A reservoir brittleness index seismic prediction method, device and readable storage medium

Abstract

The present application provides a kind of reservoir brittleness index seismic prediction method, device, computer readable storage medium and electronic equipment, the method comprises: according to the relationship of the elastic parameter of target area rock with the change of pressure Determine the relationship of the reservoir brittleness index of target area with the change of effective confining pressure;By inversion, determine the reservoir brittleness index data volume of target area without considering the influence of effective confining pressure;Based on the well logging data and seismic data of target area, calculate the formation pressure of target area, and determine the effective confining pressure data volume of target area according to the formation pressure;Based on the reservoir brittleness index data volume of target area without considering the influence of effective confining pressure, the effective confining pressure data volume of target area, according to the relationship of the reservoir brittleness index of target area with the change of effective confining pressure, determine the reservoir brittleness index data volume of target area considering the influence of effective confining pressure.The technical scheme of the present application improves the accuracy and rationality of reservoir brittleness index seismic prediction, especially provides technical support for guiding deep shale gas well site deployment and well trajectory design.

Description

Technical Field

[0001] This invention relates to the field of petroleum geophysical exploration technology, and in particular to a method, apparatus, computer-readable storage medium, and electronic device for seismic prediction of reservoir brittleness index considering the influence of effective confining pressure. Background Technology

[0002] For unconventional shale gas reservoir exploration and development, the reservoir brittleness index is a crucial parameter for the "engineering sweet spot," effectively characterizing the fracturability of rocks. Conventional brittleness index prediction techniques primarily rely on pre-stack seismic inversion methods. Elastic parameters are obtained through seismic inversion and directly converted into the brittleness index, providing a three-dimensional spatial representation to guide well location deployment and well trajectory design. However, with further exploration and development of deep shale gas, the strata face high-temperature and high-pressure geological environments, altering the ductility and plasticity of the rocks. Conventional brittleness index prediction does not consider these deep geological conditions, resulting in low accuracy and failing to meet the demands of efficient exploration and development of deep shale gas. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a method, apparatus, computer-readable storage medium, and electronic device for seismic prediction of reservoir brittleness index. This method improves the accuracy and rationality of seismic prediction of brittleness index by considering the influence of deep effective confining pressure on reservoir brittleness index, and provides scientific and technical support for guiding the deployment of deep shale gas well locations and well trajectory design.

[0004] In a first aspect, embodiments of the present invention provide a method for seismic prediction of reservoir brittleness index, comprising the following steps:

[0005] The relationship between the elastic parameters of the rock in the target area and the pressure is determined based on the relationship between the reservoir brittleness index and the effective confining pressure in the target area.

[0006] The reservoir brittleness index data volume of the target area was determined by inversion without considering the influence of effective confining pressure;

[0007] Calculate the formation pressure in the target area based on well logging data and seismic data, and determine the effective confining pressure data volume of the target area based on the formation pressure;

[0008] Based on the reservoir brittleness index data volume of the target area without considering the influence of effective confining pressure and the effective confining pressure data volume of the target area, the reservoir brittleness index data volume of the target area considering the influence of effective confining pressure is determined according to the relationship between the reservoir brittleness index and the effective confining pressure of the target area.

[0009] According to an embodiment of the present invention, determining the relationship between the reservoir brittleness index of the target area and the effective confining pressure based on the relationship between the elastic parameters of the target area rock and pressure includes the following steps:

[0010] The elastic parameters of the rock in the target area under different pressure conditions are measured, and the reservoir brittleness index is determined based on the elastic parameters.

[0011] Based on the relationship between the elastic parameters of the target area rock and different pressures, a template for the relationship between the reservoir brittleness index and the effective confining pressure in the target area is determined.

[0012] Based on the template of the relationship between reservoir brittleness index and effective confining pressure in the target area, the optimal relationship between reservoir brittleness index and effective confining pressure in the target area is fitted by the least squares method.

