models using the Metropolis criterion, often results in extremely high rejection probabilities at low temperatures. While heat bath algorithms can avoid extremely high model rejection probabilities, their computational cost is prohibitive for practical application. Therefore, a stable and efficient method for analyzing reservoir elastic anisotropy is urgently needed. Summary of the Invention

[0004] The purpose of the embodiments of this specification is to provide a method, device and storage medium for calculating anisotropy parameters to solve the problem of how to stably and efficiently analyze the elastic anisotropy of a reservoir.

[0005] In order to solve the above technical problems, an embodiment of this specification proposes a method for calculating anisotropic parameters, including: calculating a predicted velocity value corresponding to each candidate geological parameter through a pre-constructed rock physics model; retaining the corresponding candidate geological parameter if the comparison result between the predicted velocity value and the measured velocity value meets the error judgment condition; performing inversion on the retained candidate geological parameter using the rock physics model to obtain an inversion parameter; lowering the ambient temperature and repeating the above operations of calculating the predicted velocity value, judging the error judgment condition, and inversion until the ambient temperature drops to a preset minimum temperature; and calculating the anisotropic parameter by integrating the inversion parameters under different ambient temperatures.

[0006] The embodiments of this specification also provide an anisotropic parameter calculation device, comprising: a predicted velocity value calculation module, configured to calculate predicted velocity values corresponding to respective candidate geological parameters using a pre-constructed rock physics model; a candidate geological parameter retention module, configured to retain the corresponding candidate geological parameters when a comparison result between the predicted velocity values and the measured velocity values meets an error judgment condition; an inversion module, configured to perform inversion on the retained candidate geological parameters using the rock physics model to obtain inversion parameters; an ambient temperature reduction module, configured to reduce the ambient temperature and repeat the above-mentioned operations of calculating predicted velocity values, judging error judgment conditions, and inversion until the ambient temperature is reduced to a preset minimum temperature; and an anisotropic parameter calculation module, configured to calculate anisotropic parameters by integrating the inversion parameters at different ambient temperatures.

[0007] The embodiments of this specification further provide a computer-readable storage medium having a computer program / instruction stored thereon. When the computer program / instruction is executed, it is used to implement the steps of the above-mentioned anisotropic parameter calculation method.

[0008] As can be seen from the technical solutions provided by the above embodiments of this specification, the embodiments of this specification respectively calculate the predicted velocity values of each candidate geological parameter for a pre-constructed rock physics model, and screen the candidate geological parameters by the calculated predicted velocity values. For the parameters obtained by screening, the rock physics model is used for inversion to obtain the inversion parameters. Thereafter, the temperature is lowered in sequence and the above operations are repeated until the lowest temperature is reached, thereby obtaining the inversion parameters at various ambient temperatures. Combining these inversion parameters, the corresponding anisotropy parameters are obtained after forward calculation using the rock physics model. The above method can stably and efficiently predict the content of different types of pores, fracture aspect ratios and elastic anisotropy in the target area, thereby providing instructions and references for subsequent oil and gas storage capacity assessment, accurate velocity modeling, and analysis of geological sweet spots and engineering sweet spots. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0010] Figure 1 This is a flow chart of a method for calculating anisotropy parameters according to an embodiment of this specification;

[0011] Figure 2A This is a schematic diagram of an outer tube according to an embodiment of the present specification;

[0012] Figure 2B This is a schematic diagram of an inner tube according to an embodiment of the present specification;

[0013] Figure 3 This is a schematic diagram of an experimental result of an embodiment of this specification;

[0014] Figure 4 This is a module diagram of an anisotropic parameter calculation device according to an embodiment of this specification. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of this specification to clearly and completely describe the technical solutions in the embodiments of this specification. Obviously, the embodiments described are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this specification.

[0016] In order to solve the above technical problems, the present invention proposes a method for calculating anisotropic parameters. The execution subject of the anisotropic parameter calculation method can be an electronic computing device, which includes but is not limited to a server, an industrial computer, a PC, etc. Figure 1 As shown, the anisotropy parameter calculation method may include the following specific implementation steps.

