Shale multi-scale fracture seismic prediction method, device, equipment and medium

Abstract

This disclosure provides a method, apparatus, equipment, and medium for multi-scale seismic prediction of shale fractures, relating to the field of oil and gas geophysical exploration technology. The method includes: calculating seismic coherence attributes based on post-stack seismic data using the Gradient Structure Tensor (GST) algorithm; probabilistically inferring the seismic coherence attributes to obtain the prior distribution of fracture parameters; determining the azimuth elastic impedance characterized by the fracture parameters based on azimuth seismic data and well logging data; establishing a target functional for fracture parameters based on seismic geometric attribute constraints based on the prior distribution of fracture parameters and the azimuth elastic impedance characterized by the fracture parameters; and optimally solving the target functional using a Bayesian direct inversion framework to obtain the prediction result. The embodiments of this disclosure effectively combine the response of seismic coherence and other attribute parameters to fracture development, further constraining the pre-stack seismic inversion of fracture parameters, reducing the ambiguity of fracture parameter inversion, and improving the accuracy of the inversion results.

Description

Technical Field

[0001] This disclosure relates to the field of oil and gas geophysical exploration technology, and in particular to a method, apparatus, equipment and medium for seismic prediction of multi-scale fractures in shale. Background Technology

[0002] Existing seismic prediction methods for shale fracture parameters are mainly based on the anisotropic reflection coefficient equation parameterized by rock physics. However, due to the large number of parameters, the fracture parameter inversion often suffers from strong ambiguity and poor robustness, making it difficult to effectively characterize the existence of multi-scale fractures in shale. While seismic geometric properties can reflect the development characteristics of fractures to some extent, they cannot quantitatively explain the fracture development and cannot effectively obtain relevant seismic parameters of the fractures.

[0003] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] This disclosure provides a method, apparatus, equipment, and medium for seismic prediction of multi-scale fractures in shale, which at least to some extent overcomes the problem of poor prediction results due to seismic prediction of shale fracture parameters in related technologies.

[0005] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0006] According to one aspect of this disclosure, a method for seismic prediction of multi-scale fractures in shale is provided, comprising:

[0007] Based on post-stack seismic data, seismic coherence properties were calculated using the Gradient Structure Tensor (GST) algorithm.

[0008] By using probabilistic inference theory, the seismic coherence properties are probabilistically analyzed to obtain the prior distribution of fracture parameters;

[0009] Based on azimuth seismic data and well logging data, determine the azimuth elastic impedance characterized by fracture parameters;

[0010] Based on the prior distribution of crack parameters and the azimuth elastic impedance characterized by crack parameters, an objective functional of crack parameters constrained by seismic geometric properties is established.

[0011] By using the Bayesian direct inversion framework, the objective functional is optimally solved to obtain the prediction results.

[0012] In one embodiment of this disclosure, determining the azimuth elastic impedance characterized by fracture parameters based on azimuth seismic data and well logging data includes:

[0013] Using azimuth seismic data, azimuth elastic impedance is obtained;

[0014] Rock physical analysis is obtained using well logging data;

[0015] Based on azimuth elastic impedance and rock physics analysis, the azimuth elastic impedance characterized by fracture parameters is obtained.

[0016] In one embodiment of this disclosure, the objective functional is optimally solved using a Bayesian direct inversion framework to obtain prediction results, including:

[0017] Pre-stack seismic direct inversion of fracture parameters constrained by seismic geometry is performed using Bayesian inference and maximizing probabilistic solutions to obtain the final prediction results.

[0018] According to another aspect of this disclosure, a shale multi-scale fracture seismic prediction device is provided, comprising:

[0019] The calculation module is used to calculate seismic coherence properties based on post-stack seismic data using the Gradient Structure Tensor (GST) algorithm.

[0020] The probabilistic module is used to probabilistically infer seismic coherence properties using probabilistic inference theory to obtain the prior distribution of fracture parameters.

[0021] The data processing module is used to determine the azimuth elastic impedance characterized by fracture parameters based on azimuth seismic data and well logging data.

[0022] The functional construction module is used to establish a target functional of crack parameters based on seismic geometric property constraints, based on the prior distribution of crack parameters and the azimuth elastic impedance characterized by crack parameters.

