Construction method, device and application of three-dimensional rock mechanical model

Abstract

The invention discloses a construction method and device of a three-dimensional rock mechanics model and application of the three-dimensional rock mechanics model. The method comprises the steps that rock mechanics attribute field data of a single well in a target oil reservoir are obtained; correcting the rock mechanical property field of the single well; constructing a one-dimensional rock mechanics model based on the corrected rock mechanics attribute field data of the single well; and performing longitudinal discretization processing on the one-dimensional rock mechanical model to construct a three-dimensional rock mechanical model. The rock mechanics modeling method is combined with rock mechanics indoor experiments and fractured well ground stress parameters, and Young modulus and Poisson's ratio dynamic and static data conversion and difference correction of various mechanical parameters are carried out in the modeling process. Quality control of various parameters in the modeling process is achieved, and the precision of the rock mechanical model is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of oilfield development, and in particular to a method, device and application of constructing a three-dimensional rock mechanics model. Background Art

[0002] As conventional oil and gas reservoir development enters the middle and late stages, the scale of utilization of unconventional oil and gas, such as tight oil reservoirs, is gradually expanding. Horizontal well fracturing technology is currently the primary means of developing these reservoirs. With the recent trend toward deeper formations in tight unconventional oil and gas reservoir development, there is an urgent need to address challenges such as efficient horizontal well fracturing and reservoir-wide integrated fracturing deployment. Essentially, this involves predicting fracture morphology, or in other words, developing reservoir rock mechanics models.

[0003] Currently, most fracturing optimization methods are based on one-dimensional rock mechanics models. This type of method has advantages such as evaluating rate blocks and providing timely field guidance. However, this type of rock mechanics parameter field modeling method is mainly used to guide single-well fracturing scale design and combines logging shear wave interpretation with laboratory testing. However, it has the following shortcomings:

[0004] At present, most of the shear wave prediction methods use empirical formulas (see Equation 1). However, this method does not take into account the differences of specific reservoirs, nor does it take into account the influence of multiple reservoir parameters on shear wave time difference prediction. Therefore, using the shear wave prediction obtained by this method for rock mechanics calculations will lead to unreasonable subsequent parameters (such as Figure 1 ).

[0005]

[0006] Where Δt s —Shear wave time difference, us / ft; Δt p —P-wave time difference, us / ft; ρ—rock skeleton density, g / cm 3 ; e—natural logarithm, 2.71828.

[0007] The actual well temperature profile test results and the existing minimum horizontal principal stress calculation results show that there is a stress shielding layer distributed at the top of the B1 layer, that is, a high stress value. However, the shear wave data calculated using the empirical formula and the minimum stress value calculated therefrom do not have a stress shielding layer distribution. Therefore, the calculation parameter deviation of the empirical formula method is relatively large.

[0008] 3. As the proportion of reservoirs requiring reservoir stimulation increases in commercial oil and gas development, especially overseas fields, and their size grows, the demands for productivity and economic benefits are also increasing. Fracturing optimization primarily focused on increasing operating speed is no longer sufficient to meet current reservoir stimulation requirements. It requires a comprehensive approach to reservoir fracturing deployment. Therefore, using traditional fracturing optimization methods will inevitably compromise the effectiveness of reservoir stimulation. Summary of the Invention

[0009] The purpose of the present invention is to provide a method, device and application for constructing a three-dimensional rock mechanics model, which can effectively integrate and utilize rock mechanics parameter data, achieve full coverage of rock mechanics parameters in the model of the study area, and provide technical support for the overall fracturing deployment of the reservoir. This solves the problem that the existing technology is limited to static description and cannot achieve dynamic and static integration. During the modeling process, the test parameters of the fractured well are used to perform quality control of various mechanical parameters, thereby improving the accuracy of the rock mechanics model. To achieve the above purpose, the present invention provides the following technical solutions:

[0010] The present invention provides a method for constructing a three-dimensional rock mechanics model, the method comprising:

[0011] Obtain rock mechanical property field data for a single well in the target reservoir;

[0012] Correcting the rock mechanical property field of the single well;

[0013] Based on the calibrated rock mechanical property field data of a single well, a one-dimensional rock mechanical model is constructed;

[0014] The one-dimensional rock mechanics model is vertically discretized to construct a three-dimensional rock mechanics model.

[0015] Furthermore, the rock mechanics property field data includes: well logging interpretation curve parameters, rock mechanics parameters and fracturing well monitoring data;

[0016] The logging interpretation curve parameters include: shear wave and compression wave time difference, natural gamma, mud content, and physical property interpretation results;

[0017] The rock mechanics parameters include: indoor experimental parameters and rock mechanics experimental parameters, wherein,

[0018] The indoor experimental parameters include triaxial stress test static and dynamic Young's modulus, Poisson's ratio, ground stress magnitude and direction, and compressive / tensile strength; the typical rock mechanics experimental parameters include acoustic wave characteristic experimental parameters and Kaiser acoustic emission parameters;

[0019] The monitoring data of the fracturing well includes: fracture pressure, ground stress, fracturing operation curve, fracture morphology prediction and post-fracturing productivity curve.

