Formation pressure constrained fracture density prediction method and related equipment

Abstract

This disclosure provides a method and related equipment for predicting fracture density under formation pressure constraints, relating to the field of geophysical exploration technology. The method comprises: obtaining geological and well logging information; determining anisotropy parameters based on the geological and well logging information; calculating a formation pressure coefficient based on measured formation pressure; and, based on the formation pressure coefficient and anisotropy parameters, obtaining a fracture density prediction result under formation pressure constraints. According to embodiments of the present disclosure, formation fracture density can be more accurately described, minimizing the impact of formation pressure.

Description

Technical Field

[0001] The present disclosure relates to the field of geophysical exploration technology, and in particular to a formation pressure-constrained fracture density prediction method and related equipment. Background Art

[0002] The study of formation fracture parameters is of great significance in fields such as oil and gas field development. Fracture density, a key indicator of fracture parameters, provides guidance for describing the migration, accumulation, and reservoir formation of oil and gas. Predicting and evaluating reservoir fracture density is a crucial task in oil and gas exploration and development.

[0003] Many experts and scholars have conducted research on the seismic prediction of fracture parameters such as fracture density, such as three-dimensional seismic attribute prediction technology, multi-wave multi-component prediction technology, and P-wave azimuthal anisotropy prediction technology. Among them, P-wave azimuthal anisotropy fracture prediction technology predicts fracture parameters such as fracture density by observing the variation patterns of P-wave velocity, amplitude, frequency, and other attributes induced by fractures at the observation azimuth. Current methods for predicting fracture density based on anisotropy mainly predict anisotropic parameters through P-wave impedance and attenuation, and then combine the quantitative characterization of various anisotropic parameters with fracture density to predict fracture density. However, factors that affect anisotropic parameters include not only fracture parameters but also other factors such as formation pressure. Therefore, the fracture density prediction results obtained by this method will be affected by formation pressure.

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

[0005] The present disclosure provides a formation pressure-constrained fracture density prediction method and related equipment, which at least to a certain extent overcomes the problem of inaccurate fracture density prediction in related technologies.

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

[0007] According to one aspect of the present disclosure, a method for predicting fracture density constrained by formation pressure is provided, comprising:

[0008] Obtain geological information and logging information such as P-wave velocity, S-wave velocity and density;

[0009] Based on geological information and well logging information, anisotropy parameters are obtained;

[0010] Based on the measured formation pressure, the formation pressure coefficient is calculated;

[0011] According to the formation pressure coefficient and anisotropy parameters, the fracture density prediction results under the formation pressure constraint are obtained.

[0012] In one embodiment of the present disclosure, anisotropic parameters are obtained based on geological information and well logging information such as compressional wave velocity, shear wave velocity, and density, including:

[0013] Based on geological information and well logging information, a rock physics model is constructed according to rock physics characteristics;

[0014] The anisotropic parameters are obtained by combining the stiffness matrix of the rock physics model with the anisotropy theory.

[0015] In one embodiment of the present disclosure, the method further includes:

[0016] Obtain conventional logging information such as overburden pressure and normal compaction bulk modulus, and interpreted logging information such as hydrostatic pressure and observed bulk modulus;

[0017] Based on conventional logging information and interpreted logging information, the measured formation pressure is obtained, which includes overburden pressure and hydrostatic pressure.

[0018] According to another aspect of the present disclosure, a device for predicting fracture density constrained by formation pressure is provided, comprising:

[0019] Information acquisition module, used to obtain geological information and well logging information;

[0020] Parameter determination module, used to obtain anisotropic parameters based on geological information and well logging information;

[0021] A pressure coefficient determination module is used to calculate the formation pressure coefficient based on the measured formation pressure;

[0022] The density prediction module is used to obtain the fracture density prediction result under the formation pressure constraint based on the formation pressure coefficient and anisotropy parameters.

[0023] According to another aspect of the present disclosure, an electronic device is provided, comprising: a memory for storing instructions; and a processor for calling the instructions stored in the memory to implement the above-mentioned formation pressure-constrained fracture density prediction method.

[0024] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which computer instructions are stored. When the computer instructions are executed by a processor, the above-mentioned formation pressure-constrained fracture density prediction method is implemented.