[0013] The relationship between the reservoir brittleness index and the effective confining pressure in the target area is determined based on the optimal relationship between the reservoir brittleness index and the effective confining pressure in the target area.

[0014] According to an embodiment of the present invention, the elastic parameters include longitudinal wave velocity and transverse wave velocity perpendicular to the rock bedding.

[0015] According to an embodiment of the present invention, determining the reservoir brittleness index data volume of the target region without considering the influence of effective confining pressure through inversion includes the following steps:

[0016] Based on the pre-stack Young's modulus-Poisson's ratio AVO reflection coefficient approximation equation, the elastic Young's modulus data volume and Poisson's ratio data volume of the target region are obtained by Bayesian inversion.

[0017] Based on the elastic Young's modulus and Poisson's ratio data volumes of the target region, the reservoir brittleness index data volume of the target region is calculated without considering the influence of effective confining pressure.

[0018] According to an embodiment of the present invention, the calculation of formation pressure in the target area based on well logging data and seismic data of the target area, and the determination of the effective confining pressure data volume of the target area based on the formation pressure, includes the following steps:

[0019] Time-depth calibration is performed on well logging data and seismic data of the target area. The actual P-wave impedance data volume of the target area is obtained by post-stack impedance inversion. The overlying rock pressure data volume and hydrostatic pressure data volume of the target area are determined based on the actual P-wave impedance.

[0020] Based on the CPS formation pressure prediction model, the P-wave impedance data volume under the normal compaction trend of the target area is obtained by using the Kriging interpolation method.

[0021] Based on the actual P-wave impedance data volume, overlying rock pressure data volume, hydrostatic pressure data volume, and P-wave impedance data volume under normal compaction trend of the target area, the formation pressure data volume of the target area is determined by Eaton's formula.

[0022] Based on the overlying rock pressure data volume and formation pressure data volume of the target area, determine the effective confining pressure data volume of the target area.

[0023] According to an embodiment of the present invention, obtaining the P-wave impedance data volume under the normal compaction trend of the target area using the Kriging interpolation method based on the CPS formation pressure prediction model includes the following steps:

[0024] The following steps are used to determine the P-wave impedance of a well in the target area under normal compaction trend:

[0025] Calculate the elastic tensor of wet clay using empirical formulas:

[0026] Calculate the elastic tensor of sandy mixtures using the Voight-Reuss-Hill model;

[0027] The elastic tensor of the equivalent rock layer composed of wet clay-sand mixture-organic matter was calculated using the Backus average formula.

[0028] The longitudinal wave velocity of the equivalent rock layer is determined based on the density of the rock in the target area and the elastic tensor of the equivalent rock layer.

[0029] The longitudinal wave impedance under normal compaction trend of the target area is determined based on the density of the rock in the target area and the longitudinal wave velocity of the equivalent rock layer.

[0030] Based on the P-wave impedance of multiple wells in the target area under normal compaction trend, a P-wave impedance data volume under normal compaction trend in the target area is constructed.

[0031] According to an embodiment of the present invention, constructing a P-wave impedance data volume under the normal compaction trend of the target area based on the P-wave impedance of multiple wells in the target area includes the following steps:

[0032] Based on the P-wave impedance of multiple wells in the target area under normal compaction trend, a P-wave impedance data volume under normal compaction trend in the target area is constructed by Kriging interpolation.

[0033] In a second aspect, embodiments of the present invention provide a reservoir brittleness index seismic prediction device, comprising:

[0034] The variation relationship determination module is used to determine the relationship between the reservoir brittleness index and the effective confining pressure in the target area based on the relationship between the elastic parameters of the rock in the target area and the pressure.

[0035] Based on the reservoir brittleness index data volume of the target area without considering the influence of effective confining pressure and the effective confining pressure data volume of the target area, the reservoir brittleness index data volume of the target area considering the influence of effective confining pressure is determined according to the relationship between the reservoir brittleness index and the effective confining pressure of the target area.

[0036] The ideal index determination module is used to determine the reservoir brittleness index data volume of the target area without considering the influence of effective confining pressure through inversion.