[0020] Furthermore, the rock mechanical property field of the single well is corrected, including:

[0021] The Young's modulus, Poisson's ratio, maximum horizontal stress, minimum horizontal stress, fracture pressure, vertical stress, fracture toughness, shear modulus, confining pressure, pore pressure gradient and vertical stress gradient in the rock mechanical property field of the single well are corrected.

[0022] Furthermore, the correction of the rock mechanical property field of the single well includes:

[0023] Eliminating abnormal values ​​in the logging interpretation curve parameters to obtain the P-wave and S-wave data of a single well;

[0024] Based on the longitudinal wave and shear wave data, the dynamic Young's modulus and dynamic Poisson's ratio are obtained;

[0025] Calibrate the dynamic Young's modulus and dynamic Poisson's ratio through acoustic wave characteristic experimental parameters;

[0026] Performing dynamic-static data conversion on the calibrated dynamic Young's modulus and dynamic Poisson's ratio to obtain a first static Young's modulus and a first static Poisson's ratio;

[0027] When the difference between the values ​​of the first static Young's modulus and the first static Poisson's ratio and the values ​​of the second static Young's modulus and the second static Poisson's ratio obtained from the triaxial stress static experimental test is less than or equal to a threshold, the correction of the first static Young's modulus and the first static Poisson's ratio is completed.

[0028] Furthermore, the correction of the rock mechanical property field of the single well further includes:

[0029] Obtain Kaiser acoustic emission experimental data for multiple samples from a single well;

[0030] Substituting the Kaiser acoustic emission experimental data into the maximum and minimum horizontal principal stress calculation formulas of the reservoir to calculate the corresponding maximum horizontal stress and minimum horizontal stress;

[0031] The average values ​​of the maximum horizontal stress and the minimum horizontal stress of all samples are calculated. The average value of the maximum horizontal stress is the corrected maximum horizontal stress, and the average value of the minimum horizontal stress is the corrected minimum horizontal stress.

[0032] Furthermore, the maximum and minimum horizontal principal stresses of the reservoir are calculated as follows:

[0033]

[0034]

[0035] Where σ H represents the maximum horizontal stress, σ h represents the minimum horizontal stress, μ s Represents the static Poisson's ratio, a decimal; σ v represents the vertical principal stress, P p represents the reservoir pore pressure, α represents the Biot elastic coefficient, and β1 and β2 represent the horizontal tectonic stress coefficients.

[0036] Furthermore, the correction of the rock mechanical property field of the single well further includes:

[0037] The burst pressure is calculated using the burst pressure theory formula;

[0038] The burst pressure is corrected using on-site construction parameters.

[0039] Furthermore, the theoretical formula of the burst pressure is expressed as follows:

[0040] P f =3σ h -σ H -αP p +S t

[0041] S t =S c / c

[0042] S c =(0.0045E D (1-V sh )+0.008E D V sh )×1000d

[0043] Where, P f Indicates the burst pressure; P p represents the reservoir pore pressure; S t represents tensile strength; c represents proportional coefficient; S c Indicates compressive strength; V sh represents the mud content; d represents the correction coefficient, which is 1.2.

[0044] Furthermore, the rock mechanical property field of the single well is corrected, further comprising:

[0045] The correction formulas for vertical stress, fracture toughness, shear modulus, confining pressure, pore pressure gradient, and vertical stress gradient are as follows:

[0046] Vertical stress:

[0047]

[0048] Fracture toughness:

[0049] P frac_T =0.217P c +0.0059S t 3 +0.0923S t 2 +0.517S t -0.3322

[0050] Confining pressure:

[0051]

[0052] Shear modulus:

[0053]

[0054] Pore ​​pressure gradient, vertical stress gradient:

[0055] Pp_g=Pp / TVD Pz_g=σz / TVD

[0056] Among them, σ z represents vertical stress; ρ r (h) represents the density of the overlying rock that changes with depth; H represents the depth of the formation; g represents the acceleration of gravity; P frac_T represents fracture toughness; P c represents the confining pressure; μ S represents the static Poisson's ratio; G represents the shear modulus; P p_g , P z_g Represents pore pressure gradient and vertical stress gradient.

[0057] Furthermore, based on the corrected rock mechanical property field data of a single well, a one-dimensional rock mechanical model is constructed, including:

[0058] Based on the corrected rock mechanical property field data of a single well, predict the rock mechanical property field data of other wells in the target reservoir;

[0059] A one-dimensional rock mechanics model is constructed based on rock mechanics property field data of other wells in the target oil reservoir.