[0025] According to yet another aspect of the present disclosure, a computer program product is provided. The computer program product stores instructions, which, when executed by a computer, enable the computer to implement the above-mentioned formation pressure-constrained fracture density prediction method.

[0026] According to yet another aspect of the present disclosure, there is provided a chip comprising at least one processor and an interface;

[0027] An interface for providing program instructions or data to at least one processor;

[0028] At least one processor is configured to execute program instructions to implement the above-mentioned formation pressure-constrained fracture density prediction method.

[0029] The formation pressure-constrained fracture density prediction method provided in the disclosed embodiments introduces a formation pressure coefficient as a correction factor to constrain the fracture density predicted by anisotropic parameters. Because anisotropic parameters are affected not only by fracture density but also by factors such as formation pressure, introducing the formation pressure coefficient to correct the predicted fracture density allows for more reasonable fracture density predictions, more accurately describing formation fracture density, and minimizing the influence of formation pressure.

[0030] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0032] Obviously, the drawings described below are only some embodiments of the present disclosure. A person skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0033] Figure 1 A flow chart of a method for predicting fracture density constrained by formation pressure according to an embodiment of the present disclosure is shown;

[0034] Figure 2 A flow chart of another method for predicting fracture density constrained by formation pressure according to an embodiment of the present disclosure is shown;

[0035] Figure 3a The Eaton method is used to predict the fracture density of the well bypass.

[0036] Figure 3b The following shows the prediction results of the well bypass fracture density using the method of the embodiment of the present disclosure;

[0037] Figure 4aThe Eaton method well logging fracture density prediction results are shown;

[0038] Figure 4b The following shows the well logging fracture density prediction results using the method of the embodiment of the present disclosure;

[0039] Figure 5 A schematic diagram of a fracture density prediction device constrained by formation pressure according to an embodiment of the present disclosure is shown;

[0040] Figure 6 A structural block diagram of an electronic device in an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0041] Example embodiments will be described more fully hereinafter with reference to the accompanying drawings.

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

[0043] The inventors discovered that the current anisotropy prediction method mainly predicts anisotropy parameters through longitudinal wave impedance, attenuation, etc., and then predicts fracture density through quantitative characterization of the relationship between various anisotropic parameters and fracture density. However, the actual influencing factors of anisotropy parameters are not limited to fracture parameters. Formation pressure and other factors are also the cause of inducing seismic anisotropy. The currently commonly used characterization relationship between anisotropy parameters and fracture density obviously ignores this point.

[0044] Existing methods for predicting fracture density based on anisotropic parameters generally assume that the contribution of formation pressure is negligible. However, in reality, formation pressure has a significant impact on anisotropic parameters, raising questions about the validity of fracture density predictions based on anisotropic parameters. To more accurately describe formation fracture density and mitigate the influence of formation pressure, this paper introduces a formation pressure coefficient as a correction factor to constrain the fracture density predicted by anisotropic parameters. Because anisotropic parameters are affected not only by fracture density but also by factors such as formation pressure, the formation pressure coefficient is introduced to correct the predicted fracture density, resulting in more reliable fracture density predictions.

[0045] It should also be noted that the formation pressure-constrained fracture density prediction method of the embodiment of the present disclosure can be applied to electronic devices. The execution subject of the formation pressure-constrained fracture density prediction method can be at least one of the user terminals such as mobile phones, tablet computers, wearable devices, etc. that can be configured to execute the formation pressure-constrained fracture density prediction method provided by the embodiment of the present disclosure, or the execution subject of the method can also be the client itself that can execute the method.

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

[0047] Figure 1 A flow chart of a fracture density prediction method constrained by formation pressure according to an embodiment of the present disclosure is shown. Figure 1 As shown, the formation pressure-constrained fracture density prediction method provided in the embodiment of the present disclosure includes steps S110-S140.

[0048] In S110 , geological information and well logging information are obtained;

[0049] The well logging information in S110 may be well logging information such as longitudinal wave velocity, shear wave velocity, and density.