[0037] The effective confining pressure determination module is used to calculate the formation pressure in the target area based on well logging data and seismic data of the target area, and to determine the effective confining pressure data volume of the target area based on the formation pressure.

[0038] The actual index determination module is used to determine the reservoir brittleness index data volume of the target area considering the influence of effective confining pressure, based on the reservoir brittleness index data volume of the target area without considering the influence of effective confining pressure and the effective confining pressure data volume of the target area, according to the relationship between the reservoir brittleness index of the target area and the effective confining pressure.

[0039] Thirdly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the reservoir brittleness index seismic prediction method as described above.

[0040] Fourthly, embodiments of the present invention provide an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, characterized in that, when the program is executed by the processor, it implements the reservoir brittleness index seismic prediction method as described above.

[0041] Compared with the prior art, the above-mentioned technical solution of the present invention has the following beneficial effects:

[0042] Compared with existing technologies, the technical solution of this invention constructs a template for the relationship between reservoir brittleness index and effective confining pressure through rock physics experiments, and further predicts the brittleness index under the influence of effective confining pressure based on three-dimensional seismic data, effectively improving the prediction accuracy of the brittleness index of deep rock formations (such as shale), so as to strongly support the efficient exploration and development of gas in unconventional rock formations (such as shale). Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart of the reservoir brittleness index prediction method under the influence of effective confining pressure provided in Embodiment 1 of the present invention;

[0045] Figure 2 This refers to the MTS815 Flex Test GT programmable servo rock mechanics testing system equipment used in Embodiment 1 of the present invention;

[0046] Figure 3 This is the core sample taken from the target formation during drilling, as used in Embodiment 1 of the present invention;

[0047] Figure 4 This is a schematic diagram illustrating the variation of longitudinal wave velocity with pressure in a vertical shale bedding according to Embodiment 1 of the present invention;

[0048] Figure 5 This is a schematic diagram illustrating the variation of shear wave velocity with pressure in a vertical shale bedding plane according to Embodiment 1 of the present invention.

[0049] Figure 6 This is a schematic diagram illustrating the variation of the reservoir brittleness index with effective confining pressure in Embodiment 1 of the present invention;

[0050] Figure 7 This is a comparison chart of well logging calculation curves of the brittleness index without considering the effect of effective confining pressure and the brittleness index considering the effect of effective confining pressure in Embodiment 1 of the present invention.

[0051] Figure 8 This is a brittleness index prediction plan view of the actual working area considering the effect of effective confining pressure in Embodiment 1 of the present invention;

[0052] Figure 9 This is the brittleness index prediction plane of the actual working area in Embodiment 1 of the present invention, which does not consider the influence of effective confining pressure.

[0053] Figure 10 This is a comparison chart of the brittleness index of the target reservoir in a single well and the brittleness index measured in the core sample before and after correction, according to Embodiment 1 of the present invention.

[0054] Figure 11 This is a schematic diagram of the structural composition of the electronic device according to Embodiment 4 of the present invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] This embodiment mainly provides a method for predicting reservoir brittleness index considering the influence of effective confining pressure. The main flowchart is as follows: Figure 1 As shown:

[0057] S100, using ultrasonic mechanical experimental equipment to measure the longitudinal and transverse wave velocities of core samples in the target area under different confining pressures;

[0058] Based on laboratory measurement results, a template for the relationship between longitudinal and transverse wave velocities and effective confining pressure was established for S200, and a formula for the relationship between the brittleness index and the effective confining pressure was constructed accordingly.

[0059] S300 calculates the overlying strata pressure and reservoir formation pressure based on well logging and seismic data, and calculates the effective confining pressure accordingly.

[0060] S400, based on the Young's modulus-Poisson's ratio AVO approximate reflection coefficient equation, the Young's modulus, Poisson's ratio, and density are obtained based on Bayesian inversion theory, and the conventional brittleness index data volume is calculated accordingly, that is, the brittleness index without considering the effect of effective confining pressure.