[0060] Furthermore, the rock mechanical property field data of other wells in the target reservoir are predicted, including:

[0061] Based on the corrected shear wave data of a single well and combined with a multi-factor neural network, the shear wave data of other wells are predicted.

[0062] Furthermore, the rock mechanical property field data of other wells in the target reservoir are predicted, and further include:

[0063] Based on the corrected maximum horizontal stress and minimum horizontal stress, the maximum horizontal stress and minimum horizontal stress of other wells are predicted.

[0064] Furthermore, the rock mechanical property field data of other wells in the target reservoir are predicted, and further include:

[0065] Based on the corrected vertical stress, fracture toughness, shear modulus, confining pressure, pore pressure gradient and vertical stress gradient, the vertical stress, fracture toughness, shear modulus, confining pressure, pore pressure gradient and vertical stress gradient of other wells in the target reservoir are predicted.

[0066] Furthermore, a three-dimensional rock mechanics model is constructed for the one-dimensional rock mechanics model, including:

[0067] performing vertical discretization processing on rock mechanical property field data of other wells in the target oil reservoir in the one-dimensional rock mechanical model;

[0068] The rock mechanical property field data of other wells in the vertically discretized target reservoir are predicted in plane to construct a three-dimensional rock mechanical model.

[0069] The present invention also provides a device for constructing a three-dimensional rock mechanics model, the device comprising:

[0070] An acquisition module is used to obtain rock mechanical property field data of a single well in a target reservoir;

[0071] a correction module, configured to correct the rock mechanical property field of the single well;

[0072] The first construction module is used to construct a one-dimensional rock mechanics model based on the corrected rock mechanics property field data of the single well;

[0073] The second construction module is used to perform longitudinal discretization processing on the one-dimensional rock mechanics model to construct a three-dimensional rock mechanics model.

[0074] The present invention also provides an application of the above-mentioned method for constructing a three-dimensional rock mechanics model in improving the accuracy of tight oil reservoir rock mechanics property field modeling.

[0075] Technical effects and advantages of the present invention:

[0076] ① The rock mechanics modeling method relies on three main data sources: well logging interpretations from wells penetrating the target layer; laboratory rock mechanics experiments, including triaxial stress testing, acoustic wave characterization experiments, and Kaiser acoustic emission tests; and fracturing operation curves, fracture morphology predictions, and post-fracturing productivity curves. This extensive and comprehensive data set provides the foundation for the data integrity of the rock mechanics model.

[0077] ② This rock mechanics modeling method considers the diverse geological characteristics of specific reservoirs and the influence of multiple reservoir parameters on shear wave moveout prediction. Using existing shear wave wells as templates, a multi-factor neural network is used to predict shear wave data from other wells, improving shear wave prediction accuracy and achieving full coverage of rock mechanics parameters in the model for the study area.

[0078] ③ This rock mechanics modeling method, combined with laboratory rock mechanics experiments and in-situ stress parameters from fractured wells, performs dynamic and static data conversion of Young's modulus and Poisson's ratio during the modeling process, and corrects for differences in various mechanical parameters. This ensures quality control of various parameters during the modeling process, improving the accuracy of the rock mechanics model.

[0079] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 The shear wave prediction data and related rock mechanics calculation results obtained by the empirical formula method in the prior art;

[0081] Figure 2 This is a flow chart of a method for constructing a three-dimensional rock mechanics model in an embodiment of the present invention;

[0082] Figure 3 This is a comparison chart of the dynamic Young's modulus and Poisson's ratio calculation results and dynamic and static data of Well S3 in an embodiment of the present invention;

[0083] Figure 4a This is a static-dynamic conversion diagram of the Young's modulus of the S3 well in an embodiment of the present invention;

[0084] Figure 4b This is a static-dynamic conversion diagram of the Poisson's ratio of the S3 well in an embodiment of the present invention;

[0085] Figure 5 The static Young's modulus and Poisson's ratio of Well S3 in the embodiment of the present invention are compared with the experimental static data;

[0086] Figure 6a The basic process of shear wave prediction based on the neural network model in the embodiment of the present invention is as follows: Figure 1 ;

[0087] Figure 6b The basic process of shear wave prediction based on the neural network model in the embodiment of the present invention is as follows: Figure 2 ;

[0088] Figure 6c The basic process of shear wave prediction based on the neural network model in the embodiment of the present invention is as follows: Figure 3 ;

[0089] Figure 7 Comparison between the minimum horizontal principal stresses calculated and tested for the S5 well fracturing in the embodiment of the present invention;

[0090] Figure 8The correction result of the fracture pressure and fracturing monitoring data of the S5 well in the embodiment of the present invention is shown;

[0091] Figure 9 This is the three-dimensional prediction result of Young's modulus, a typical mechanical parameter of the target layer in the embodiment of the present invention. DETAILED DESCRIPTION

[0092] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0093] In order to solve the shortcomings of the existing technology, the present invention discloses a method for constructing a three-dimensional rock mechanics model, such as Figure 2 As shown, the method includes step 1: obtaining rock mechanical property field data of a single well in a target oil reservoir; step 2: correcting the rock mechanical property field of the single well; step 3: constructing a one-dimensional rock mechanical model based on the corrected rock mechanical property field data of the single well; step 4: performing longitudinal discretization processing on the one-dimensional rock mechanical model to construct a three-dimensional rock mechanical model.