[0050] In S120, anisotropic parameters are obtained based on geological information and well logging information;

[0051] In some embodiments, anisotropy parameters are obtained based on geological information and logging information such as longitudinal wave velocity, shear wave velocity, and density. A rock physics model can be constructed based on the geological information and logging information according to rock physical characteristics; and anisotropy parameters are obtained through the stiffness matrix of the rock physics model in combination with anisotropy theory.

[0052] In S130, the formation pressure coefficient is calculated based on the measured formation pressure;

[0053] In some embodiments, conventional logging information such as overburden pressure, normal compaction bulk modulus, etc. and interpreted logging information such as hydrostatic pressure and observed bulk modulus, etc. are obtained; based on the conventional logging information and the interpreted logging information, the measured formation pressure is obtained.

[0054] In S140 , a fracture density prediction result under formation pressure constraint is obtained based on the formation pressure coefficient and the anisotropy parameter.

[0055] The fracture density prediction method constrained by formation pressure provided by the embodiments of the present disclosure takes into account the influence of formation pressure on anisotropic parameters, overcomes the limitations of using anisotropic parameters to predict fracture density, and makes the fracture density prediction method more reasonable.

[0056] Figure 2 A flow chart of a fracture density prediction method constrained by formation pressure is shown in the embodiment of the present disclosure. Figure 2 , illustrating the formation pressure constrained fracture density prediction method provided by the embodiment of the present disclosure.

[0057] like Figure 2 As shown, in the embodiment of the present disclosure, a rock physics model is constructed based on existing information such as well logging and geology, and according to rock physics characteristics; the stiffness matrix obtained from the rock physics model is combined with anisotropy theory to achieve the determination of anisotropy parameters.

[0058] In the embodiment of the present disclosure, information such as overburden pressure, hydrostatic pressure, and bulk modulus is extracted through conventional well logging and interpretation of well logging information, and the formation pressure is calculated according to the bulk modulus method.

[0059] Based on the observed bulk modulus and the normal compaction bulk modulus, the formation pressure coefficient term is obtained. The formation pressure coefficient and anisotropy parameters are introduced into the new method model to obtain the fracture density prediction results under the formation pressure constraint.

[0060] The following details Figure 1 and Figure 2 The specific implementation process of each step.

[0061] Taking HTI media as an example, based on geological information and well logging information and rock physical characteristics, the rock physical model of HTI media is obtained, and its stiffness matrix is ​​expressed as follows:

[0062]

[0063] Among them, C 11 、C 12 、C 13 、C 22 、C 23 、C 33 、C 44 、C 55 、C 66 Both represent stiffness parameters. And C 12 =C 13 、C 22 =C 33 、C 55 =C 66 、C 23 =C 22 -2C 44 .

[0064] The relationship between anisotropy parameters and stiffness parameters is as follows:

[0065]

[0066]

[0067]

[0068] Analysis of the rock physics model shows that when fractures are filled with gas and oil and water respectively, the anisotropy parameter and fracture density have the following relationship (Bakulin, 2000):

[0069] (1) During gas filling, the quantitative characterization of anisotropy parameters and crack density is obtained based on the relationship between anisotropy parameters and linear sliding model parameters:

[0070]

[0071]

[0072]

[0073] (2) Quantitative characterization of anisotropic parameters and fracture density during oil-water filling: (8)

[0075] ε (V) =0

[0076]

[0077]

[0078] In the above formula, ε (V) , δ (V) And γ is the anisotropy parameter, g is the square of the ratio of shear wave to longitudinal wave, and e is the crack density.

[0079] By comparison, it can be seen that the change of crack filling material has no effect on the crack density relationship of γ, and its numerical change is related to the crack density.

[0080] According to the relationship between the anisotropy parameter γ and the crack density e, the crack density expression is obtained:

[0081]

[0082] In order to eliminate the influence of formation pressure, it is necessary to obtain the formation pressure. The formation pressure prediction method is as follows:

[0083]

[0084] Where p is the pore pressure, σ v is the overburden stress, σ n is the hydrostatic pressure, Δt s is the measured acoustic time difference, Δt n is the normal compaction acoustic wave time difference, c is the Eaton index, and when c=3, it is under-compaction and overpressure.