[0023] The prediction module is used to obtain the prediction results by using the Bayesian direct inversion framework to find the optimal solution for the objective functional.

[0024] According to another aspect of this disclosure, an electronic device is provided, comprising: a memory for storing instructions; and a processor for calling the instructions stored in the memory to implement the above-described shale multi-scale fracture seismic prediction method.

[0025] According to another aspect of this disclosure, a computer-readable storage medium is provided having computer instructions stored thereon, which, when executed by a processor, implement the above-described method for predicting multi-scale fractures in shale.

[0026] According to another aspect of this disclosure, a computer program product is provided, which stores instructions that, when executed by a computer, cause the computer to implement the above-described shale multi-scale fracture seismic prediction method.

[0027] According to another aspect of this disclosure, a chip is provided, including at least one processor and an interface;

[0028] An interface is used to provide program instructions or data to at least one processor;

[0029] At least one processor is used to execute program instructions to implement the above-described method for predicting shale multi-scale fractures.

[0030] The shale multi-scale fracture seismic prediction method provided in this disclosure effectively combines the response of attribute parameters such as seismic coherence to fracture development, and further constrains the pre-stack seismic inversion of fracture parameters, reducing the ambiguity of fracture parameter inversion and improving the accuracy of inversion results.

[0031] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0032] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0033] Obviously, the accompanying drawings described below are merely some embodiments of this disclosure. Those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0034] Figure 1 This diagram illustrates a flowchart of a shale multi-scale fracture seismic prediction method according to an embodiment of the present disclosure.

[0035] Figure 2 This diagram illustrates a flowchart of a shale multi-scale fracture seismic prediction method according to an embodiment of the present disclosure.

[0036] Figure 3 A schematic diagram showing the elastic impedance inversion results in different orientations in the embodiments of this disclosure is provided.

[0037] Figure 4 This shows a two-dimensional inversion profile of pre-stack seismic prediction of fracture parameters based on seismic geometric attribute constraints in an embodiment of this disclosure;

[0038] Figure 5 A comparison diagram is shown between the two-dimensional prediction results of crack density in the embodiments of this disclosure and the prediction of crack density by conventional methods;

[0039] Figure 6 This diagram illustrates a shale multi-scale fracture seismic prediction device according to an embodiment of the present disclosure.

[0040] Figure 7 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0041] The exemplary implementation will now be described more fully with reference to the accompanying drawings.

[0042] It should be noted that the example implementation can be implemented in many forms and should not be construed as being limited to the examples set forth herein.

[0043] Fractures, as important reservoir spaces in shale oil and gas, are crucial not only for evaluating geological sweet spots but also for drilling and fracturing in shale oil and gas reservoirs. Fracture development is controlled by multiple factors, making prediction and identification challenging. However, as a key indicator for oil and gas exploration and development, research on its seismic prediction has attracted widespread attention from scholars. Studies show that fractures increase rock permeability and provide space for oil and gas reservoirs, enabling shale formations to successfully produce oil and gas during fracturing. The development direction, degree of development, and distribution characteristics of shale fractures are influenced by tectonic movements, stress fields, and the mechanical properties of the rock. Currently, methods using seismic data to estimate fractures have become an important means of predicting underground fractures. Due to the presence of fractures, when seismic waves pass through fractured areas in shale formations, the seismic response characteristics are more complex compared to unfractured rocks. This is because the propagation velocity and amplitude of seismic waves change in the fractured medium.

[0044] With the acquisition of wide-azimuth seismic data, the development of fractures leads to azimuth anisotropy in earthquakes. This variation in the velocity and amplitude of P-waves with azimuth is called the AVAZ variation characteristic, which can be effectively used for fracture detection. Fracture earthquake prediction typically relies on a reasonable fracture medium parameterization theory. Currently, commonly used equivalent models of fracture rock physics include Hudson's (1980, 1981) model and Schoenberg's (1980) linear slip model. The combination of these two models can effectively quantify the relationship between fracture parameters and anisotropy parameters. Based on this, numerous published papers have derived seismic reflection coefficient equations based on various fracture medium parameterizations, such as Tsvankin's HTI anisotropic fracture reflection coefficient equation, Rüger's P-wave reflection coefficient equation in azimuthally anisotropic media, and Gray's AVAZ calculation method for fracture azimuth and density based on HTI media. However, because the anisotropic reflection coefficient equations usually involve many parameters to be inverted, the inversion robustness is poor, and there are many possible solutions.