[0017] S110: Calculate the predicted velocity value corresponding to each candidate geological parameter using the pre-built rock physics model.

[0018] A rock physics model is a model constructed to simulate formations with strong anisotropy. In some embodiments, the rock physics model can be constructed in two steps: anisotropic matrix and low-scale pore modeling, and anisotropic shale modeling with horizontal fractures.

[0019] In the process of modeling the anisotropic matrix and low-scale pores, the anisotropic SCA model is first used to mix quartz, feldspar, pyrite, clay and microscopic pores inside the matrix (oil-saturated pores) in the form of no main phase minerals to obtain the equivalent anisotropic (VTI) elastic stiffness matrix.

[0020] The fourth-order tensor of inclusions can be calculated based on the equivalent anisotropic elastic stiffness matrix. Since the final rock physics model assumes that there are multiple inclusion families in a homogeneous anisotropic medium, it is necessary to determine the parameters of the inclusions, that is, to obtain the fourth-order tensor of the inclusions.

[0021] Specifically, we can use the formula Calculate the SCA equivalent elastic stiffness tensor, where is the SCA equivalent elastic stiffness tensor, is the volume fraction of the n-th phase component, is the elastic stiffness matrix of the n-th phase component, is a fourth-order unit tensor, is the elastic stiffness matrix of the p-th phase component, N is the total number of phases of the component, is the fourth-order Eshelby tensor that controls the spatial morphology of the inclusion, and Specifically, in, α is the inclusion aspect ratio (0 to 1), d = c 11 , f=c 44 , g=c 13 +c 44 , h=c 33 , c 11 、c 12 、c 13 、c 33 、c 44 are the elastic stiffness tensor parameters under Voigt representation. In the above formula, since the equivalent elastic stiffness tensors are coupled with each other, the formula It can be solved by iteration.

[0022] After determining the fourth-order tensor of the inclusions, that is, completing the modeling of the anisotropic matrix and low-scale pores, an anisotropic shale model with horizontal fractures can be established. Based on the T-matrix theory, the spatial distribution of inclusions is clearly defined, so that the elastic interaction between inclusions can be considered. The model corresponding to the T-matrix theory assumes that in a homogeneous anisotropic medium (elastic stiffness tensor C (o) ) contains multiple inclusion families r = 1, 2, ..., N, each family of inclusions has exactly the same shape and uniform spatial distribution, and the volume fraction of the rth family of inclusions is v (r) , with an aspect ratio of α (r) Therefore, the equivalent stiffness of the medium can be expressed by the formula Calculate, where is the equivalent stiffness of the medium, C (0) is the elastic stiffness tensor, <t1>is the first-order scattering correction for the elastic field, Among them, v (r) is the volume fraction of the rth group inclusion, t (r) =ΔC (r) (IG (r) ΔC (r) ) -1 , ΔC (r) =C (r) -C (0) , C (r) is the fourth-order Eshelby tensor of the r-th group inclusion, I is the fourth-order unit tensor, G (r) is the fourth-order Eshelby tensor in a single family of inclusions, X is the second-order scattering correction, in, is the interaction between the rth group and the sth group inclusions. (r) and The calculation method of the two is the same as Consistent, but in calculation The inclusion aspect ratio α in the corresponding formula needs to be changed to the spatial distribution aspect ratio α of the inclusion family d , N is the number of inclusion families.

[0023] For the sake of simplicity, only the influence of a single family of fractures is considered at the current scale. At the same time, it is assumed that the fractures satisfy a uniform and symmetrical spatial distribution. By combining the fourth-order tensor of the inclusions and the equivalent stiffness of the medium, the construction of the anisotropic rock physics model can be completed.

[0024] Preferably, when the method in the embodiments of this specification is performed on a shale oil reservoir with significant anisotropy, the rock physics model is also constructed based on the characteristics of the shale oil reservoir, and the collected core samples are also obtained for the shale oil reservoir.

[0025] Candidate geological parameters are used for forward modeling of rock physics models. These can be simulated based on demand or determined based on actual scans of core samples. The types of candidate geological parameters can be pre-defined, such as fracture porosity, clay porosity, and fracture aspect ratio.