[0094] Step 1: Obtain rock mechanical property field data for a single well in the target reservoir. Specifically, collect and organize logging interpretation curves for all interlayer wells in the target reservoir, existing rock mechanical parameter interpretation results, and monitoring data from existing fracturing wells in the target layer. Well logging interpretation curve parameters include: S-wave and P-wave time difference, natural gamma, shale content, and physical property interpretation results. Rock mechanical parameters include: static and dynamic Young's modulus, Poisson's ratio, in-situ stress magnitude and direction, compressive / tensile strength, and other laboratory parameters from triaxial stress testing, as well as typical rock mechanical experiments such as acoustic wave characterization experiments and Kaiser acoustic emission. Fracturing well monitoring data includes: fracture pressure, in-situ stress magnitude, fracturing operation curves, fracture morphology predictions, and post-fracturing productivity curves derived from fracturing wells.

[0095] Step 2: Correcting the rock mechanical property field of the single well, including correcting the Young's modulus, Poisson's ratio, maximum horizontal stress, minimum horizontal stress, fracture pressure, vertical stress, fracture toughness, shear modulus, confining pressure, pore pressure gradient and vertical stress gradient in the rock mechanical property field of the single well.

[0096] In a specific embodiment of the present invention, correcting the rock mechanical property field of the single well further includes: removing abnormal values ​​in the logging interpretation curve parameters to obtain the P-wave and S-wave data of the single well;

[0097] Based on the longitudinal wave and shear wave data, the dynamic Young's modulus and dynamic Poisson's ratio are obtained;

[0098] Calibrate the dynamic Young's modulus and dynamic Poisson's ratio through acoustic wave characteristic experimental parameters;

[0099] Performing dynamic-static data conversion on the calibrated dynamic Young's modulus and dynamic Poisson's ratio to obtain a first static Young's modulus and a first static Poisson's ratio;

[0100] When the difference between the values ​​of the first static Young's modulus and the first static Poisson's ratio and the values ​​of the second static Young's modulus and the second static Poisson's ratio obtained from the triaxial stress static experimental test is less than or equal to a threshold, the correction of the first static Young's modulus and the first static Poisson's ratio is completed.

[0101] In one specific embodiment of the present invention, outliers in the well logging curve are removed, parameter units are adjusted, and target intervals are selected for shear wave prediction. Shear wave data is the basis for calculating rock mechanical parameters. Combined with P-wave transit time, it can directly derive fundamental rock mechanical parameters such as Young's modulus and Poisson's ratio. Parameters such as Young's modulus, Poisson's ratio, and in situ stress are then calibrated using existing indoor triaxial testing to ensure the accuracy of the calculated parameters. Therefore, the quantity and quality of shear waves will directly impact subsequent work.

[0102] In a specific embodiment of the present invention, the dynamic Young's modulus and dynamic Poisson's ratio (Equations 2 and 3) are obtained based on the longitudinal wave and shear wave data, and are calibrated by the acoustic wave characteristic experimental data (including time difference, natural gamma, mud content, rock skeleton density, etc.) (e.g. Figure 3 ).

[0103]

[0104]

[0105] Where, E d represents dynamic Young's modulus, GPa; μ d Represents the dynamic Poisson's ratio, a decimal.

[0106] Depend on Figure 3 It can be seen that the calculated values ​​of the dynamic Young's modulus and dynamic Poisson's ratio of Well S3 are slightly different from the laboratory data, that is, the calculated values ​​of the sonic logging curve meet the subsequent rock mechanics parameter calculation standards.

[0107] The Young's modulus and Poisson's ratio calculated from the acoustic logging curve are dynamic values, while the rock mechanics parameters used in subsequent fracturing simulations need to be static values. Therefore, it is necessary to combine the experimental data for dynamic and static data conversion, that is, to regress the static and dynamic experimental data of the same sample or samples within a certain depth range. The static data comes from the triaxial stress test results, and the dynamic data comes from the acoustic characteristic test results, such as Figure 4a and 4b .Depend on Figure 4a and 4b The relationship between the static and dynamic conversion of Young's modulus and Poisson's ratio is obtained by converting the parameters measured by the acoustic logging to obtain static parameters, which are then compared with the results of the triaxial stress static test (such as Figure 5 ), the errors between the two are small, which meets the requirements for rock mechanics parameter calculation.