[0045] The inventors discovered that the geometric properties of earthquakes have become one of the most powerful interpretative tools, effectively reflecting some of the seismic characteristics of fractured shale media. Attributes such as curvature, coherence, and dip angle can effectively characterize the geometric features of fractures. Curvature, a mathematical algorithm used to describe the degree of bending deformation of a stratum, is a mathematically effective means of identifying fracture development characteristics in areas with well-developed fractures. Coherence reflects the similarity of strata; because fractures disrupt the continuity of strata, differences in seismic phase axes also occur. Seismic coherence properties can also be effectively used to identify areas with well-developed fractures.

[0046] This disclosure proposes a multi-scale seismic prediction method for shale fractures considering seismic geometric attribute constraints. It effectively combines the response of seismic curvature, coherence, and other attribute parameters to fracture development, further constraining the pre-stack seismic inversion of fracture parameters to reduce the ambiguity of fracture parameter inversion and improve the accuracy of the inversion results. First, seismic geometric attributes are calculated using the Gradient Structure Tensor (GST) algorithm. Second, the obtained GST curvature and coherence attributes are probabilistically transformed and used as prior constraints for fracture parameter inversion. Finally, based on Bayesian probabilistic seismic inversion, the prior distribution of fracture parameters is established, and through a probabilistic model of seismic geometric attributes, based on anisotropic elastic impedance in different azimuths, the posterior probability distribution of fracture parameters is estimated and inverted for prediction, improving the accuracy of shale fracture prediction and description.

[0047] The following detailed description of this exemplary implementation method is provided in conjunction with the accompanying drawings and embodiments.

[0048] Figure 1 This invention discloses a flowchart of a shale multi-scale fracture seismic prediction method according to an embodiment of the present invention, as shown below. Figure 1 As shown in the embodiments of this disclosure, the shale multi-scale fracture seismic prediction method includes steps S110-S150.

[0049] In S110, seismic coherence properties are calculated based on post-stack seismic data using the Gradient Structure Tensor (GST) algorithm.

[0050] In some embodiments, the gradient structure GST algorithm is used to calculate the geometric properties of earthquakes, including earthquake curvature and earthquake coherence properties.

[0051] In S120, probabilistic inference theory is used to probabilistically transform seismic coherence properties and obtain the prior distribution of fracture parameters.

[0052] Using probabilistic inference theory, we probabilistically transform seismic coherence and curvature properties to establish a priori distribution of fracture development.

[0053] In S130, the azimuth elastic impedance characterized by fracture parameters is determined based on azimuth seismic data and well logging data.

[0054] In S140, based on the prior distribution of crack parameters and the azimuth elastic impedance characterized by crack parameters, an objective functional of crack parameters constrained by seismic geometric properties is established.

[0055] Using a Bayesian inversion framework, and based on the model-parameterized elastic impedance equation of the fracture medium, a fracture parameter seismic prediction objective functional constrained by seismic geometric properties is established.

[0056] In S150, the Bayesian direct inversion framework is used to find the optimal solution for the objective functional and obtain the prediction results.

[0057] In some embodiments, determining the azimuth elastic impedance characterized by fracture parameters based on azimuth seismic data and well logging data can be achieved by using azimuth seismic data to obtain azimuth elastic impedance; using well logging data to obtain rock physical analysis; and obtaining the azimuth elastic impedance characterized by fracture parameters based on azimuth elastic impedance and rock physical analysis.

[0058] In some embodiments, the Bayesian direct inversion framework is used to optimally solve the objective functional to obtain the prediction result. Alternatively, Bayesian inference and maximizing the probability solution can be used to perform pre-stack seismic direct inversion of crack parameters constrained by seismic geometric properties to obtain the final prediction result.