[0085] This formula is applicable to undercompaction and overpressure. Many scholars have subsequently improved and perfected it. For example, the following method is based on the bulk modulus of undercompaction theory to predict formation pressure:

[0086]

[0087] Where p is the pore pressure, σ v is the overburden stress, σ n is the hydrostatic pressure, K is the observed bulk modulus, K nis the normal compaction bulk modulus, a is a constant coefficient, with a = 1 as the initial value, which can adjust the sensitivity of the result to the contrast value.

[0088] Introducing the formation pressure coefficient term K pa ,make:

[0089]

[0090] Introducing the pressure coefficient term into the fracture density formula expressed by anisotropic parameters as a constraint term, we obtain:

[0091] e=3λ(3-2g)γ / 8(1+λ)+(2-K pa ) / (1+λ) (15)

[0092] Where e is the crack density, λ is the proportional coefficient, g is the square of the ratio of shear wave to longitudinal wave, K pa is the formation pressure coefficient.

[0093] The crack density obtained by this method is more reasonable. Figure 3a and Figure 3b The figure shows the comparison results of the fracture density predicted by conventional anisotropic parameters and the fracture density prediction results of the new method. 3a is the fracture density prediction result of the Eaton method, and 3b is the fracture density prediction result of the method of the embodiment of the present disclosure. It can be seen that the original method has a significant prediction error in the fracture density around 2920m to 2960m, and the new method has a good degree of consistency with the logging data in the selected section.

[0094] Figure 4a and Figure 4b The figure shows the comparison between the fracture density predicted by conventional anisotropic parameters and the fracture density predicted by the new method. 4a is the fracture density prediction result of the Eaton method, and 4b is the fracture density prediction result of the new method. It can be seen that the fracture density predicted by the original method near the H5 layer is higher than that of the H3 layer, which is inconsistent with the results reflected by the logging data. The prediction results of the new method are more consistent with the logging understanding.

[0095] Furthermore, although the steps of the methods of the present disclosure are depicted in a particular order in the drawings, this does not require or imply that the steps must be performed in this particular order, or that all illustrated steps must be performed to achieve desired results.

[0096] In some embodiments, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0097] Based on the same inventive concept, the presently disclosed embodiments also provide a device for predicting fracture density constrained by formation pressure, as described in the following embodiments. Because the principles underlying the problems solved by this device embodiment are similar to those of the aforementioned method embodiment, the implementation of this device embodiment can be referenced to the implementation of the aforementioned method embodiment, and any repetitions will not be repeated.

[0098] Figure 5 A schematic diagram of a fracture density prediction device constrained by formation pressure according to an embodiment of the present disclosure is shown. Figure 5 As shown, the formation pressure-constrained fracture density prediction device 500 includes:

[0099] Information acquisition module 502, used to obtain geological information and well logging information;

[0100] Parameter determination module 504, for obtaining anisotropy parameters based on geological information and well logging information;

[0101] A pressure coefficient determination module 506 is configured to calculate a formation pressure coefficient based on the measured formation pressure;

[0102] The density prediction module 508 is used to obtain the fracture density prediction result under the formation pressure constraint according to the formation pressure coefficient and the anisotropy parameter.

[0103] In some embodiments, the parameter determination module 504 may include:

[0104] A model building unit, for building a rock physics model according to rock physics characteristics based on geological information and well logging information;

[0105] The parameter obtaining unit is used to obtain anisotropic parameters by combining the stiffness matrix of the rock physics model with anisotropy theory.

[0106] In some embodiments, the stiffness matrix of the rock physics model is expressed as in formula (1) above.

[0107] In some embodiments, the relationship between the anisotropy parameter and the stiffness parameter is as shown in Formula (2), Formula (3) and Formula (4) above.

[0108] In some embodiments, the formation pressure-constrained fracture density prediction device 500 may further include:

[0109] The second acquisition module is used to obtain conventional logging information and interpret logging information;

[0110] The information processing module is used to obtain the measured formation pressure based on conventional logging information and interpreted logging information. The measured formation pressure includes overburden pressure and hydrostatic pressure.

[0111] In some embodiments, the fracture density prediction result under the formation pressure constraint is calculated using the above formula (15).