[0108] In a specific embodiment of the present invention, the correction of the rock mechanical property field of the single well further includes: obtaining Kaiser acoustic emission test data of multiple samples of the single well, the Kaiser acoustic emission test data including stress gradients: vertical, horizontal maximum, and horizontal minimum; as well as vertical stress values, horizontal maximum stress values, and horizontal minimum stress values;

[0109] The Kaiser acoustic emission experimental data is substituted into the maximum and minimum horizontal principal stress calculation formulas of the reservoir to obtain the corresponding maximum horizontal stress and minimum horizontal stress; the average value of the maximum horizontal stress and the minimum horizontal stress of all samples is calculated, and the average value of the maximum horizontal stress is the corrected maximum horizontal stress, and the average value of the minimum horizontal stress is the corrected minimum horizontal stress.

[0110] The in-situ stress in oil and gas reservoir rocks is primarily a coupled force of gravity, tectonic, pore, and thermal stresses, and varies with each coupled stress, representing a variable. In-situ stress exhibits a three-dimensional distribution, with one in-situ stress (principal stress) occurring on each of the x, y, and z axes. These three stresses are mutually perpendicular and unequal, with the vertical principal stress being the vertical principal stress. There are two principal stresses in the horizontal plane, representing the maximum and minimum principal stresses. The commonly used "six-five model" is used to calculate the maximum and minimum horizontal principal stresses in the reservoir, as shown in Equations 4 and 5.

[0111]

[0112]

[0113] Where σ H Indicates the maximum horizontal stress, MPa; σ h Indicates the minimum horizontal stress, MPa; μ s Represents the static Poisson's ratio, a decimal; σ vrepresents the vertical principal stress, MPa; P p represents the reservoir pore pressure, MPa; α represents the Biot elastic coefficient, a decimal, and its theoretical value is "1-porosity", but it needs to be adjusted during fitting; β1, β2 represent the horizontal tectonic stress coefficient, a decimal.

[0114] In a specific embodiment of the present invention, the correction of the rock mechanical property field of the single well further includes: calculating the fracture pressure using a fracture pressure theoretical formula; and correcting the fracture pressure using on-site construction parameters.

[0115] Formation fracture pressure is defined as the bottomhole fluid pressure at which hydraulic fractures are generated in the formation. This pressure is related to factors such as the elastic properties of the rock, pore pressure, the development of natural fractures, and the local in-situ stress. The ratio of the formation fracture pressure to the depth of the formation is called the fracture pressure gradient. Formation fracture pressure and its gradient can be predicted or determined using methods such as isomorphic theoretical equations, well logging analysis, field operation parameter calculations, and statistical analysis. This study used the following theoretical formula to calculate the fracture pressure and calibrated it using field operation parameters to achieve a reasonable value.

[0116] The theoretical formula of burst pressure is as follows:

[0117] P f =3σ h -σ H -αP p +S t (6)

[0118] S t =S c / c (7)

[0119] S c =(0.0045E D (1-V sh )+0.008E D V sh )×1000d (8)

[0120] Where, Pf represents the burst pressure, MPa; P p Represents reservoir pore pressure, MPa; S t represents tensile strength, MPa; c represents proportional coefficient, 2-20, here it is 7; S c Indicates compressive strength, MPa; V sh represents the mud content, a decimal; d represents the correction coefficient, which is 1.2.

[0121] In a specific embodiment of the present invention, the correction of the rock mechanical property field of the single well also includes: correction of vertical stress, fracture toughness, shear modulus, confining pressure, pore pressure gradient and vertical stress gradient.

[0122] Based on the calculation of the above rock mechanics parameters, the vertical stress, fracture toughness, shear modulus, confining pressure, pore pressure gradient, vertical stress gradient and other indicators are calculated. Among them, the vertical stress is approximately equal to the gravity of the overlying rock; fracture toughness is a measure of the resistance to crack expansion. First, the stress intensity factor of the crack is introduced. This factor refers to the stress magnitude near the crack segment and depends on the dimensional storage of the crack and the surrounding cutoff and the applied load (i.e., the fluid pressure in the crack and its closure pressure). According to the energy conditions, the internal pressure will induce a stress intensity factor at a certain point on the edge of the crack. When this factor is greater than the fracture toughness of the rock, the crack will expand forward. In addition, fracture toughness is divided into three types: opening type, sliding type and tearing type. The type designed in this study is the opening type, which is the typical type used in the process of hydraulic fracturing to generate cracks; the shear modulus is the ratio of shear stress to shear strain of the material under the action of shear stress within the proportional limit of elastic deformation. It is similar to Young's modulus and is a type of elastic modulus. The corresponding calculation model (correction formula) is as follows:

[0123] Vertical stress:

[0124]

[0125] Fracture toughness:

[0126]

[0127] Confining pressure:

[0128]

[0129] Shear modulus:

[0130]

[0131] Pore ​​pressure gradient, vertical stress gradient:

[0132] P p_g =P p / TVD P z_g =σ z / TVD (13)

[0133] Among them, σ z represents vertical stress, MPa; ρ r (h) represents the density of the overlying rock that varies with depth, kg / m 3 ; H represents the depth of the formation, m; g represents the acceleration of gravity, m / s2 ;P frac_T represents the fracture toughness, This refers to type I fracture; P c Indicates confining pressure, MPa; μ S represents the static Poisson's ratio, a decimal; G represents the shear modulus, GPa; P p_g , P z_g Represents pore pressure gradient and vertical stress gradient, MPa / m.