[0059] The embodiments disclosed herein are based on pre-stack seismic direct inversion of fracture parameters constrained by seismic geometric attributes. Compared with indirect prediction methods for fracture parameters without seismic geometric attribute constraints, the prediction accuracy is significantly improved. The method has been tested and applied in typical areas, providing effective geophysical technical support for shale oil and gas exploration and development.

[0060] Figure 2 This invention discloses a flowchart of a shale multi-scale fracture seismic prediction method according to an embodiment of the present invention, as shown below. Figure 2 As shown in the embodiments of this disclosure, seismic GST geometric coherence attributes are extracted from post-stack seismic data and probabilistically analyzed to obtain the prior distribution of fracture parameters; azimuth elastic impedance is obtained from azimuth seismic data, and a direct relationship between azimuth elastic impedance and fracture parameters is established; the prior distribution of fracture parameters formed by seismic geometric attributes is added to the objective functional of fracture parameter elastic impedance inversion; and a Bayesian direct inversion framework is used to achieve seismic inversion prediction of fracture parameters based on seismic geometric attribute constraints. The fracture density is further calculated using the obtained fracture inversion parameters.

[0061] In the above embodiments, the geometric coherence attributes of seismic GST are extracted using post-stack seismic data and probabilistically analyzed to obtain the prior distribution of fracture parameters, specifically including:

[0062] The GST algorithm used to extract coherence attributes is a third-generation coherence algorithm based on intrinsic structure, capable of scanning seismic data by dip or azimuth. First, autocorrelation and cross-correlation are performed on the columns of the sample vectors; at this point, the elements of the covariance matrix can be represented as:

[0063] (4)

[0064] Elements of the covariance matrix It calculates the cross-correlation between two vertical seismic waveforms within the calculation window.

[0065] The eigenvalue and eigenvector decomposition of the covariance matrix can be written as:

[0066] (5)

[0067] in, For feature vectors, The eigenvalues ​​corresponding to the eigenvectors. Then the coherence value can be expressed as:

[0068] (6)

[0069] in, This represents the cross-correlation between two vertical seismic waveforms within the calculation window. The smaller the coherence value, the greater the probability of the presence of cracks. Therefore, the probability model for cracks occurring in the entire three-dimensional volume can be equivalently obtained as:

[0070] (7)

[0071] in, This is the coherence value.

[0072] In the above embodiments, azimuth elastic impedance is obtained using azimuth seismic data, and a direct relationship between azimuth elastic impedance and fracture parameters is established, specifically including:

[0073] The relationship between the linear sliding model parameters and the Thomsen anisotropy parameters can be expressed as:

[0074] (8)

[0075] in, Indicates the normal weakness of the crack. Indicates the tangential weakness of the crack. It is the crack density. It is the square of the reciprocal of the ratio of the P-wave velocity to the S-wave velocity in the fractured rock. Therefore, the fracture density can be expressed by the parameters of the linear slip model as:

[0076] (9)

[0077] Using equation (9), the crack density can be calculated by retrieving the elastic parameters of the cracked rock and the parameters of the linear slip model from the seismic data.

[0078] The method for inverting azimuthal elastic impedance based on fracture density in shale reservoirs is based on a normalized approximation formula for azimuthal anisotropic elastic impedance:

[0079] (10)

[0080] Take the logarithm of both sides of equation (10):

[0081] (11)

[0082] in, It is the longitudinal wave impedance. It is the transverse wave impedance. Indicates the angle of incidence of the earthquake. It is the azimuth angle. The coefficients in the formula are expressed as follows:

[0083] (12)

[0084] (13)

[0085] (14)

[0086] (15)

[0087] To solve for the physical parameters of the fractured rock, it can be seen from formula (11) that seismic data from more than four azimuths are required:

[0088] (16)

[0089] Using formula (16), we have established a deterministic relationship between fractured rock parameters and azimuth elastic impedance, which can be simplified into matrix form:

[0090] (17)

[0091] In the formula, Corresponding to the azimuth elastic impedance on the left side of equation (16), It is a coefficient matrix. These are the rock physical parameters to be solved.

[0092] Figure 3 The results of elastic impedance inversion are shown in different orientations.

