Formation collapse pressure earthquake prediction method and device based on rock mechanics nonlinear change

Abstract

The invention relates to the technical field of oil exploration and development, and discloses a stratum collapse pressure earthquake prediction method and device based on rock mechanics nonlinear variation, and the method comprises the steps: carrying out the nonlinear fitting of compressive strength with rock cohesion and an internal friction angle through a triaxial mechanics experiment, and obtaining a corresponding fitting relation; then, through seismic inversion, a rock mechanical attribute body, an overlying stratum pressure body, a pore pressure body, a maximum horizontal principal stress body, a minimum horizontal principal stress body, and three-dimensional space rock cohesion distribution and internal friction angle distribution are obtained through calculation; and finally, according to the pore pressure body, the maximum horizontal principal stress body, the minimum horizontal principal stress body, the three-dimensional space rock cohesion distribution and the three-dimensional space rock internal friction angle distribution, predicting stratum three-dimensional collapse pressure body data in combination with a collapse pressure calculation model. By means of the method, the formation collapse pressure can be predicted more accurately, geological disasters caused by improper drilling operation can be reduced, and therefore the geological environment is protected, and potential influences on the earth surface and underground water resources are reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil exploration and development, in particular to seismic exploration and pre-drilling prediction of three pressure of petroleum engineering formation, more particularly to a method for predicting formation collapse pressure based on nonlinear change of rock mechanics. BACKGROUND

[0002] The wellbore formed in the drilling process will break the original underground stress balance, causing stress concentration of the rock around the well. When the effective liquid column pressure in the wellbore is too small, brittle rock will collapse, and plastic rock will shrink; when the effective liquid column pressure in the wellbore is too high, it will fracture the formation, both of which can lead to downhole complications and accidents. Collapse pressure is a complex and variable parameter related to the stability of the well wall around the well, which is determined by many factors such as the properties of the rock around the well, the depth, the temperature, the pressure, the formation pore pressure, the properties of the drilling fluid (water loss, inhibition, sealing performance, etc.). Researchers at home and abroad have found that the occurrence of well wall collapse is mainly related to the ground stress. Researchers have established a well wall collapse model by analyzing the strength of the rock downhole and the stress state around the well, combined with certain strength criteria. After years of theoretical and experimental research by many scholars, Mohr-Coulomb criterion has become one of the most commonly used strength criteria in rock engineering. The criterion believes that when rock shear failure occurs, it must overcome the sum of the inherent shear strength of the rock and the frictional resistance on the shear plane. Through a large number of true triaxial compression experiments, scholars have proposed the Mogi-Clulomb strength criterion considering the influence of principal stress. This criterion is actually an improved Mohr-Coulomb criterion, which fully considers the influence law of the intermediate principal stress. Its linear model uses rock mechanics parameters such as cohesion and internal friction angle. For example, Zhong Jingmin, et al., Drilling Dynamic Collapse Pressure Discussion, Drilling and Production Technology, 2012, 45(5): 11-14.

[0003] The most important thing in calculating the collapse pressure is to determine the rock mechanics parameters such as cohesion and internal friction angle in the collapse pressure calculation formula. The rock mechanics parameters used to calculate the collapse pressure can be obtained by rock mechanics tests on the corresponding rock.

[0004] Based on the linear Mohr-Coulomb criterion, cohesion and internal friction angle can be simply calculated from tensile strength and tensile capacity. Based on this criterion, formation collapse pressure can be calculated using the maximum and minimum horizontal principal stresses, cohesion, and internal friction angle (e.g., Qiu Shasha, Establishment and Application of Three-Pressure Profile for Deep Gas Reservoirs in the Shuangyushi Structure, Master's Thesis, Southwest Petroleum University, 2017). Due to the linear assumption, this calculation model estimates cohesion and internal friction angle at fixed values. However, the linear Mohr-Coulomb criterion considers the confining pressure effect: under the influence of confining pressure, the rock compressive strength increases, and the cohesion and internal friction angle also change accordingly. The constant cohesion and internal friction angle in this calculation model are clearly unreasonable, leading to theoretical errors in the calculation of formation collapse pressure.