[0026] In some embodiments, the candidate geological parameters are selected from a certain inversion search range. Therefore, the inversion search range needs to be determined in advance.

[0027] To obtain candidate geological parameters, core samples can be scanned to obtain these parameters. These parameters can include fracture porosity, clay porosity, and fracture aspect ratio. Specifically, Otus segmentation can be performed on the CT and SEM scans of the core samples to obtain a binary image of the pores and matrix. This binary image can then be used as the geological parameter for analysis in subsequent steps.

[0028] The inversion search range can be determined based on the geological parameters obtained from the scans. Specifically, a watershed algorithm can be used to determine the pore aspect ratio spectrum corresponding to the CT scan results and the SEM scan results, and thereby determine the inversion search range for three geological parameters, such as fracture porosity, clay porosity, and fracture aspect ratio.

[0029] After determining the inversion search range, the corresponding candidate model parameters can be selected within the inversion search range. In one example, an initial value can be selected within the inversion search range, and then other candidate model parameters can be selected based on the initial value and the corresponding interval. Alternatively, the formula Determine the candidate model parameters, where m i are candidate model parameters, and Used to limit the inversion search range, u i =U[0,1].

[0030] After determining the rock physics model and candidate geological parameters, the rock physics model can be used to calculate the predicted velocity values, that is, forward modeling can be performed using the rock physics model. The predicted velocity values include the predicted P-wave velocity and the predicted S-wave velocity.

[0031] Specifically, the candidate geological parameters can be used as input together with the mineral content and porosity information interpreted by well logging and brought into the rock physics model for forward modeling to obtain the P-wave velocity and S-wave velocity perpendicular to the bedding direction.

[0032] S120: If the comparison result between the predicted velocity value and the measured velocity value meets the error judgment condition, the corresponding candidate geological parameter is retained.

[0033] After obtaining the predicted velocity value, it can be determined whether the corresponding candidate geological parameter can be retained according to the magnitude of the predicted velocity value, that is, the candidate geological parameters are screened using the predicted velocity value as a constraint condition.

[0034] In some embodiments, the error function between the measured speed value and the predicted speed value can be calculated to determine whether the new parameter needs to be retained. Specifically, the formula E = |Vp-Vp prediction | 2 +|Vs-vs predietion | 2 Calculate the error function value, where E is the error function value, Vp is the measured longitudinal wave velocity, and vp prediction is the predicted longitudinal wave velocity, vs is the measured shear wave velocity, Vs prediction To predict the shear wave velocity.

[0035] The error judgment condition is used to define the specific judgment criteria corresponding to the comparison result between the predicted velocity value and the measured velocity value. In some embodiments, after calculating the error function value, the next candidate geological parameter can be replaced and the error function value after the replacement candidate geological parameter can be calculated. The difference ΔE is calculated based on the error function values before and after the replacement, and the difference in the error function values is compared to determine whether the error judgment condition is met.

[0036] The error judgment condition may be: the difference ΔE of the error function value satisfies ΔE≤0, or ΔE>0 and P=exp(-ΔE / T)>u i , where T is the ambient temperature, u i The random update parameter u is used to determine the candidate model parameters. i =U[0,1].

[0037] The method of selecting the next candidate geological parameter in the above process can refer to the implementation in step S110 and will not be repeated here.

[0038] For all candidate geological parameters, the above method is used in turn to make judgments, and all candidate geological parameters that meet the error judgment conditions are retained for subsequent inversion operations.

[0039] S130: For the retained candidate geological parameters, inversion is performed using the rock physics model to obtain inversion parameters.

[0040] After the judgment process in step S120, parameters that meet the conditions are selected from all candidate geological parameters. Then, all retained candidate geological parameters can be used to perform inversion using the rock physics model to obtain inversion parameters.

[0041] In contrast to the forward modeling process, the inversion process primarily uses rock physics models to determine the rock physical properties of the formation based on P- and S-wave velocities. Specifically, inversion using rock physics models can obtain parameters such as fracture aspect ratio, fracture porosity, and clay porosity.