[0061] S500, based on the conventional brittleness index data volume and the effective confining pressure data volume of the target area, calculates the brittleness index prediction result considering the influence of the effective confining pressure according to the relationship between the brittleness index and the effective confining pressure.

[0062] It should be noted that the execution order of the above steps is not limited to this. For example, depending on actual needs, step S400 can be executed first, followed by step S300.

[0063] The following section uses shale reservoirs as an example to describe in detail the implementation details of each step.

[0064] The S100 first utilizes the independently developed MTS815 Flex Test GT programmable servo rock mechanics testing system to test the elastic parameters of core samples from the target layer under different pressure conditions based on the ultra-penetrating method. Tests were conducted on 15 rock samples at 13 pressure points to obtain the longitudinal wave velocity and transverse wave velocity perpendicular to the shale bedding.

[0065] S200, based on the brittleness index calculation method, directly converts the elastic parameters measured in the laboratory into the brittleness index, establishes a template for the relationship between the brittleness index and the effective confining pressure, and fits the optimal relationship between the brittleness index and the effective confining pressure using the least squares method.

[0066]

[0067]

[0068]

[0069] In the formula, Brit is the brittleness index, E is Young's modulus, σ is Poisson's ratio, and Vp V s ρ and ρ are the longitudinal wave velocity and transverse wave density measured in the laboratory, respectively.

[0070] The relationship between the brittleness index and the effective confining pressure, fitted using the least squares method, is as follows:

[0071]

[0072] In the formula, Britn(P) e P is the brittleness index under varying effective confining pressure, Brit(0) is the brittleness index without effective confining pressure, and P is the brittleness index without effective confining pressure. e For effective confining pressure, σ is Poisson's ratio under conditions without effective confining pressure.

[0073] S300 calculates overlying strata pressure and reservoir formation pressure based on well logging data and seismic data.

[0074] Specifically, step S300 can be further divided into the following steps.

[0075] S300.1, time-depth calibration of multiple wells in the work area, and longitudinal wave impedance obtained by post-stack wave impedance inversion, the calculation formulas of upper rock layer pressure and hydrostatic pressure based on time domain are as shown in (5)-(6), and the three-dimensional data volume of upper layer pressure and hydrostatic pressure is obtained by integration.

[0076]

[0077]

[0078] In the formula: Sv is the pressure of the overlying strata, and Phy is the hydrostatic pressure.

[0079] S300.2, based on the CPS formation pressure prediction model, obtains the P-wave impedance under normal compaction trend, and combines it with Kriging interpolation to obtain the three-dimensional data volume of P-wave impedance under normal compaction trend.

[0080] The specific steps to obtain the longitudinal wave impedance under normal compaction trend are as follows:

[0081] ① Calculate the elastic tensor of wet clay using empirical formulas:

[0082]

[0083]

[0084]

[0085] The values ​​of each independent parameter are:

[0086]

[0087] In the formula: φ and f c These represent the total porosity of the rock and the volumetric content of clay, respectively. κ is the porosity of wet clay, i.e., the volume fraction of pore fluid in wet clay; C ij It is a 6×6 elasticity matrix, representing the elastic tensor of wet clay, which is related to the porosity κ of wet clay and the elastic tensor of pure clay. The empirical formula is used to obtain it.

[0088] ② Calculate the elastic tensor of sandy mixtures using the Voight-Reuss-Hill model:

[0089]

[0090] in

[0091]

[0092]

[0093] In the formula: f i M is the volume content of the i-th component. i It is the elastic modulus (bulk modulus K or shear modulus μ) of the i-th component. The Voigt and Reuss models provide upper limits M for the equivalent rock modulus. V and lower limit M R The arithmetic mean of the upper and lower limits is used to obtain M. H .

[0094] After obtaining the elastic modulus, the elastic tensor of the sand mixture can be expressed as:

[0095]

[0096] ③ Calculate the elastic tensor of the equivalent shale composed of wet clay-sand mixture-organic matter using the Backus average formula.