[0093] In the above embodiments, the prior distribution of crack parameters formed by seismic geometric properties is added to the objective functional of crack parameter elastic impedance inversion, specifically including:

[0094] With the constraints of the crack occurrence probability model, and using the Bayesian inversion theory framework, the posterior probability distribution of the crack parameters can be written as:

[0095] (18)

[0096] in, It is the mean. It is covariance. It's the weight.

[0097] The crack occurrence probability model converted from seismic coherence values ​​can be added as a constraint term to the inversion objective function. The objective function can then be written in the form of coherence constraints and model constraints:

[0098] (19)

[0099] in , These are the inversion weighting coefficients. For parametric models.

[0100] The final objective functional of the crack parameters can then be expressed as:

[0101] (20)

[0102] In the above embodiments, the Bayesian direct inversion framework is used to find the optimal solution for the objective functional, thereby realizing the seismic inversion prediction of crack parameters based on seismic geometric attribute constraints and obtaining the final inversion result.

[0103] Figure 4 This paper presents a two-dimensional seismic profile for pre-stack seismic prediction of fracture parameters based on seismic geometric attribute constraints, demonstrating the effectiveness of the proposed technique and improving the stability and accuracy of multi-parameter fracture prediction.

[0104] Figure 5 This figure compares the two-dimensional fracture density prediction results considering seismic geometric constraints with those predicted by conventional methods to verify the accuracy of the proposed method. The well string in the figure shows the interpreted fracture density results, which show a high degree of agreement with the two-dimensional fracture density prediction results considering seismic geometric constraints, but a poor agreement with the results predicted by conventional methods. This further demonstrates that the proposed technology is more advanced than conventional methods and can better predict fractures in shale oil and gas reservoirs based on seismic conditions.

[0105] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result.

[0106] In some embodiments, certain steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be broken down into multiple steps for execution.

[0107] Based on the same inventive concept, this disclosure also provides a shale multi-scale fracture seismic prediction device, as described in the following embodiments. Since the principle by which this device solves the problem is similar to that of the above-described method embodiments, the implementation of this device embodiment can refer to the implementation of the above-described method embodiments, and repeated details will not be elaborated further.

[0108] Figure 6 This diagram illustrates a shale multi-scale fracture seismic prediction device according to an embodiment of the present disclosure, as shown below. Figure 6 As shown, the shale multi-scale fracture seismic prediction device 600 includes:

[0109] The calculation module 602 is used to calculate seismic coherence properties based on post-stack seismic data using the Gradient Structure Tensor (GST) algorithm.

[0110] The probabilistic module 604 is used to probabilistically infer seismic coherence properties using probabilistic inference theory to obtain the prior distribution of fracture parameters.

[0111] The data processing module 606 is used to determine the azimuth elastic impedance characterized by fracture parameters based on azimuth seismic data and well logging data.

[0112] Functional construction module 608 is used to establish a target functional of crack parameters based on seismic geometric property constraints, based on the prior distribution of crack parameters and the azimuth elastic impedance characterized by crack parameters.

[0113] The prediction module 610 is used to obtain the prediction result by using the Bayesian direct inversion framework to find the optimal solution for the objective functional.

[0114] In some embodiments, the data processing module is configured to obtain azimuth elastic impedance using azimuth seismic data; obtain rock physical analysis using well logging data; and obtain azimuth elastic impedance characterized by fracture parameters based on azimuth elastic impedance and rock physical analysis.

[0115] In some embodiments, the prediction module is used to perform pre-stack seismic direct inversion of crack parameters constrained by seismic geometric properties using Bayesian inference and maximizing probabilistic solutions to obtain the final prediction result.

[0116] The concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to define the order of functions performed by these devices, modules or units or their interdependencies.

[0117] Regarding the shale multi-scale fracture seismic prediction device in the above embodiments, the specific operation methods of each module have been described in detail in the embodiments of the shale multi-scale fracture seismic prediction method, and will not be elaborated here.

[0118] It should be noted that although several modules or units of the device used for action execution are mentioned in the detailed description above, this division is not mandatory.