[0042] In addition, after obtaining inversion parameters for different candidate geological parameters, the average value of the inversion parameters can be obtained to perform corresponding analysis operations.

[0043] S140: Lowering the ambient temperature and repeating the above operations of calculating the predicted speed value, determining the error judgment condition, and inverting until the ambient temperature drops to a preset minimum temperature.

[0044] During the initial execution of steps S110-S130, the ambient temperature corresponding to the rock physics model can be set as the initial temperature. Subsequently, an annealing operation is performed to lower the ambient temperature, and the aforementioned steps of calculating the predicted velocity value, determining the error function value using the error judgment criteria, and inverting the retained candidate geological parameters to obtain the inversion parameters are repeated. After all steps are completed, the ambient temperature is lowered again and the corresponding operations are performed.

[0045] In some embodiments, the formula T=T o a n Lower the ambient temperature, where T o is the initial temperature, a is the cooling rate, n is the number of cooling rounds, and T is the ambient temperature after cooling.

[0046] The specific cooling range can also be set according to actual application requirements and there is no restriction on this.

[0047] Accordingly, a minimum temperature is preset. When the ambient temperature drops to the minimum temperature, the above cycle process can be terminated, and the candidate geological parameters and corresponding inversion parameters retained at each temperature can be obtained.

[0048] S150: Calculate anisotropic parameters by integrating the inversion parameters at different ambient temperatures.

[0049] After the ambient temperature is reduced to the minimum temperature, the inversion parameters at different ambient temperatures are obtained. The anisotropic parameters can be calculated by combining these inversion parameters.

[0050] In some embodiments, anisotropy parameters may be calculated by substituting the inverted parameters into a rock physics model and performing forward modeling to obtain the corresponding anisotropy parameters. Specifically, for example, the fracture aspect ratio, fracture porosity, and clay porosity obtained through inversion may be simultaneously substituted into the rock physics model and performed forward modeling to calculate elastic anisotropy.

[0051] The following example illustrates the application of the inversion method to the lower fourth section of Well B in the Qianjiang Sag. The specific implementation steps are as follows.

[0052] Step 1: Set the initial mineral composition of the model based on the well logging interpretation results. The well data in the study area are from the lower fourth section of Well B in Qianjiang Depression, Jianghan Basin. The mineral content interpreted by the well logging is as follows: Figure 2A As shown, the blank areas are salt layers. A series of salt and shale layers form a distinctive rhythmic formation. As non-reservoir layers, the salt layers have extremely low porosity and a relatively simple mineral composition, primarily composed of a mixture of salt rock and a small amount of clay. Well logging interpretation indicates that the average porosity of the inter-salt shale within the depth range of 4300-4550m is 8.09%. To establish a quantitative rock physics interpretation panel, the initial values of the model components are set based on the average mineral content: carbonate mineral content is set to 39.5%, silicate mineral content is set to 26%, glauberite content is set to 13%, clay content is set to 20%, and kerogen content is set to 1.5%.

[0053] Step 2: Verify the rock physics model based on well data. The intersection results of the P-wave velocity ratio and P-wave impedance obtained based on the well logging P-wave velocity, S-wave velocity, density curve and the corresponding rock physics plate are as follows: Figure 2B As shown, the triangular points represent well logging data points, and the color of the points represents the clay content interpreted from the logging. By setting appropriate ranges for clay and fracture content, the elastic response of the logging data can be roughly interpreted. The results show that the patterns verified by core testing are still consistent with the patterns shown by the well data. However, because the patterns shown here only represent the elastic response patterns under a certain pore space assumption, they cannot accurately describe all scattered well data points. Therefore, inversion studies based on rock physics models are still needed to quantitatively predict the anisotropy of the inter-salt shale in this well section.

[0054] Step 3: Determine the inversion search range for the target parameters. Before inverting the target parameters, it is necessary to set an appropriate search range and initial values. Based on the pore structure analysis of the core samples, we set the search range for the fracture aspect ratio to between 0 and 0.2, and the fracture and clay porosity to between 0 and 0.4. Initial parameter values are randomly generated within the search range and then used as input along with the mineral content and porosity information from the well logging interpretation. The inversion process from steps 4 to 7 is then executed.