[0097]

[0098] In the formula: C ij c represents the elastic stiffness component of the equivalent shale. ij For each phase, the elastic stiffness component is represented by angle brackets <·>, which indicate a volume-weighted average of the properties within the phase.

[0099] ④ Determine the longitudinal wave velocity of the equivalent rock layer based on the density of the rock in the target area and the elastic tensor of the equivalent rock layer, and determine the longitudinal wave impedance of the target area under normal compaction trend based on the density of the rock in the target area and the longitudinal wave velocity of the equivalent rock layer.

[0100] I P =V P·ρ

[0101]

[0102] In the formula: ρ represents the rock density, Vp represents the longitudinal wave velocity of the equivalent shale, and Ip is regarded as the longitudinal wave impedance under normal compaction conditions.

[0103] ⑤ Based on the calculation of longitudinal wave impedance from multiple wells within the work area, and combined with Kriging interpolation, a three-dimensional impedance data volume under normal compaction trend is obtained.

[0104] S300.3, based on Eaton's formula, can obtain the three-dimensional formation pressure data volume of the work area.

[0105] P = S V -((S V -Phy)·(I P / I P _NCT) C (16)

[0106] In the formula: P is the formation pressure, S V I is the pressure of the overlying rock, Phy is the hydrostatic pressure, and I is the pressure of the hydrostatic pressure. P For the actual inverted longitudinal wave impedance, I P _NCT is the longitudinal wave impedance under normal compaction trend, and c is Eaton constant.

[0107] S300.4, the effective confining pressure is calculated according to the following formula:

[0108] Pe = S v -P (17)

[0109] In the formula: Pe is the effective confining pressure, S v ρ is the pressure of the overlying rock, and P is the formation pressure.

[0110] (4) Conduct pre-stack AVO inversion based on the approximate equations (18)-(19) of Young's modulus-Poisson's ratio AVO reflection coefficient. Use angle-stacked seismic data, initial model, and incident angle seismic wavelets as inputs. Iterative inversion is carried out based on Bayesian inversion theory to output three-dimensional data volumes of Young's modulus, Poisson's ratio, and density.

[0111]

[0112] In the formula: E is Young's modulus, σ is Poisson's ratio, ρ is density, θ is the angle of incidence, and k is the ratio of P-wave velocity to S-wave velocity.

[0113] The three-dimensional data volume of the brittleness index without the influence of effective confining pressure can be obtained by using the conventional brittleness index calculation formula (19).

[0114]

[0115] In the formula: Brit is the brittleness index, E is Young's modulus, and σ is Poisson's ratio.

[0116] (5) According to the brittleness index calculation formula (20) under the influence of effective confining pressure, input the brittleness index data volume without the influence of effective confining pressure, the effective confining pressure data volume, and the Poisson's ratio data volume, and you can obtain the three-dimensional data volume of brittleness index prediction under the influence of effective confining pressure.

[0117]

[0118] In the formula: Britn(P e P is the brittleness index under varying effective confining pressure, Brit(0) is the brittleness index without effective confining pressure, and P is the brittleness index without effective confining pressure. e The effective confining pressure is σ, which is Poisson's ratio under conditions without confining pressure.

[0119] It should be noted that although this embodiment uses a three-dimensional data volume to describe the spatial characterization of the reservoir, it is not limited to this in practical applications. For example, a two-dimensional data volume can also be constructed to describe the planar characterization of the reservoir.