[0112] The concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0113] Regarding the formation pressure constrained fracture density prediction device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the formation pressure constrained fracture density prediction method, and will not be elaborated here.

[0114] It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, such division is not mandatory.

[0115] In fact, according to the embodiment of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0116] Some of the blocks shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0117] Refer to the following Figure 6 To describe the electronic device provided by the embodiment of the present disclosure. Figure 6 The electronic device 600 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0118] Figure 6 FIG. 1 shows a schematic diagram of the architecture of an electronic device 600 provided by an embodiment of the present invention. Figure 6 As shown, the electronic device 600 includes but is not limited to: at least one processor 610 and at least one memory 620.

[0119] The memory 620 is used to store instructions.

[0120] In some embodiments, the memory 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0121] In some embodiments, the memory 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0122] In some embodiments, the memory 620 may store an operating system, which may be a real-time operating system (RTX), LINUX, UNIX, WINDOWS, or OS X.

[0123] In some embodiments, data may also be stored in the memory 620 .

[0124] As an example, the processor 610 may read data stored in the memory 620 . The data may be stored at the same storage address as the instruction, or the data may be stored at a different storage address than the instruction.

[0125] The processor 610 is configured to call instructions stored in the memory 620 to implement the steps described in the "Exemplary Method" section above according to various exemplary embodiments of the present disclosure. For example, the processor 610 may perform the following steps of the aforementioned method embodiment:

[0126] Obtain geological information and well logging information;

[0127] Based on geological information and well logging information, anisotropy parameters are obtained;

[0128] Based on the measured formation pressure, the formation pressure coefficient is calculated;

[0129] According to the formation pressure coefficient and anisotropy parameters, the fracture density prediction results under the formation pressure constraint are obtained.

[0130] It should be noted that the processor 610 may be a general-purpose processor or a dedicated processor. The processor 610 may include one or more processing cores, and the processor 610 executes various functional applications and data processing by running instructions.

[0131] In some embodiments, the processor 610 may include a central processing unit (CPU) and / or a baseband processor.

[0132] In some embodiments, the processor 610 may determine an instruction based on the priority identifier and / or function category information carried in each control instruction.

[0133] In the present disclosure, the processor 610 and the memory 620 may be provided separately or integrated together.

[0134] As an example, the processor 610 and the memory 620 may be integrated on a single board or a system on chip (SOC).

[0135] like Figure 6 As shown, the electronic device 600 is in the form of a general-purpose computing device. The electronic device 600 may further include a bus 630 .

[0136] Bus 630 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.

[0137] The electronic device 600 may also communicate with one or more external devices 640 (e.g., a keyboard, a pointing device, a Bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 600, and / or any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., a router, a modem, etc.). Such communication may be performed through an input / output (I / O) interface 650.

[0138] Furthermore, the electronic device 600 can also communicate with one or more networks (eg, a local area network (LAN), a wide area network (WAN) and / or a public network, such as the Internet) through the network adapter 660 .

[0139] like Figure 6 As shown, the network adapter 660 communicates with other modules of the electronic device 600 via the bus 630 .

[0140] It should be understood that although not shown in the figures, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0141] It is understood that the structure shown in the embodiment of the present disclosure does not constitute a specific limitation on the electronic device 600. In other embodiments of the present disclosure, the electronic device 600 may include Figure 6 More or fewer components may be shown, or some components may be combined or separated, or the components may be arranged differently. Figure 6 The components shown can be implemented in hardware, software, or a combination of software and hardware.

[0142] The present disclosure also provides a computer-readable storage medium having computer instructions stored thereon. When the computer instructions are executed by a processor, the method for predicting fracture density constrained by formation pressure described in the above method embodiment is implemented.

[0143] The computer-readable storage medium in the embodiments of the present disclosure is a computer instruction that can be sent, propagated or transmitted for use by or in conjunction with an instruction execution system, apparatus or device.

[0144] As an example, computer readable storage media are non-volatile storage media.

[0145] In some embodiments, more specific examples of computer-readable storage media in the present disclosure may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, a USB flash drive, a mobile hard disk, or any suitable combination of the foregoing.