[0055] Step 4: Metropolis-heat bath method inversion. For each logging point, the Metropolis-heat bath method is used for random inversion 100 times (execute the inversion process from step 4 to step 7), and the average value is taken as the inversion result. Figure 3 The inversion results of the lower fourth section of well B at different depths are shown, with section A taken from 4301-4315m and section B taken from 4405-4421m. Since the inversion process uses both longitudinal and transverse wave velocities as constraints, the calculated results of the model are highly consistent with the longitudinal and transverse wave velocities measured by well logging. The average relative errors of longitudinal and transverse waves are -1.32% and -2.39%, respectively, indicating that the inversion objective function has a strong constraint on the model. The inversion results show that the fracture aspect ratio in the study area is mainly distributed below 0.1. The average fracture aspect ratios of sections A and B are very close, at 0.048 and 0.044, respectively. For the three types of pore content, in the order of fractures, clay pores, and carbonate pores, the average values of section A are 26.87%, 26.64%, and 46.49%, and the average values of section B are 18.38%, 19.98%, and 61.64%. Overall, the carbonate pore P carbonate The content of clay pore P is the highest, while clay With crack P fracture The content is close to 1:1.

[0056] Step 5: Inversion results are incorporated into the rock physics model to predict rock anisotropy. The fracture aspect ratio, fracture porosity, and clay porosity obtained from the inversion are simultaneously incorporated into the rock physics model for forward modeling to calculate elastic anisotropy. Based on the anisotropic rock physics model proposed in this paper, the Thomsen anisotropy parameters for this well section can be predicted. The P-wave anisotropy parameter ε for Section A ranges from 0.389 to 0.272, with an average value of 0.342, and the S-wave anisotropy parameter γ ranges from 0.478 to 0.285, with an average value of 0.354. For Section B, the P-wave anisotropy parameter ε ranges from 0.404 to 0.222, with an average value of 0.289, and the S-wave anisotropy parameter γ ranges from 0.512 to 0.249, with an average value of 0.315. Combining the pore structure characteristics of sections A and B, although the aspect ratios of the fractures in the two sections are similar, the fracture and clay pore content in section A is significantly higher than that in section B. The increase in soft pore content significantly enhances the overall anisotropy of the rock. Therefore, the anisotropic responses of both longitudinal and transverse waves in section A are stronger than those in section B.

[0057] Based on the inversion method in the above steps, the fracture content, clay pore content, fracture aspect ratio and elastic anisotropy parameters of the fourth lower section of the subsurface were effectively obtained.

[0058] Through the introduction of the above embodiments and scenario examples, it can be seen that the above method calculates the predicted velocity value of each candidate geological parameter based on the pre-constructed rock physics model, and screens the candidate geological parameters based on the calculated predicted velocity values. For the screened parameters, the rock physics model is used to perform inversion to obtain the inversion parameters. The temperature is then lowered and the above operation is repeated until the lowest temperature is reached, thereby obtaining the inversion parameters at various ambient temperatures. By combining these inversion parameters, the corresponding anisotropy parameters are obtained after forward calculation using the rock physics model. The above method can stably and efficiently predict the content of different types of pores, fracture aspect ratios, and elastic anisotropy in the target area, thereby providing guidance and reference for subsequent oil and gas storage capacity assessment, accurate velocity modeling, and analysis of geological and engineering sweet spots.

[0059] Based on the above anisotropic parameter calculation method, the present embodiment also proposes an anisotropic parameter calculation device. Figure 4 As shown, the anisotropy parameter calculation device includes the following modules.

[0060] The predicted velocity value calculation module 410 is used to calculate the predicted velocity value corresponding to each candidate geological parameter using a pre-built rock physics model.

[0061] The candidate geological parameter retaining module 420 is configured to retain the corresponding candidate geological parameter when the comparison result between the predicted velocity value and the measured velocity value meets the error judgment condition.