[0005] The Mohr-Coulomb criterion states that the compressive strength of rock under triaxial confining pressure increases linearly with confining pressure. However, Mohr found that when the confining pressure is high and varies over a wide range, the relationship between triaxial strength and confining pressure is nonlinear. Similar tests on different rocks show that the nonlinear relationship between axial stress and confining pressure is common under large-scale high confining pressure, thus the nonlinear Mohr-Coulomb criterion was adopted. Based on this criterion, a parabolic relationship between cohesion and internal friction angle with confining pressure can be found. However, obtaining this relationship requires "large-scale, high-density" confining pressure tests, which significantly increases the experimental requirements and costs. In addition, since it is difficult to measure the actual rock confining pressure underground, and can only be approximated by pore pressure, such calculation models also have certain difficulties and accuracy errors in engineering practice. Summary of the Invention

[0006] To address the problems and shortcomings of the existing technologies, this invention proposes a seismic prediction method for formation collapse pressure based on nonlinear variations in rock mechanics. This invention uses triaxial mechanical experimental results from core samples to nonlinearly fit compressive strength, rock cohesion, and internal friction angle. Based on this fitting relationship and combined with three-dimensional compressive strength seismic prediction data, the corresponding rock cohesion distribution and internal friction angle distribution are estimated. Compared to constant cohesion and internal friction angle, this method better conforms to the experimental laws of rock mechanics. Then, based on the spatially nonlinear variations in rock cohesion and friction angle distribution, as well as the pore pressure volume, maximum horizontal principal stress volume, and minimum horizontal principal stress volume obtained through seismic inversion and calculation, these are substituted into the collapse pressure calculation formula to obtain the corresponding three-dimensional collapse pressure seismic prediction data.

[0007] To achieve the above-mentioned objectives, the technical solution of the present invention is as follows:

[0008] This invention discloses a method for predicting seismic collapse pressure based on nonlinear variations in rock mechanics, the method comprising the following steps:

[0009] Step S1. Perform triaxial mechanical experiments on the core samples to obtain multiple compressive strength data and multiple rock cohesion and internal friction angle data. Then, based on the experimental data obtained from the triaxial mechanical experiments, perform nonlinear fitting between the compressive strength and the rock cohesion and internal friction angle respectively to calculate the relevant equation set. Specifically, fit the compressive strength and the rock cohesion to obtain the first fitting relationship; fit the compressive strength and the rock internal friction angle to obtain the second fitting relationship; combine the two fitting relationships to obtain the following fitting equation set:

[0010]

[0011] Where C is the rock cohesion, in MPa; φ is the rock internal friction angle, in °; S c It represents the compressive strength of the rock.

[0012] Step S2. Obtain the P-wave velocity volume V at any spatial point (x, y, z) within the work area through seismic inversion. p (x,y,z), transverse wave velocity body V s (x,y,z) and density volume ρ(x,y,z). Seismic inversion technology makes full use of the rich structural, stratigraphic, and lithological information provided by well logging, drilling, and geological data to deduce information such as wave impedance, density, velocity, porosity, permeability, sandstone and mudstone percentage, and pressure of underground strata from conventional seismic profiles.

[0013] Step S3. The longitudinal wave velocity volume V obtained from the inversion in step S1 p (x,y,z), transverse wave velocity body V s The rock mechanical property volume is calculated using (x,y,z) and density volume ρ(x,y,z) at any point in space. The rock mechanical property volume required in this invention refers to the Young's modulus, Poisson's ratio, and compressive strength prediction volume of the rock.

[0014] In this invention, the specific expression for calculating Young's modulus is as follows:

[0015]

[0016] Where E(x,y,z) is the Young's modulus of the formation, in GPa;

[0017] In this invention, the calculation expression for the Poisson's ratio is as follows:

[0018]

[0019] Where μ(x,y,z) is the Poisson's ratio at any point (x,y,z) in space, which is dimensionless;

[0020] In this invention, the calculation expression for the compressive strength prediction body is as follows:

[0021] S c (x,y,z)=A×E(x,y,z)×(1-V sh )+B×E(x,y,z)×V sh (8);

[0022] Among them, S c (x, y, z) represents the compressive strength, measured in MPa; V sh The mud content is represented by A and B, which are approximation coefficients for different lithologies.

[0023] Step S4. Combining the P-wave velocity volume and density volume, obtain the overlying strata pressure volume and pore pressure volume data using the following calculation expressions. Overlying strata pressure refers to the pressure in the overlying strata caused by gravity, while pore pressure refers to the pressure of the fluid contained in the pores of the strata. The calculation expression for the overlying strata pressure volume is as follows:

[0024]

[0025] In the formula, S v (x,y,z) represents the gravity volume data of the overlying strata, in MPa; H represents the vertical depth at any spatial point, in meters; ρ(x,y,z) represents the density volume of the strata, in kg / m³. 3 g is the acceleration due to gravity, usually taken as 9.8 m / s². 2 .