[0120] Example 2

[0121] This embodiment uses actual data from a shale gas production area in the Sichuan Basin to illustrate the effectiveness of the method. First, core samples are taken from the target layer (…). Figure 3 ), to conduct elastic parameter measurements under different pressure conditions ( Figure 4 , Figure 5 ), and create a template showing the relationship between brittleness index and confining pressure ( Figure 6 Based on the logging curves of this work area, the conventional brittleness index and the brittleness index considering the confining pressure were calculated. A comparison shows that as depth increases, the confining pressure on the rock increases, and the brittleness index calculated by this method is relatively smaller than that calculated by the conventional method. Figure 7 This aligns with geological understanding. Further analysis using actual seismic data from the work area, including Poisson's ratio, overlying strata pressure, formation pressure, and conventional brittleness index, yielded a final brittleness index prediction considering the influence of effective confining pressure. Figure 8 ), compared with the conventional fragility index prediction results ( Figure 9 In comparison, the overall trend of the target layer is consistent, and the brittleness index prediction considering the effective confining pressure is relatively smaller and more reasonable. Single-well comparisons show that the brittleness index prediction considering the effective confining pressure is significantly better than the conventional brittleness index prediction. Figure 10 The decrease of 9.7% is consistent with laboratory measurement results and actual geological understanding.

[0122] The above analysis shows that the brittleness index prediction results considering the influence of effective confining pressure are more reasonable and accurate, effectively improving the prediction accuracy of the engineering "sweet spot", effectively guiding well location deployment and horizontal well trajectory design, and strongly supporting the efficient exploration and development of unconventional shale gas.

[0123] Example 3

[0124] This embodiment provides a reservoir brittleness index seismic prediction device, including:

[0125] The variation relationship determination module is used to determine the relationship between the reservoir brittleness index and the effective confining pressure in the target area based on the relationship between the elastic parameters of the rock in the target area and the pressure.

[0126] Based on the reservoir brittleness index data volume of the target area without considering the influence of effective confining pressure and the effective confining pressure data volume of the target area, the reservoir brittleness index data volume of the target area considering the influence of effective confining pressure is determined according to the relationship between the reservoir brittleness index and the effective confining pressure of the target area.

[0127] The ideal index determination module is used to determine the reservoir brittleness index data volume of the target area without considering the influence of effective confining pressure through inversion.

[0128] The effective confining pressure determination module is used to calculate the formation pressure in the target area based on well logging data and seismic data of the target area, and to determine the effective confining pressure data volume of the target area based on the formation pressure.

[0129] The actual index determination module is used to determine the reservoir brittleness index data volume of the target area considering the influence of effective confining pressure, based on the reservoir brittleness index data volume of the target area without considering the influence of effective confining pressure and the effective confining pressure data volume of the target area, according to the relationship between the reservoir brittleness index of the target area and the effective confining pressure.

[0130] Example 4

[0131] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When executed by a processor, the program performs the following... Figure 1 The steps of a seismic prediction method for reservoir brittleness index are shown.

[0132] It should be noted that all or part of the processes in the methods of the above embodiments of the present invention can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. Of course, there are other types of readable storage media, such as quantum memories, graphene memories, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0133] Example 5

[0134] Figure 11 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Figure 11 As shown, at the hardware level, this electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or it may include non-volatile memory, such as at least one disk drive. Of course, this electronic device may also include other hardware required for other business operations.

[0135] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 11 The bus is represented by only line segments, but this does not mean that there is only one bus or one type of bus.

[0136] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor. The processor reads the corresponding computer program from non-volatile memory into main memory and then runs it. The processor executes the program stored in memory to perform actions such as... Figure 1 This illustrates all the steps in a seismic prediction method for reservoir brittleness index.

[0137] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus. The communication interface is used for communication between the above electronic devices and other devices.

[0138] A bus, including hardware, software, or both, is used to couple the aforementioned components together. For example, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, a bus may include one or more buses. Although specific buses are described and illustrated in embodiments of the invention, the invention contemplates any suitable bus or interconnect.

[0139] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0140] The memory may include a large-capacity storage device for data or instructions. For example, and not limitingly, the memory may include a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disk drive, a magneto-optical disk drive, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where suitable, the memory may include removable or non-removable (or fixed) media. In a particular embodiment, the memory is a non-volatile solid-state memory. In a particular embodiment, the memory includes a read-only memory (ROM). Where suitable, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0141] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0142] It should be noted that those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0143] The apparatus, device, system, module, or unit described in the above embodiments can be implemented by a computer chip or entity, or by a product with a certain function. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, an in-vehicle human-machine interaction device, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0144] While this invention provides method operation steps as shown in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive means. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only execution order. In actual devices or terminal products, the method can be executed in the order shown in the embodiments or drawings, or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even a distributed data processing environment).