[0119] In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0120] Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0121] The following reference Figure 7 This describes the electronic device provided in the embodiments of this disclosure. Figure 7 The electronic device 700 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0122] Figure 7 This diagram illustrates the architecture of an electronic device 700 provided in an embodiment of the present invention. Figure 7 As shown, the electronic device 700 includes, but is not limited to, at least one processor 710 and at least one memory 720.

[0123] Memory 720 is used to store instructions.

[0124] In some embodiments, memory 720 may include a readable medium in the form of volatile memory cells, such as random access memory (RAM) 7201 and / or cache memory 7202, and may further include read-only memory (ROM) 7203.

[0125] In some embodiments, the memory 720 may also include a program / utility 7204 having a set (at least one) program module 7205, such program module 7205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0126] In some embodiments, the memory 720 may store an operating system. This operating system may be a real-time operating system (RTX), such as Linux, UNIX, Windows, or OS X.

[0127] In some embodiments, the memory 720 may also store data.

[0128] As an example, processor 710 can read data stored in memory 720, which may be stored at the same memory address as the instruction, or the data may be stored at a different memory address than the instruction.

[0129] Processor 710 is configured to invoke instructions stored in memory 720 to implement the steps described in the "Exemplary Methods" section above, according to various exemplary embodiments of this disclosure. For example, processor 710 may execute the following steps of the above method embodiments:

[0130] Based on post-stack seismic data, seismic coherence properties were calculated using the Gradient Structure Tensor (GST) algorithm.

[0131] By using probabilistic inference theory, the seismic coherence properties are probabilistically analyzed to obtain the prior distribution of fracture parameters;

[0132] Based on azimuth seismic data and well logging data, determine the azimuth elastic impedance characterized by fracture parameters;

[0133] Based on the prior distribution of crack parameters and the azimuth elastic impedance characterized by crack parameters, an objective functional of crack parameters constrained by seismic geometric properties is established.

[0134] By using the Bayesian direct inversion framework, the objective functional is optimally solved to obtain the prediction results.

[0135] It should be noted that the processor 710 described above can be a general-purpose processor or a special-purpose processor. The processor 710 may include one or more processing cores, and the processor 710 executes various functional applications and data processing by running instructions.

[0136] In some embodiments, processor 710 may include a central processing unit (CPU) and / or a baseband processor.

[0137] In some embodiments, the processor 710 may determine an instruction based on the priority identifier and / or function category information carried in each control instruction.

[0138] In this disclosure, the processor 710 and the memory 720 can be configured separately or integrated together.

[0139] As an example, the processor 710 and memory 720 can be integrated on a single board or a system-on-a-chip (SOC).

[0140] like Figure 7 As shown, the electronic device 700 is embodied in the form of a general-purpose computing device. The electronic device 700 may also include a bus 730.

[0141] Bus 730 can represent one or more of several types of bus structures, including a memory bus or memory controller, peripheral bus, graphics acceleration port, processor, or a local bus using any of the various bus structures.

[0142] Electronic device 700 can also communicate with one or more external devices 740 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 700, and / or with any device that enables electronic device 700 to communicate with one or more other computing devices (e.g., router, modem, etc.). Such communication can be performed through input / output (I / O) interface 770.

[0143] Furthermore, the electronic device 700 can also communicate with one or more networks (such as local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via the network adapter 760.

[0144] like Figure 7 As shown, the network adapter 760 communicates with other modules of the electronic device 700 via the bus 730.

[0145] It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 700, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0146] It is understood that the structure illustrated in the embodiments of this disclosure does not constitute a specific limitation on the electronic device 700. In other embodiments of this disclosure, the electronic device 700 may include more than Figure 7 This may involve more or fewer components, or combining certain components, or splitting certain components, or different component arrangements. Figure 7 The components shown can be implemented in hardware, software, or a combination of both.

[0147] This disclosure also provides a computer-readable storage medium storing computer instructions thereon, which, when executed by a processor, implement the shale multi-scale fracture seismic prediction method described in the above method embodiments.

[0148] In this embodiment of the disclosure, the computer-readable storage medium is a computer instruction that can be sent, propagated, or transmitted for use by or in conjunction with an instruction execution system, apparatus, or device.