[0062] The inversion module 430 is configured to perform inversion on the retained candidate geological parameters using the rock physics model to obtain inversion parameters.

[0063] The ambient temperature reducing module 440 is used to reduce the ambient temperature and repeat the above operations of calculating the predicted speed value, determining the error judgment condition, and inverting until the ambient temperature is reduced to a preset minimum temperature.

[0064] The anisotropic parameter calculation module 450 is used to calculate anisotropic parameters by integrating the inversion parameters under different ambient temperatures.

[0065] based on Figure 1 The present invention provides a computer-readable storage medium having a computer program / instructions stored thereon. The computer-readable storage medium can be read by a processor based on an internal bus of a device, and the processor can then execute the program instructions in the computer-readable storage medium.

[0066] In this embodiment, the computer-readable storage medium can be implemented in any appropriate manner. The computer-readable storage medium includes, but is not limited to, random access memory (RAM), read-only memory (ROM), cache, hard disk drive (HDD), memory card, etc. The computer storage medium stores computer program instructions. When the computer program instructions are executed, the contents of this specification are implemented. Figure 1 The program instructions or modules of the corresponding embodiment.

[0067] In this embodiment, the processor may be implemented in any suitable manner. For example, the processor may take the form of a microprocessor or a processor and a computer-readable medium storing computer-readable program code (such as software or firmware) that can be executed by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller, etc. Specifically, the processor may execute when it is set on the corresponding device. Figure 1 The method steps in the corresponding embodiments.

[0068] Although the process flows described above include multiple operations occurring in a particular order, it should be understood that these processes may include more or fewer operations, which may be performed sequentially or in parallel (eg, using parallel processors or a multi-threaded environment).

[0069] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0070] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0071] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0072] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0073] Embodiments of this specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. Embodiments of this specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In distributed computing environments, program modules may be located in local and remote computer storage media, including storage devices.

[0074] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between the various embodiments can be referenced across them. Each embodiment focuses on the differences from the other embodiments. In particular, since the system embodiments are generally similar to the method embodiments, their description is relatively simple; for relevant parts, reference can be made to the description of the method embodiments. Throughout this specification, reference to the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples" indicates that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. Throughout this specification, the illustrative use of these terms does not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, those skilled in the art may combine and integrate the different embodiments or examples, and features of different embodiments or examples, described in this specification, without conflict.

[0075] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A method for calculating anisotropic parameters, characterized in that: include: Calculate the predicted velocity values corresponding to each candidate geological parameter using a pre-built rock physics model; If the comparison result between the predicted velocity value and the measured velocity value meets the error judgment condition, the corresponding candidate geological parameter is retained; For the retained candidate geological parameters, inversion is performed using the rock physics model to obtain inversion parameters; Lower the ambient temperature and repeat the above operations of calculating the predicted speed value, determining the error judgment condition, and inverting until the ambient temperature drops to the preset minimum temperature; The anisotropic parameters are calculated by integrating the inversion parameters at different ambient temperatures.

2. The method according to claim 1, wherein Before calculating the predicted velocity value corresponding to each candidate geological parameter, the method further includes: Scanning a core sample to obtain geological parameters; the geological parameters include at least one of fracture porosity, clay porosity, and fracture aspect ratio; Determine the inversion search range based on the geological parameters obtained by scanning; The candidate geological parameters are determined within the inversion search range.

3. The method according to claim 2, wherein The scanning of the core sample to obtain geological parameters includes: Based on the CT scan results and SEM scan results of the core samples, Otus segmentation is performed to obtain binary images of pores and matrix; The inversion search range is determined based on the geological parameters obtained by scanning, including: determining a pore aspect ratio spectrum corresponding to the CT scan result and the SEM scan result by a watershed algorithm; Determining the candidate geological parameters in the inversion search range includes: Using the formula Determine the candidate geological parameters, where m i are candidate model parameters, and Used to limit the inversion search range, u i =U[0,1].