[0145] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0146] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0147] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1The steps of the function specified in one or more boxes.

[0148] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0149] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, electronic devices, and readable storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0150] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A method of reservoir brittleness index seismic prediction, characterized in that, The method comprises the following steps: determining a relationship between the reservoir brittleness index of the target area and the effective confining pressure according to the relationship between the elastic parameters of the rock in the target area and the pressure; determining the reservoir brittleness index data volume of the target area without considering the influence of the effective confining pressure through inversion; calculating the formation pressure of the target area based on the logging data and the seismic data of the target area, and determining the effective confining pressure data volume of the target area according to the formation pressure; determining the reservoir brittleness index data volume of the target area considering the influence of the effective confining pressure according to the relationship between the reservoir brittleness index of the target area and the effective confining pressure, the effective confining pressure data volume of the target area, and the reservoir brittleness index data volume of the target area without considering the influence of the effective confining pressure; the relationship between the reservoir brittleness index of the target area and the effective confining pressure is determined according to the relationship between the elastic parameters of the rock in the target area and the pressure, which comprises the following steps: measuring the elastic parameters of the rock in the target area under different pressure conditions, and determining the reservoir brittleness index according to the elastic parameters; determining the relationship template between the reservoir brittleness index of the target area and the effective confining pressure according to the relationship between the elastic parameters of the rock in the target area and the different pressures; fitting the best relationship between the reservoir brittleness index of the target area and the effective confining pressure through the least square method according to the relationship template between the reservoir brittleness index of the target area and the effective confining pressure; and determining the relationship between the reservoir brittleness index of the target area and the effective confining pressure according to the best relationship between the reservoir brittleness index of the target area and the effective confining pressure; the reservoir brittleness index data volume of the target area without considering the influence of the effective confining pressure is determined through inversion, which comprises the following steps: obtaining the elastic Young's modulus data volume and the Poisson's ratio data volume of the target area through Bayesian inversion based on the pre-stack Young's modulus-Poisson's ratio AVO reflection coefficient approximate equation; and calculating the reservoir brittleness index data volume of the target area without considering the influence of the effective confining pressure according to the elastic Young's modulus data volume and the Poisson's ratio data volume of the target area; the formation pressure of the target area is calculated based on the logging data and the seismic data of the target area, and the effective confining pressure data volume of the target area is determined according to the formation pressure, which comprises the following steps: time-depth calibration is performed on the logging data and the seismic data of the target area, the actual P-wave impedance data volume of the target area is obtained through post-stack wave impedance inversion, and the overlying rock pressure data volume and the hydrostatic pressure data volume of the target area are determined according to the actual P-wave impedance; the P-wave impedance data volume under the normal compaction trend of the target area is obtained by using the Kriging interpolation method based on the CPS formation pressure prediction model; the formation pressure data volume of the target area is determined through the Eaton formula according to the actual P-wave impedance data volume, the overlying rock pressure data volume, the hydrostatic pressure data volume, and the P-wave impedance data volume under the normal compaction trend of the target area; and the effective confining pressure data volume of the target area is determined according to the overlying rock pressure data volume and the formation pressure data volume of the target area.

2. The reservoir brittleness index seismic prediction method of claim 1, wherein, The elastic parameters comprise the P-wave velocity and the S-wave velocity perpendicular to the rock bedding.