[0149] As an example, a computer-readable storage medium is a non-volatile storage medium.

[0150] In some embodiments, more specific examples of computer-readable storage media in this disclosure may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, USB flash drives, portable hard drives, or any suitable combination of the foregoing.

[0151] In this embodiment of the disclosure, the computer-readable storage medium may include data signals propagated in baseband or as part of a carrier wave, wherein computer instructions (readable program code) are carried.

[0152] The transmitted data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof.

[0153] In some examples, computational instructions contained on a computer-readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0154] This disclosure also provides a computer program product that stores instructions that, when executed by a computer, cause the computer to implement the shale multi-scale fracture seismic prediction method described in the above method embodiments.

[0155] The aforementioned instructions can be program code. In practice, the program code can be written using any combination of one or more programming languages.

[0156] Programming languages ​​include object-oriented programming languages—such as Java and C++—as well as conventional procedural programming languages—such as the "C" language or similar programming languages.

[0157] The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0158] In cases involving remote computing devices, the remote computing devices can be connected to user computing devices via any type of network, including local area networks (LANs) or wide area networks (WANs), or they can be connected to external computing devices (e.g., via the Internet using an Internet service provider).

[0159] This disclosure also provides a chip, including at least one processor and an interface;

[0160] An interface is used to provide program instructions or data to at least one processor;

[0161] At least one processor is used to execute program instructions to implement the shale multi-scale fracture seismic prediction method described in the above method embodiments.

[0162] In some embodiments, the chip may further include a memory for storing program instructions and data, the memory being located within or outside the processor.

[0163] Those skilled in the art will understand that all or part of the steps of the above embodiments can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which can be collectively referred to as "circuit", "module" or "system".

[0164] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein.

[0165] This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

Claims

1. A method for seismic prediction of multi-scale fractures in shale, characterized in that, include: Based on post-stack seismic data, seismic coherence properties were calculated using the Gradient Structure Tensor (GST) algorithm. Using probabilistic inference theory, the earthquake coherence properties are probabilistically analyzed to obtain the prior distribution of fracture parameters; The seismic coherence properties are probabilistically expressed using the following formula: ； In the formula, This represents the prior crack parameters after the seismic geometric properties have been probabilistically converted. Based on azimuth seismic data and well logging data, determine the azimuth elastic impedance characterized by fracture parameters; Based on the prior distribution of the crack parameters and the azimuth elastic impedance characterized by the crack parameters, an objective functional of the crack parameters based on seismic geometric property constraints is established. as follows: ； in, and For the inversion weighting coefficients, in the formula Corresponding to azimuth elastic impedance It is a coefficient matrix. These are the crack parameters to be solved. It is a given constant matrix; Using the Bayesian direct inversion framework, the objective functional is optimally solved to obtain the prediction result.

2. The method according to claim 1, characterized in that, The determination of the azimuth elastic impedance characterized by fracture parameters based on azimuth seismic data and well logging data includes: Using azimuth seismic data, azimuth elastic impedance is obtained; Rock physical analysis is obtained using well logging data; Based on the azimuth elastic impedance and the rock physical analysis, the azimuth elastic impedance characterized by the fracture parameters is obtained.

3. The method according to claim 1, characterized in that, The seismic coherence properties are calculated using the following formula: ； in, This indicates the cross-correlation between two vertical seismic waveforms within the calculation window. This represents the eigenvalues ​​corresponding to the eigenvectors. This is the coherence value.

4. The method according to claim 1, characterized in that, The method of using the Bayesian direct inversion framework to optimally solve the objective functional and obtain the prediction result includes: Pre-stack seismic direct inversion of fracture parameters constrained by seismic geometry is performed using Bayesian inference and maximizing probabilistic solutions to obtain the final prediction results.

5. An electronic device, characterized in that, include: Memory, used to store instructions; A processor is configured to invoke instructions stored in the memory to implement the shale multi-scale fracture seismic prediction method as described in any one of claims 1-4.

6. A computer-readable storage medium storing computer instructions thereon, characterized in that, When the computer instructions are executed by the processor, they implement the shale multi-scale fracture seismic prediction method according to any one of claims 1-4.

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