[0134] Step 3: Based on the corrected rock mechanical property field data of the single well, a one-dimensional rock mechanical model is constructed, including: predicting the rock mechanical property field data of other wells in the target reservoir based on the corrected rock mechanical property field data of the single well; and constructing a one-dimensional rock mechanical model based on the rock mechanical property field data of other wells in the target reservoir.

[0135] Step 301: Based on the corrected shear wave data of a single well combined with a multi-factor neural network, predict the shear wave data of other wells; Step 302: Based on the corrected maximum horizontal stress and minimum horizontal stress, predict the maximum horizontal stress and minimum horizontal stress of other wells; Step 303: Based on the corrected vertical stress, fracture toughness, shear modulus, confining pressure, pore pressure gradient and vertical stress gradient, predict the vertical stress, fracture toughness, shear modulus, confining pressure, pore pressure gradient and vertical stress gradient of other wells in the target reservoir.

[0136] For step 301: Reference Figures 6a-6c This application fully considers the geological characteristics of the target reservoir, using existing shear wave wells as templates to predict shear wave data from other wells through a multi-factor neural network. By analyzing and quantifying the changing patterns of multiple factors influencing shear wave time difference, the corresponding shear wave time difference is constructed through fitting. Based on conventional experience, four indicators—short wave time difference, natural gamma, compressional wave time difference, and shale content—are used as the influencing factor set for a single well.

[0137] Multi-well data training is conducted based on all single wells with complete four indicators, including shear wave time difference, natural gamma, compressional wave time difference, and mud content. That is, a nonlinear relationship between the three indicators, including natural gamma, compressional wave time difference, and mud content, and shear wave time difference is established to prepare for the next fitting test.

[0138] Based on the established relationship between multiple indicators and shear wave time difference, the shear wave data of existing wells are fitted. By adjusting the parameters of the constructed neural network, the shear wave data predicted by the model are made to match the actual shear wave data as much as possible. Finally, a neural network model that meets the requirements is obtained to predict the shear wave data of other wells.

[0139] The fitted neural network model was used to predict shear wave data from other wells. Three indicators—natural gamma, P-wave time difference, and shale content—were used as input data. The neural network model then calculated the shear wave data for the well. Based on the distribution of unknown wells, shear wave data from more wells was predicted, ensuring comprehensive coverage of the target reservoir and laying the foundation for the precise development of the rock mechanics model.

[0140] For step 302: Horizontal Tectonic Stress Coefficient, existing Kaiser acoustic emission test data was substituted into the above two equations for backcalculation. The backcalculated stress coefficients for different samples were arithmetic averaged to obtain stress coefficients used to predict the maximum and minimum horizontal stresses in other wells. Using Kaiser acoustic emission test data for two samples from Well S5 (see Table 1), the parameters in Table 1 were backcalculated based on Equations 4 and 5. Reservoir parameters such as pore pressure were determined based on reservoir physical properties, resulting in final stress coefficients of 0.2604 and 0.4154, respectively.

[0141] Table 1 Kaiser acoustic emission experimental data

[0142]

[0143] Based on Equations 4 and 5, the maximum and minimum horizontal principal stresses of other wells are predicted, and the principal stresses obtained from the fracturing test of the existing fracturing well S5 are used for correction, as shown in the following example: Figure 7 Since the longitudinal difference of the stress curve has a direct impact on the control of the fracture height, the longitudinal difference is also corrected.

[0144] Step 4: performing longitudinal discretization processing on the one-dimensional rock mechanics model to construct a three-dimensional rock mechanics model, including: performing longitudinal discretization processing on the rock mechanics property field data of other wells in the target oil reservoir in the one-dimensional rock mechanics model; performing planar prediction on the rock mechanics property field data of other wells in the longitudinally discretized target oil reservoir to construct a three-dimensional rock mechanics model.

[0145] Constructing a three-dimensional rock mechanics model Based on the above typical mechanical parameter calculation model, combined with the drilling platform position, the one-dimensional rock mechanics parameters of other wells are predicted to cover the target area to the greatest extent. Then, the arithmetic average method is used to perform longitudinal discretization on the predicted rock mechanics parameters, and a three-dimensional rock mechanics model is established through plane prediction. Figure 9 The color depth of the three-dimensional distribution diagram of Young's modulus can more realistically reflect the rock mechanical properties of the well.