3. The reservoir brittleness index seismic prediction method of claim 1, wherein, The CPS formation pressure prediction model is based on the following steps: The normal compaction trend of the target area is determined by the following steps: The elastic tensor of the wet clay is calculated by using the empirical formula: The elastic tensor of the sandy mixture is calculated by using the Voight-Reuss-Hill model; The elastic tensor of the equivalent rock layer composed of wet clay, sandy mixture and organic matter is calculated by using the Backus average formula; The longitudinal wave velocity of the equivalent rock layer is determined according to the density of the rock in the target area and the elastic tensor of the equivalent rock layer, and the longitudinal wave impedance of the target area under the normal compaction trend is determined according to the density of the rock in the target area and the longitudinal wave velocity of the equivalent rock layer; Based on the normal compaction trend of the target area, the normal compaction trend of the target area is determined by the following steps:

4. The reservoir brittleness index seismic prediction method of claim 3, wherein: Based on the normal compaction trend of the target area, the normal compaction trend of the target area is determined by the following steps: Comprising:

5. A reservoir brittleness index seismic prediction apparatus, characterized by, The change relationship determination module is used to determine the relationship between the reservoir brittleness index and the effective confining pressure according to the relationship between the elastic parameters of the rock in the target area and the pressure; The ideal index determination module is used to determine the reservoir brittleness index data body of the target area without considering the influence of the effective confining pressure by inversion; The effective confining pressure determination module is used to calculate the formation pressure of the target area based on the logging data and seismic data of the target area, and to determine the effective confining pressure data body of the target area according to the formation pressure; The actual index determination module is used to determine the reservoir brittleness index data body of the target area considering the influence of the effective confining pressure based on the reservoir brittleness index data body of the target area without considering the influence of the effective confining pressure, the effective confining pressure data body of the target area, and the relationship between the reservoir brittleness index and the effective confining pressure of the target area; The relationship between the reservoir brittleness index and the effective confining pressure of the target area is determined according to the relationship between the elastic parameters of the rock in the target area and the pressure, including the following steps: measuring the elastic parameters of the rock in the target area under different pressure conditions, and determining the reservoir brittleness index according to the elastic parameters; determining the relationship template of the reservoir brittleness index and the effective confining pressure of the target area according to the relationship between the elastic parameters of the rock in the target area and the pressure; fitting the best relationship between the reservoir brittleness index and the effective confining pressure of the target area by least squares method according to the relationship template of the reservoir brittleness index and the effective confining pressure of the target area; determining the relationship between the reservoir brittleness index and the effective confining pressure of the target area according to the best relationship between the reservoir brittleness index and the effective confining pressure of the target area; ​ The reservoir brittleness index data body of the target area determined by inversion without considering the influence of effective confining pressure comprises the following steps: obtaining the elastic Young's modulus data body and the Poisson's ratio data body of the target area by Bayesian inversion based on the approximate equation of pre-stack Young's modulus-Poisson's ratio AVO reflection coefficient; and calculating the reservoir brittleness index data body of the target area without considering the influence of effective confining pressure according to the elastic Young's modulus data body and the Poisson's ratio data body of the target area. The formation pressure of the target area is calculated based on the logging data and the seismic data of the target area, and the effective confining pressure data body of the target area is determined according to the formation pressure, which comprises the following steps: time-depth calibration is performed on the logging data and the seismic data of the target area, the actual P-wave impedance data body of the target area is obtained by post-stack wave impedance inversion, and the overlying rock pressure data body and the hydrostatic pressure data body of the target area are determined according to the actual P-wave impedance; the P-wave impedance data body under the normal compaction trend of the target area is obtained by using the Kriging interpolation method based on the CPS formation pressure prediction model; the formation pressure data body of the target area is determined by Eaton formula according to the actual P-wave impedance data body, the overlying rock pressure data body, the hydrostatic pressure data body and the P-wave impedance data body under the normal compaction trend of the target area; and the effective confining pressure data body of the target area is determined according to the overlying rock pressure data body and the formation pressure data body of the target area.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by the processor, implements the method of claim 1 4. The method of seismic reservoir brittleness index prediction of any one of claims 1 to 3.

7. An electronic device comprising a memory and a processor, said memory having stored thereon a computer program, characterized in that, The computer program, when executed by the processor, implements the method of claim 1 4. The method of claim 1-3.

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