[0146] In the embodiments of the present disclosure, the computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer instructions (readable program codes).

[0147] Such a propagated data signal may take any of a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof.

[0148] In some examples, computing instructions contained on a computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0149] The embodiments of the present disclosure further provide a computer program product, which stores instructions. When the instructions are executed by a computer, the computer implements the formation pressure-constrained fracture density prediction method described in the above method embodiment.

[0150] The above instructions may be program codes. In specific implementation, the program codes may be written in any combination of one or more programming languages.

[0151] Programming languages ​​include object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages.

[0152] The program code may execute entirely on the user's computing device, partly on the user's computing device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device or entirely on the remote computing device or server.

[0153] Where a remote computing device is involved, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0154] The embodiment of the present disclosure further provides a chip, comprising at least one processor and an interface;

[0155] An interface for providing program instructions or data to at least one processor;

[0156] At least one processor is configured to execute program instructions to implement the formation pressure constrained fracture density prediction method described in the above method embodiment.

[0157] In some embodiments, the chip may further include a memory for storing program instructions and data, and the memory may be located inside or outside the processor.

[0158] Those skilled in the art will appreciate that all or part of the steps for implementing the above embodiments may be implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software, which may be collectively referred to herein as a "circuit," "module," or "system."

[0159] Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein.

[0160] This disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the appended claims.

Claims

1. A fracture density prediction method constrained by formation pressure, characterized in that: include: Acquiring geological information and well logging information, wherein the well logging information includes compressional wave velocity, shear wave velocity, and density; Obtaining anisotropy parameters based on the geological information and the well logging information; Based on the measured formation pressure, the formation pressure coefficient is calculated; Obtaining a fracture density prediction result under formation pressure constraints according to the formation pressure coefficient and the anisotropy parameter; The fracture density prediction result under formation pressure constraint is calculated using the following formula: ； in, is the crack density, is the proportionality coefficient, is the square of the ratio of shear wave to longitudinal wave, is the formation pressure coefficient.

2. The method according to claim 1, characterized in that The obtaining of anisotropic parameters based on the geological information and the well logging information includes: constructing a rock physics model based on the geological information and the well logging information according to rock physics characteristics; The anisotropy parameters are obtained by combining the stiffness matrix of the rock physics model with the anisotropy theory.

3. The method according to claim 2, characterized in that The stiffness matrix of the rock physics model is expressed as follows: ； in, 、 、 、 、 、 、 、 、 、 、 、 are stiffness parameters, and 、 、 、 .

4. The method according to claim 2, characterized in that The relationship between anisotropy parameters and stiffness parameters is as follows: ； ； ； in, 、 as well as is the anisotropy parameter.

5. The method according to claim 1, wherein The method further comprises: Obtain conventional logging information and interpret logging information; The measured formation pressure is obtained based on the conventional logging information and the interpreted logging information.

6. A fracture density prediction device constrained by formation pressure, characterized in that: include: Information acquisition module, used to obtain geological information and well logging information; A parameter determination module, configured to obtain anisotropy parameters based on the geological information and the well logging information; A pressure coefficient determination module is used to calculate the formation pressure coefficient based on the measured formation pressure; A density prediction module, configured to obtain a fracture density prediction result under formation pressure constraints based on the formation pressure coefficient and the anisotropy parameter; The fracture density prediction result under formation pressure constraint is calculated using the following formula: ； in, is the crack density, is the proportionality coefficient, is the square of the ratio of shear wave to longitudinal wave, is the formation pressure coefficient.

7. An electronic device, characterized in that: include: a memory for storing instructions; The processor is configured to call the instructions stored in the memory to implement the method for predicting fracture density constrained by formation pressure as described in any one of claims 1 to 5.

8. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the computer instructions are executed by a processor, the method for predicting fracture density constrained by formation pressure as recited in any one of claims 1 to 5 is implemented.

9. A chip, characterized in that: comprising at least one processor and an interface; The interface is configured to provide program instructions or data to the at least one processor; The at least one processor is configured to execute the program instructions to implement the method for predicting fracture density constrained by formation pressure as recited in any one of claims 1 to 5.

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