[0146] The present invention also provides a device for constructing a three-dimensional rock mechanics model, the device comprising:

[0147] An acquisition module is used to obtain rock mechanical property field data of a single well in a target reservoir;

[0148] a correction module, configured to correct the rock mechanical property field of the single well;

[0149] The first construction module is used to construct a one-dimensional rock mechanics model based on the corrected rock mechanics property field data of the single well;

[0150] The second construction module is used to perform longitudinal discretization processing on the one-dimensional rock mechanics model to construct a three-dimensional rock mechanics model.

[0151] The present invention also provides an application of the three-dimensional rock mechanics model construction method in improving the modeling accuracy of tight oil reservoir rock mechanics property fields.

[0152] The method of the present invention has high data utilization, convenient operation, accurate simulation results, and takes into account the differences in geological characteristics of the reservoir and the influence of multiple reservoir parameters on the shear wave time difference prediction. Using existing shear wave wells as templates, the shear wave data of other wells are predicted through a multi-factor neural network, which improves the accuracy of shear wave prediction and achieves full coverage of the rock mechanics parameters of the model in the study area. The technical solution of the present invention can realistically simulate the distribution of fracture morphology during the fracturing process of tight oil reservoirs, solve the inter-well interference caused by unreasonable well location deployment, and guide the efficient and economical overall deployment of the entire reservoir. Through late energy replenishment, the rock mechanics model can still effectively consider the impact of horizontal wells in multiple fracture situations and fracture morphology changes under various injection and production modes under different development methods on the development effect of tight oil reservoirs.

[0153] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for constructing a three-dimensional rock mechanics model, characterized in that: The method comprises, Obtain rock mechanical property field data for a single well in the target reservoir; Correcting the rock mechanical property field of the single well; Based on the calibrated rock mechanical property field data of a single well, a one-dimensional rock mechanical model is constructed; The one-dimensional rock mechanics model is vertically discretized to construct a three-dimensional rock mechanics model.

2. The method for constructing a three-dimensional rock mechanics model according to claim 1, characterized in that: The rock mechanics attribute field data include: well logging interpretation curve parameters, rock mechanics parameters and fracturing well monitoring data; The logging interpretation curve parameters include: shear wave and compression wave time difference, natural gamma, mud content, and physical property interpretation results; The rock mechanics parameters include: indoor experimental parameters and rock mechanics experimental parameters, wherein, The indoor experimental parameters include triaxial stress test static and dynamic Young's modulus, Poisson's ratio, ground stress magnitude and direction, and compressive / tensile strength; the typical rock mechanics experimental parameters include acoustic wave characteristic experimental parameters and Kaiser acoustic emission parameters; The monitoring data of the fracturing well includes: fracture pressure, ground stress, fracturing operation curve, fracture morphology prediction and post-fracturing productivity curve.

3. The method for constructing a three-dimensional rock mechanics model according to claim 2, characterized in that: Correcting the rock mechanical property field of the single well includes: The Young's modulus, Poisson's ratio, maximum horizontal stress, minimum horizontal stress, fracture pressure, vertical stress, fracture toughness, shear modulus, confining pressure, pore pressure gradient and vertical stress gradient in the rock mechanical property field of the single well are corrected.

4. The method for constructing a three-dimensional rock mechanics model according to claim 3, characterized in that: The correcting of the rock mechanical property field of the single well includes: Eliminating abnormal values ​​in the logging interpretation curve parameters to obtain the P-wave and S-wave data of a single well; Based on the longitudinal wave and shear wave data, the dynamic Young's modulus and dynamic Poisson's ratio are obtained; Calibrate the dynamic Young's modulus and dynamic Poisson's ratio through acoustic wave characteristic experimental parameters; Performing dynamic-static data conversion on the calibrated dynamic Young's modulus and dynamic Poisson's ratio to obtain a first static Young's modulus and a first static Poisson's ratio; When the difference between the values ​​of the first static Young's modulus and the first static Poisson's ratio and the values ​​of the second static Young's modulus and the second static Poisson's ratio obtained from the triaxial stress static experimental test is less than or equal to a threshold, the correction of the first static Young's modulus and the first static Poisson's ratio is completed.

5. The method for constructing a three-dimensional rock mechanics model according to claim 3, characterized in that: The correction of the rock mechanical property field of the single well further includes: Obtain Kaiser acoustic emission experimental data for multiple samples from a single well; Substituting the Kaiser acoustic emission experimental data into the maximum and minimum horizontal principal stress calculation formulas of the reservoir to calculate the corresponding maximum horizontal stress and minimum horizontal stress; The average values ​​of the maximum horizontal stress and the minimum horizontal stress of all samples are calculated. The average value of the maximum horizontal stress is the corrected maximum horizontal stress, and the average value of the minimum horizontal stress is the corrected minimum horizontal stress.