4. The method according to claim 1, wherein The rock physics model is constructed in the following way: The anisotropic SCA model is used to mix the internal microscopic pores to obtain the equivalent anisotropic elastic stiffness matrix; The fourth-order tensor of the inclusion is calculated based on the equivalent anisotropic elastic stiffness matrix; Obtain the equivalent stiffness corresponding to the medium; The rock physics model is constructed by integrating the fourth-order tensor of the inclusion and the equivalent stiffness of the medium.

5. The method according to claim 4, wherein The calculation of the fourth-order tensor of the inclusion based on the equivalent anisotropic elastic stiffness matrix includes: Using the formula Calculate the SCA equivalent elastic stiffness tensor, where is the SCA equivalent elastic stiffness tensor, v n is the volume fraction of the nth phase component, is the elastic stiffness matrix of the n-th phase component, is a fourth-order unit tensor, is the elastic stiffness matrix of the p-th phase component, N is the total number of phases of the component, is the fourth-order Eshelby tensor that controls the spatial morphology of the inclusion, and Specifically, in, is the aspect ratio of the inclusion, d = c 11 , f=c 44 , g=c 13 +c 44 , h=c 33 , c 11 、c 12 、c 13 、c 33 、c 44 are the elastic stiffness tensor parameters under Voigt representation; Obtaining the equivalent stiffness corresponding to the medium includes: Using the formula Calculate the equivalent stiffness of the medium, where is the equivalent stiffness of the medium, C (0) is the elastic stiffness tensor, <t1>is the first-order scattering correction for the elastic field, Among them, v (r) is the volume fraction of the rth group inclusion, t (r) =ΔC (r) (IG (r) ΔC (r) ) -1 , ΔC (r) =C (r) -C (0) , C (r) is the fourth-order Eshelby tensor of the r-th group inclusion, I is the fourth-order unit tensor, G (r) is the fourth-order Eshelby tensor in a single family of inclusions, X is the second-order scattering correction, in, is the interaction between the rth and sth group inclusions, and N is the number of inclusion groups.

6. The method according to claim 3, wherein If the comparison result between the predicted velocity value and the measured velocity value meets the error judgment condition, the corresponding candidate geological parameter is retained, including: Using the formula E=|Vp-Vp prediction | 2 +|Vs-Vs prediction | 2 Calculate the error function value, where E is the error function value, Vp is the measured longitudinal wave velocity, and Vp prediction is the predicted longitudinal wave velocity, Vs is the measured shear wave velocity, and Vs prediction To predict the shear wave velocity; The error judgment condition includes: the difference ΔE of the error function value satisfies ΔE≤0, or ΔE>0 and P=exp(-ΔE / T)>u i , where T is the ambient temperature; the difference in the error function value is obtained by calculating the difference in the error function value before and after updating the candidate geological parameters.

7. The method according to claim 1, wherein The lowering of the ambient temperature comprises: Based on the formula T=T0a n Lower the ambient temperature, where T0 is the initial temperature, a is the cooling rate, n is the number of cooling rounds, and T is the ambient temperature after the reduction.

8. The method according to claim 1, wherein The anisotropic parameters are calculated by integrating the inversion parameters at different ambient temperatures, including: The inversion parameters are substituted into the rock physics model to perform forward calculation of anisotropy parameters.

9. An anisotropic parameter calculation device, characterized in that: include: A predicted velocity value calculation module is used to calculate the predicted velocity value corresponding to each candidate geological parameter using a pre-built rock physics model; A candidate geological parameter retaining module is used to retain the corresponding candidate geological parameter when the comparison result between the predicted velocity value and the measured velocity value meets the error judgment condition; an inversion module, configured to perform inversion on the retained candidate geological parameters using the rock physics model to obtain inversion parameters; An ambient temperature reduction module is used to reduce the ambient temperature and repeat the above operations of calculating the predicted speed value, judging the error condition, and inverting until the ambient temperature is reduced to a preset minimum temperature; The anisotropic parameter calculation module is used to calculate the anisotropic parameters by integrating the inversion parameters under different ambient temperatures.

10. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: The computer program / instructions, when executed, are used to implement the steps of the method according to any one of claims 1 to 8.

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