[0026] Step S5. Combine the rock mechanical properties to obtain the maximum and minimum horizontal principal stress volumes. Formation lithology varies with depth, exhibiting different degrees of difference in its physical properties, mechanical characteristics, and pore pressure. The magnitude of the vertical in-situ stress is generally considered equal to the overlying formation pressure. Optionally, the horizontal in-situ stress calculation model in this invention uses the Huang Rongzun model as an example, and the specific calculation expression is as follows:

[0027]

[0028] Among them, Sh max (x,y,z), Sh min (x,y,z) represent the maximum and minimum horizontal principal stress bodies, respectively, in MPa; β1 and β2 are structural stress factors; α is the pore fluid pressure contribution coefficient.

[0029] Step S6. Combining the compressive strength prediction data obtained in Step S3, the rock cohesion and internal friction angle at any spatial point are calculated based on the first and second fitting equations. This yields the corresponding three-dimensional data volume C(x,y,z) of rock cohesion and the three-dimensional data volume of internal friction angle. Figure 6 A cross-sectional view of formation mechanical parameters at the well location is shown.

[0030] Step S7. Based on the obtained pore pressure volume, maximum horizontal principal stress volume, minimum horizontal principal stress volume, three-dimensional rock cohesion data volume, and three-dimensional internal friction angle data volume, and considering that whether the rock undergoes shear failure is mainly affected by the maximum and minimum principal stresses borne by the rock, the following collapse pressure calculation model can be substituted to calculate the three-dimensional collapse pressure data of the formation, realizing real-time prediction of the three-dimensional collapse pressure of the formation:

[0031]

[0032] Among them, B p (x,y,z) represents the three-dimensional collapse pressure volume data of the formation, in MPa; η is the stress regularization factor.

[0033] In the above expression, the parameter K is calculated using the following formula:

[0034]

[0035] In this invention, it is understood that the data such as longitudinal wave velocity volume, transverse wave velocity volume, density volume, rock mechanical property volume, pore pressure volume, maximum horizontal principal stress and minimum horizontal principal stress volume recorded above are three-dimensional spatial data, that is, the longitudinal wave velocity, transverse wave velocity, density, Young's modulus, Poisson's ratio, pore pressure, maximum horizontal principal stress and minimum horizontal principal stress corresponding to any spatial point in the work area, represent their three-dimensional distribution in three-dimensional space.

[0036] In the embodiments described in this invention, it is understood that vertical depth is the depth from the ground surface to the point to be predicted. Once the three-dimensional spatial region to be predicted is determined, the vertical coordinate of each spatial point is the vertical depth.

[0037] Based on the same inventive concept, another aspect of the present invention proposes a seismic prediction device for formation collapse pressure based on nonlinear variations in rock mechanics. The device is used to implement the aforementioned seismic prediction method for formation collapse pressure. Specifically, the device includes:

[0038] The relationship fitting module, based on triaxial mechanical experiments, obtains the compressive strength, cohesion, and internal friction angle of the core samples of the strata. The compressive strength is then nonlinearly fitted with the cohesion and internal friction angle to obtain the first and second fitting relationships.

[0039] The seismic inversion module obtains the P-wave velocity volume, S-wave velocity volume, and density volume through seismic inversion.

[0040] The rock mechanical property volume calculation module calculates the rock mechanical property volume based on the P-wave velocity volume, S-wave velocity volume, and density volume.

[0041] The overlying strata pressure volume calculation module calculates the overlying strata pressure volume and pore pressure volume based on the P-wave velocity volume and density volume.

[0042] The module for calculating the maximum and minimum horizontal principal stress bodies calculates the maximum and minimum horizontal principal stress bodies based on the rock mechanics properties.

[0043] The cohesion distribution and internal friction angle distribution prediction module, based on the rock mechanical property volume and combined with the first and second fitting formulas, obtains the three-dimensional data volume of rock cohesion and the three-dimensional data volume of internal friction angle in three-dimensional space.

[0044] The collapse pressure body prediction module predicts the three-dimensional collapse pressure body data of the formation in real time based on the pore pressure body, the maximum horizontal principal stress body, the minimum horizontal principal stress body, and the three-dimensional data body of rock cohesion and internal friction angle in three-dimensional space, combined with the collapse pressure calculation model.

[0045] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable in the processor, wherein when the processor executes the computer program, it implements the steps of the above-described method for predicting earthquakes caused by stratum collapse pressure.