6. A method for constructing a three-dimensional rock mechanics model according to claim 5, characterized in that: The maximum and minimum horizontal principal stress calculation formulas of the reservoir are as follows: Where σ H represents the maximum horizontal stress, σ h represents the minimum horizontal stress, μ s Represents the static Poisson's ratio, a decimal; σ v represents the vertical principal stress, P p represents the reservoir pore pressure, α represents the Biot elastic coefficient, and β1 and β2 represent the horizontal tectonic stress coefficients.

7. The method for constructing a three-dimensional rock mechanics model according to claim 3, characterized in that: The correction of the rock mechanical property field of the single well further includes: The burst pressure is calculated using the burst pressure theory formula; The burst pressure is corrected using on-site construction parameters.

8. The method for constructing a three-dimensional rock mechanics model according to claim 7, characterized in that: The theoretical formula of burst pressure is as follows: P f =3σ h -s H -αP p +S t S t =S c / c S c =(0.0045E D (1-V sh )+0.008E D V sh )×1000d Where, P f Indicates the burst pressure; P p represents the reservoir pore pressure; S t represents tensile strength; c represents proportional coefficient; S c Indicates compressive strength; V sh represents the mud content; d represents the correction coefficient, which is 1.

2.

9. The method for constructing a three-dimensional rock mechanics model according to claim 3, characterized in that: Correcting the rock mechanical property field of the single well further includes: The correction formulas for vertical stress, fracture toughness, shear modulus, confining pressure, pore pressure gradient, and vertical stress gradient are as follows: Vertical stress: Fracture toughness: Confining pressure: Shear modulus: Pore ​​pressure gradient, vertical stress gradient: P p_g =P p / TVD P z_g =σ z / TVD Among them, σ z represents vertical stress; ρ r (h) represents the density of the overlying rock that changes with depth; H represents the depth of the formation; g represents the acceleration of gravity; P frac_T represents fracture toughness; P c represents the confining pressure; μ S represents the static Poisson's ratio; G represents the shear modulus; P p_g , P z_g Represents pore pressure gradient and vertical stress gradient.

10. The method for constructing a three-dimensional rock mechanics model according to claim 3, characterized in that: Based on the calibrated rock mechanical property field data of a single well, a one-dimensional rock mechanical model is constructed, including: Based on the corrected rock mechanical property field data of a single well, predict the rock mechanical property field data of other wells in the target reservoir; A one-dimensional rock mechanics model is constructed based on rock mechanics property field data of other wells in the target oil reservoir.

11. A method for constructing a three-dimensional rock mechanics model according to claim 10, characterized in that: The rock mechanical property field data of other wells in the predicted target reservoir includes: Based on the corrected shear wave data of a single well and combined with a multi-factor neural network, the shear wave data of other wells are predicted.

12. The method for constructing a three-dimensional rock mechanics model according to claim 10, characterized in that: The rock mechanical property field data of other wells in the predicted target reservoir also includes: Based on the corrected maximum horizontal stress and minimum horizontal stress, the maximum horizontal stress and minimum horizontal stress of other wells are predicted.

13. A method for constructing a three-dimensional rock mechanics model according to any one of claims 10, 11 or 12, characterized in that: The rock mechanical property field data of other wells in the predicted target reservoir also includes: Based on the corrected vertical stress, fracture toughness, shear modulus, confining pressure, pore pressure gradient and vertical stress gradient, the vertical stress, fracture toughness, shear modulus, confining pressure, pore pressure gradient and vertical stress gradient of other wells in the target reservoir are predicted.

14. A method for constructing a three-dimensional rock mechanics model according to claim 13, characterized in that: For the one-dimensional rock mechanics model, a three-dimensional rock mechanics model is constructed, including: performing vertical discretization processing on rock mechanical property field data of other wells in the target oil reservoir in the one-dimensional rock mechanical model; The rock mechanical property field data of other wells in the vertically discretized target reservoir are predicted in plane to construct a three-dimensional rock mechanical model.

15. A device for constructing a three-dimensional rock mechanics model, characterized in that: The device comprises, An acquisition module is used to obtain rock mechanical property field data of a single well in a target reservoir; a correction module, configured to correct the rock mechanical property field of the single well; The first construction module is used to construct a one-dimensional rock mechanics model based on the corrected rock mechanics property field data of the single well; The second construction module is used to perform longitudinal discretization processing on the one-dimensional rock mechanics model to construct a three-dimensional rock mechanics model.

16. Application of the method for constructing a three-dimensional rock mechanics model according to any one of claims 1 to 14 in improving the accuracy of rock mechanics property field modeling in tight oil reservoirs.

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