[0046] The present invention further provides a computer-readable storage medium storing a computer program, which, when executed in a computer processor, implements the steps of the above-described method for predicting earthquakes caused by stratum collapse pressure.

[0047] The beneficial effects of this invention are:

[0048] 1. This invention proposes a seismic prediction method for formation collapse pressure based on nonlinear variations in rock mechanics. Compared to the traditional method based on the linear Mohr-Coulomb criterion, this method better reflects rock mechanics experimental phenomena. By fitting the nonlinear relationship between compressive strength, rock cohesion, and internal friction angle using triaxial mechanical experimental results from core samples, an improved collapse pressure calculation formula is obtained. Finally, seismic prediction data for collapse pressure is calculated, enabling more accurate pre-drilling prediction of formation collapse pressure using 3D seismic data and well logging data. This invention, through triaxial mechanical experimental results from core samples, can nonlinearly fit the compressive strength, cohesion, and internal friction angle of the rock, which better reflects actual rock mechanics experimental phenomena, thereby improving the accuracy of formation collapse pressure prediction. More accurate prediction of formation collapse pressure can reduce geological disasters caused by improper drilling operations, thus protecting the geological environment and reducing potential impacts on surface and groundwater resources.

[0049] 2. This invention not only considers the nonlinear changes in rock mechanical parameters but also achieves three-dimensional collapse pressure prediction, providing more comprehensive and detailed geological information for oil exploration and development. Accurate formation collapse pressure prediction helps optimize drilling design, reduce ineffective drilling and drilling accidents, thereby reducing drilling costs and improving the economic benefits of oil companies.

[0050] 3. This invention can be applied not only to the field of petroleum exploration and development, but its principles and technologies can also be extended to other engineering fields that require formation stability analysis, such as geological exploration and mining, with a wide range of applications. Attached Figure Description

[0051] The foregoing and hereinafter detailed description of the invention becomes clearer when read in conjunction with the following drawings, in which:

[0052] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0053] Figure 2 This is a structural diagram of the device of the present invention;

[0054] Figure 3 A three-dimensional spatial distribution map of compressive strength calculated from seismic inversion results, provided for embodiments of the present invention;

[0055] Figure 4 This is a nonlinear relationship diagram between compressive strength and cohesion provided in an embodiment of the present invention.

[0056] Figure 5 A nonlinear relationship diagram between compressive strength and internal friction angle provided in an embodiment of the present invention;

[0057] Figure 6The embodiments of the present invention provide a cross-sectional view of the rock cohesion and internal friction angle parameters at the well location;

[0058] Figure 7 Three-dimensional volume data for earthquake prediction of stratum collapse pressure based on nonlinear changes in rock mechanics, provided for embodiments of the present invention. Detailed Implementation

[0059] To enable those skilled in the art to better understand the technical solutions of this invention, several specific embodiments will be used to further illustrate the technical solutions for achieving the objectives of this invention. It should be noted that the technical solutions claimed by this invention include, but are not limited to, the following embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort should fall within the scope of protection of this invention.

[0060] As one of the most basic embodiments of the present invention, this embodiment first discloses a method for predicting earthquake collapse pressure based on nonlinear variations in rock mechanics, as detailed in the appendix to the specification. Figure 1 The method mainly includes the following steps:

[0061] Step S1. Conduct triaxial mechanical tests on the core sample and calculate the fitting equations for the undisturbed core mechanical parameters. The stress-strain curve of the rock can be obtained through the triaxial compression test, thus determining the rock's compressive strength. Based on this, the rock's cohesion and internal friction angle can be calculated. According to the Mohr-Coulomb criterion, the rock's cohesion and internal friction angle are not fixed but vary with the confining pressure. Considering the confining pressure effect: the rock's compressive strength increases under the influence of confining pressure.

[0062] This invention proposes a method for fitting and calculating a set of related equations based on the multiple compressive strengths and obtained mechanical parameters, including: fitting the rock's compressive strength and cohesion to obtain a first fitting relationship, and fitting the rock's compressive strength and internal friction angle to obtain a second fitting relationship. Combining these two fitting relationships, the following set of equations is obtained:

[0063]

[0064] Where C is the rock cohesion, and the unit is MPa; S is the internal friction angle of the rock, in degrees (°). c It represents the compressive strength of the rock.

[0065] Step S2. Obtain the P-wave velocity volume, S-wave velocity volume, and density volume through seismic inversion. Seismic inversion technology fully utilizes the rich structural, stratigraphic, and lithological information provided by well logging, drilling, and geological data to deduce information such as wave impedance, density, velocity, porosity, permeability, sandstone / mudstone percentage, and pressure of underground strata from conventional seismic profiles. Pre-stack sparse pulse inversion is used as an example.

[0066] First, partial stack data is generated from pre-stack angle gather seismic data. Pre-stack angle gather seismic data is a familiar type of angular domain reflected P-wave record formed after pre-stack depth migration. The angle refers to the incident angle, which is the angle between the incident P-wave and the normal to the top interface of the reservoir corresponding to the migration distance. The stacking method involves selecting several incident angle ranges for receiving reflected P-waves based on data characteristics, and then stacking the reflected P-waves within each selected range to form a partial stack seismic data volume. Next, pre-stack synthetic records and wavelet extraction are performed, a process familiar to practitioners. Then, a low-frequency initial model is established. Low-frequency components of well logging data within the work area are obtained through a low-pass filter, and then extrapolated to the entire three-dimensional space of the work area using a familiar spatial interpolation method to obtain a three-dimensional initial model of P-wave velocity, S-wave velocity, and density. Finally, an objective function is determined to measure the error between the synthetic record generated using the current model and the actual partial stack seismic data. By minimizing this sparse pulse deconvolution objective function, the P-wave velocity volume V at any spatial point (x,y,z) is obtained. p (x,y,z), transverse wave velocity body V s (x,y,z) and density volume ρ(x,y,z) form a corresponding three-dimensional spatial model (see schematic diagram). Figure 3 ).

[0067] Step S3. Using the longitudinal wave velocity V obtained in step S1 p (x,y,z), transverse wave velocity body V s The three-dimensional rock mechanical parameter volume is calculated using (x,y,z) and density volume ρ(x,y,z). The rock mechanical parameter volume required in this invention refers to the Young's modulus, Poisson's ratio, and compressive strength prediction volume of the rock, which reflect the mechanical properties exhibited by the rock under external pressure.

[0068] Young's modulus characterizes the physical property of a rock's tensile or compressive strength within its tolerance range. The specific calculation expression for the Young's modulus body of this invention is as follows:

[0069]

[0070] Where E(x,y,z) is Young's modulus, with units of GPa;

[0071] In this invention, the calculation expression for the Poisson's ratio is as follows:

[0072]

[0073] Where μ(x,y,z) is the Poisson's ratio at any point (x,y,z) in space, which is dimensionless;

[0074] In this invention, compressive strength represents the stress value of rock under uniaxial pressure when it fails as a whole, indirectly reflecting the formation fracture strength. It mainly has a certain statistical relationship with the rock's elastic modulus and clay content. The calculation expression for the compressive strength prediction body is as follows:

[0075] S c (x,y,z)=A×E(x,y,z)×(1-V sh )+B×E(x,y,z)×V sh (8);

[0076] Among them, S c (x, y, z) represents the compressive strength, measured in MPa; V sh The mud content is calculated based on empirical formulas familiar to practitioners combined with conventional logging data; A and B represent approximation coefficients for different lithologies. For example, J.M. Gatens and J. Hemingway estimated A to be approximately 0.0045 and B to be approximately 0.008 based on the characteristics of mudstone and shale formations.

[0077] Step S4. Combining the P-wave velocity volume and density three-dimensional attribute volume obtained in Step S2, the overlying strata pressure volume and pore pressure volume are predicted. Overlying strata pressure refers to the pressure exerted by the overlying strata on a formation due to gravity; it is a key data point in formation pressure prediction and is also called overlying pressure. The calculation formula for the overlying strata pressure volume is as follows:

[0078]

[0079] In the formula, S v (x,y,z) represents the gravity volume data of the overlying strata, in MPa; H represents the vertical depth at any spatial point, in meters; ρ(x,y,z) represents the density volume of the strata, in kg / m³. 3 g is the acceleration due to gravity, usually taken as 9.8 m / s². 2 ;

[0080] Pore ​​pressure refers to the pressure of the fluid contained in the pores of a formation. Normal pore pressure is equal to the hydrostatic pressure of the strata connected to the surface. Initial pore pressure is closely related to porosity, formation depth, and formation velocity. Taking the modified Fillippone method as an example, an empirical formula for predicting pore pressure directly using formation velocity is as follows:

[0081]

[0082] Among them, P p (x,y,z) represents the pore pressure volume; V i (x,y,z) represents the layer velocity at any spatial point in the i-th layer of the stratum, in m / s; V max (x,y,z) represents the longitudinal wave velocity of rock at any spatial point when the porosity is close to zero, in m / s; V min (x,y,z) represents the longitudinal wave velocity of rock at any spatial point when the rigidity is close to zero, in m / s.

[0083] Step S5. Prediction of the three-dimensional data volumes for the maximum and minimum horizontal principal stresses. Formation lithology varies with depth, exhibiting different degrees of difference in rock physical properties, mechanical characteristics, and pore pressure. The magnitude of in-situ stress in the vertical direction is generally considered equal to the overlying strata pressure; here, the Huang Rongzun model is used as an example for calculating in-situ stress in the horizontal direction. Combining the Poisson's ratio three-dimensional attribute volume μ(x,y,z) obtained in Step S3, and the overlying strata pressure three-dimensional data volume S obtained in Step S4... v (x,y,z) and the three-dimensional data volume P of formation pore fluid pressure p Substituting (x, y, z) into the following formula, the three-dimensional data volume of the maximum and minimum horizontal principal stresses can be calculated:

[0084]

[0085] In the formula, Sh max (x,y,z), Sh min (x,y,z) represent the three-dimensional data volumes of the maximum and minimum horizontal principal stresses, respectively, in MPa; β1 and β2 are tectonic stress factors, which can usually be inferred from the measured horizontal principal stresses in acoustic emission experiments or obtained from hydraulic fracturing experiments; α is the pore fluid pressure contribution coefficient, which is obtained from the rock strata properties using empirical methods.

[0086] Step S6. Combine the three-dimensional compressive strength prediction data volume S calculated in step S3. c (x,y,z), based on equation (1), the corresponding three-dimensional spatial rock cohesion three-dimensional data volume C(x,y,z) and rock internal friction angle three-dimensional data volume φ(x,y,z) can be estimated, such as Figure 6 The diagram shows a profile of formation mechanical parameters at the well location.

[0087] Step S7. Based on the obtained three-dimensional data volume of rock cohesion and rock internal friction angle, and using the collapse pressure calculation model, predict the three-dimensional collapse pressure volume as follows:

[0088] Combined with the three-dimensional volume data Sh of the maximum horizontal principal stress obtained in step S5max (x,y,z) and the three-dimensional volume data of the minimum horizontal principal stress Sh min (x,y,z), and the three-dimensional spatial rock cohesion three-dimensional data volume C(x,y,z) and rock internal friction angle three-dimensional data volume obtained in step S6. Based on the fact that whether rock undergoes shear failure is mainly affected by the maximum and minimum principal stresses borne by the rock, the three-dimensional collapse pressure prediction volume data is calculated by substituting these values ​​into the following formula:

[0089]

[0090] Among them, B p (x,y,z) represents the three-dimensional collapse pressure body of the formation; α is the pore fluid pressure contribution coefficient; η is the stress regularization factor, which is selected empirically relative to K. 2 The smallest value;

[0091]

[0092] Compared to the collapse pressure based on the linear Mohr-Coulomb criterion, Equation (5) removes the nonlinear factor and improves the constant cohesion and cohesion to the function of compressive strength. Figure 7 The results of the three-dimensional prediction volume data obtained based on the improved collapse pressure calculation formula are shown, according to the nonlinear relationship between the compressive strength, cohesion, and internal friction angle.

[0093] Based on the same inventive concept, another aspect of the embodiments of the present invention discloses an earthquake prediction device for formation collapse pressure based on nonlinear variations in rock mechanics. Since the principle by which this device solves the problem is similar to the earthquake prediction method for formation collapse pressure based on nonlinear variations in rock mechanics, the implementation of this device can be referred to the implementation of the method, and repeated details will not be elaborated further. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated. Figure 2 This is a schematic diagram of a device for evaluating the productivity of carbonate gas wells based on well logging data, provided in an embodiment of the present invention. Figure 2 As shown, the device may include: a relationship fitting module 201, a seismic inversion module 202, a rock mechanical property calculation module 203, an overlying stratum pressure calculation module 204, a maximum horizontal principal stress body and a minimum horizontal principal stress body calculation module 205, an internal cohesion distribution and internal friction angle distribution prediction module 206, and a collapse pressure body prediction module 207. The structure of the device will be described in detail below.

[0094] The relationship fitting module, based on triaxial mechanical experiments, obtains the compressive strength, cohesion, and internal friction angle of the core samples of the strata. The compressive strength is then nonlinearly fitted with the cohesion and internal friction angle to obtain the first and second fitting relationships.

[0095] The earthquake inversion module obtains the P-wave velocity volume, S-wave velocity volume, and density volume through earthquake inversion.

[0096] The rock mechanical property volume calculation module calculates the rock mechanical property volume based on the longitudinal wave velocity volume, the transverse wave velocity volume, and the density volume.

[0097] The overlying stratum pressure volume calculation module calculates the overlying stratum pressure volume and pore pressure volume based on the P-wave velocity volume and density volume.

[0098] The maximum and minimum horizontal principal stress bodies calculation module calculates the maximum and minimum horizontal principal stress bodies based on the rock mechanical properties.

[0099] The cohesion distribution and internal friction angle distribution prediction module obtains three-dimensional data volume of rock cohesion and three-dimensional data volume of internal friction angle in three-dimensional space based on the rock mechanical property volume and combined with the first fitting relationship and the second fitting relationship.

[0100] The collapse pressure body prediction module predicts the three-dimensional collapse pressure body data of the formation in real time based on the pore pressure body, the maximum horizontal principal stress body, the minimum horizontal principal stress body, and the three-dimensional data body of rock cohesion and internal friction angle in three-dimensional space, combined with the collapse pressure calculation model.

[0101] It should be noted that the systems, devices, models, or units described in the above embodiments can be implemented by computer chips or physical entities, or by products with certain functions. For ease of description, the above devices are described in this specification as various units based on their functions. Of course, in implementing this invention, the functions of each unit can be implemented in one or more software and / or hardware.

[0102] Furthermore, in this specification, adjectives such as first and second may only be used to distinguish an element or action, without necessarily implying any actual such relationship or order.

[0103] Furthermore, this embodiment also provides a computer device, which includes a processor, an input device, an output device, and a memory, all interconnected. The memory stores a computer program, which includes program instructions, and the processor is configured to invoke the program instructions to execute the steps described in the above embodiments.

[0104] Furthermore, another aspect of this embodiment provides a computer-readable storage medium, characterized in that: the computer-readable storage medium stores a computer program, the computer program including program instructions, which, when executed by a processor, cause the processor to perform the steps in the above embodiments.

[0105] In this embodiment, the processor can be a central processing unit (CPU). The processor can also be other general-purpose processors, 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, discrete hardware components, or combinations of the above types of chips.

[0106] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and units, such as the program units corresponding to the above-described method embodiments of the present invention. The processor executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions, and modules stored in the memory, thereby implementing the methods described in the above-described method embodiments.

[0107] The memory may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor, etc. Furthermore, the memory may include high-speed random access memory and non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, which can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0108] The one or more units are stored in the memory and, when executed by the processor, perform the methods described in the above embodiments.

[0109] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects.

[0110] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as an obstacle to the scope of protection of this invention.

[0111] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," and "connect" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0112] The above description is merely a preferred embodiment of the present invention and is not intended to hinder the present invention in any way. Any simple modifications or equivalent changes made to the above embodiments based on the technical essence of the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for predicting seismic collapse pressure in strata based on nonlinear variations in rock mechanics, characterized in that, The method includes the following steps: Step S1. Based on triaxial mechanical experiments, obtain the compressive strength, cohesion, and internal friction angle of the core samples of the strata. Perform nonlinear fitting between the compressive strength and the cohesion and internal friction angle respectively to obtain the first fitting relationship and the second fitting relationship. Step S2. Obtain the P-wave velocity volume, S-wave velocity volume, and density volume through seismic inversion; Step S3. Calculate the rock mechanical properties volume based on the P-wave velocity volume, S-wave velocity volume, and density volume; Step S4. Combine the P-wave velocity volume and density volume to obtain the overlying strata pressure volume and pore pressure volume; Step S5. Combine the rock mechanical properties to obtain the maximum and minimum horizontal principal stress volumes; Step S6. Based on the rock mechanical property volume, combined with the first fitting relationship and the second fitting relationship, obtain the three-dimensional data volume of rock cohesion and the three-dimensional data volume of internal friction angle in three-dimensional space; Step S7. Based on the pore pressure body, the maximum horizontal principal stress body, the minimum horizontal principal stress body, and the three-dimensional data body of rock cohesion and internal friction angle in three-dimensional space, the collapse pressure calculation model is used to make real-time predictions of the three-dimensional collapse pressure body data of the formation.

2. The earthquake prediction method for formation collapse pressure based on nonlinear variations in rock mechanics according to claim 1, characterized in that, In step S1, the first and second fitting equations are as follows: Where C is the rock cohesion; φ is the rock internal friction angle; S c It represents the compressive strength of the rock.

3. The earthquake prediction method for formation collapse pressure based on nonlinear variations in rock mechanics according to claim 1, characterized in that, In step S3, the rock mechanical property volume includes Young's elastic modulus volume, Poisson's ratio volume, and compressive strength prediction volume.

4. The earthquake prediction method for formation collapse pressure based on nonlinear variations in rock mechanics according to claim 1, characterized in that, In step S4, the calculation expression for the overlying strata pressure body is as follows: Among them, S v (x,y,z) represents the pressure volume of the overlying strata; H represents the vertical depth at any spatial point; ρ(x,y,z) represents the density volume of the strata; and g represents the acceleration due to gravity.

5. The earthquake prediction method for formation collapse pressure based on nonlinear variations in rock mechanics according to claim 1, characterized in that, In step S4, the calculation expression for the pore pressure body is as follows: Among them, P p (x,y,z) represents the pore pressure volume; V i (x,y,z) represents the layer velocity at any spatial point in the i-th layer of the stratum; V max (x,y,z) represents the longitudinal wave velocity of rock at any spatial point when the porosity is close to zero; V min (x,y,z) represents the longitudinal wave velocity of the rock when its rigidity is close to zero at any spatial point; Sv(x,y,z) represents the pressure body of the overlying strata.

6. The earthquake prediction method for formation collapse pressure based on nonlinear variations in rock mechanics according to claim 1, characterized in that, In step S5, the calculation expressions for the maximum and minimum horizontal principal stress bodies are as follows: Among them, Sh max (x,y,z), Sh min (x,y,z) represent the maximum and minimum horizontal principal stress volumes, respectively; μ(x,y,z) is the formation Poisson's ratio; β1 and β2 are tectonic stress factors; S v (x,y,z), P P (x,y,z) represent the overlying formation pressure volume and the formation pore fluid pressure volume, respectively; α is the pore fluid pressure contribution coefficient.

7. The earthquake prediction method for formation collapse pressure based on nonlinear variations in rock mechanics according to claim 1, characterized in that, In step S6, the collapse pressure calculation model is as follows: Among them, B p (x,y,z) represents the three-dimensional collapse pressure body of the strata; Sh max (x,y,z) represents the body with the maximum horizontal principal stress; Sh min (x,y,z) represents the minimum horizontal principal stress volume; C(x,y,z) represents the three-dimensional data volume of rock cohesion in three-dimensional space; α represents the pore fluid pressure contribution coefficient; η represents the stress regularization factor. This is a three-dimensional data volume of the internal friction angle of rocks in three-dimensional space.

8. An earthquake prediction device for formation collapse pressure based on nonlinear variations in rock mechanics, the device being used to implement the prediction method described in any one of claims 1-7, characterized in that, include: The relationship fitting module, based on triaxial mechanical experiments, obtains the compressive strength, cohesion, and internal friction angle of the core samples of the strata. The compressive strength is then nonlinearly fitted with the cohesion and internal friction angle to obtain the first and second fitting relationships. The seismic inversion module obtains the P-wave velocity volume, S-wave velocity volume, and density volume through seismic inversion. The rock mechanical property volume calculation module calculates the rock mechanical property volume based on the P-wave velocity volume, S-wave velocity volume, and density volume. The overlying strata pressure volume calculation module calculates the overlying strata pressure volume and pore pressure volume based on the P-wave velocity volume and density volume. The module for calculating the maximum and minimum horizontal principal stress bodies calculates the maximum and minimum horizontal principal stress bodies based on the rock mechanics properties. The cohesion distribution and internal friction angle distribution prediction module, based on the rock mechanical property volume and combined with the first and second fitting formulas, obtains the three-dimensional data volume of rock cohesion and the three-dimensional data volume of internal friction angle in three-dimensional space. The collapse pressure body prediction module predicts the three-dimensional collapse pressure body data of the formation in real time based on the pore pressure body, the maximum horizontal principal stress body, the minimum horizontal principal stress body, and the three-dimensional data body of rock cohesion and internal friction angle in three-dimensional space, combined with the collapse pressure calculation model.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable in the processor, wherein the processor, when executing the computer program, implements the prediction method according to any one of claims 1-7.

10. A computer-readable storage medium storing a computer program that, when executed in a computer processor, implements the prediction method according to any one of claims 1-7.

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