Multi-scale carbonate rock mechanical parameter evaluation method

Abstract

The invention discloses a multi-scale carbonate rock mechanical parameter evaluation method, which comprises the following steps: S1, carrying out core micro-CT scanning, and extracting different minerals and hole volumes according to densities; s2, carrying out a nanoindentation experiment to obtain mesoscopic rock mechanical parameters of different minerals; s3, generating a GBM model in combination with a rock slice analysis result; s4, inputting mesoscopic rock mechanical parameters obtained by the nanoindentation experiment into the GBM model, calibrating other mechanical parameters, and establishing a digital rock model; and S5, performing digital processing on the acquired large-size outcrop picture, extracting rock defect characteristics such as holes, fractures and cracks and other heterogeneous characteristics, establishing a large-size rock model, simulating field multi-size coring based on the large-size rock model, and finally obtaining mechanical parameters of rocks of different sizes. Compared with a conventional rock mechanics parameter obtaining method, the method is high in reliability and has repeatability.

Description

Technical Field

[0001] The present invention relates to the technical field of rock mechanics, and particularly relates to a method for evaluating multi-scale carbonate rock mechanical parameters. Background Art

[0002] Carbonate rocks are one of the main reservoirs of global oil and gas resources. During the process of oil and gas exploration and development, accurately characterizing and predicting the mechanical parameters of carbonate rocks is a prerequisite for ensuring the efficient and safe progress of oil and gas exploration and development. The traditional methods for obtaining the mechanical parameters of carbonate rock formations are to carry out indoor mechanical experiments through downhole coring or to use logging data for prediction. However, the coring experiment method has high costs, strong discreteness of results, is not repeatable, and can only obtain the mechanical parameters of rocks after drilling, lacking timeliness; the logging data prediction method belongs to indirect testing, with results difficult to verify and low accuracy. In fact, there are multi-scale pore and fracture structures developed inside carbonate rocks, which have experienced multiple tectonic movement transformations, showing extremely strong heterogeneity and size effects. Therefore, combining the mesoscopic rock mechanical parameters at the mineral scale with the pore characteristics of carbonate rocks themselves can better reflect the mechanical properties of formation rocks.

[0003] Chinese Patent CN202010518470.2 discloses a method and system for obtaining the mechanical parameters of deep carbonate rocks. By elemental logging, the rock mineral components and their percentage contents are obtained, and further the reconstructed density, longitudinal wave slowness, and shear wave slowness are obtained and the rock mechanical parameters are calculated. However, this method is difficult to predict the mechanical parameters of carbonate rock formations required for drilling design, and has low accuracy.

[0004] Chinese Patent CN202310609603.0 discloses a method for obtaining macroscopic rock mechanical parameters based on nanoindentation technology. The macroscopic hardness of the rock is obtained through nanoindentation testing, and further the triaxial compressive strength, Young's modulus, and Poisson's ratio are calculated through the rock mechanical parameter relationship model. However, this method does not consider the influence of actual rock pore characteristics on rock mechanical parameters. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for evaluating multi-scale carbonate rock mechanical parameters to meet the requirements of drilling design for obtaining rock mechanical parameters of different sizes, and to provide a theoretical basis for the establishment of a high-precision geological model and the assignment of mechanical parameters, and to provide technical support for preventing downhole complications.

[0006] The present invention is implemented by adopting the following technical solutions: A method for evaluating multi-scale carbonate rock mechanical parameters, comprising the following steps: S1: Perform micro-CT scanning on the core, and extract the volumes of different minerals and pores according to density; S2: Conduct nanoindentation experiments to obtain the mesoscopic rock mechanical parameters of different minerals; S3: Generate a GBM model by combining the results of thin-section analysis of rocks; S4: Input the mesoscopic rock mechanical parameters obtained from nanoindentation experiments into the GBM model, calibrate the remaining mechanical parameters, and establish a digital rock model; S5: Digitally process the large-size outcrop photos collected, extract the rock defect characteristics and heterogeneity characteristics, establish a large-size rock block model, and simulate the multi-size coring in the field based on the large-size rock block model, and finally obtain the rock mechanical parameters of different sizes.

[0007] Furthermore, step S1 includes the following sub-steps: S11: Conduct micro-CT scanning of the core to obtain grayscale images; S12: Input the grayscale images into 3D visualization analysis software for median filtering denoising, threshold segmentation, and 3D reconstruction processing; S13: Extract the volumes of different minerals and pores according to density, and calculate the equivalent pore radius r of the rock by back-calculation based on the spherical volume formula.

[0008] Furthermore, step S2 includes the following sub-steps: S21: Conduct nanoindentation experiments and record the load-displacement curves; S22: Calculate the mesoscopic rock mechanical parameters of different minerals according to the load-displacement curves; S23: Conduct statistical processing on the experimental results, establish a Weibull distribution model of elastic modulus controlled by two parameters, and improve the reliability.

[0009] Furthermore, step S3 includes the following sub-steps: S31: Combine the results of thin-section analysis of rocks, select a GBM including the mineral crystal morphology for model establishment to obtain a GBM model, and randomly generate Voronoi polygons within the set radius range of the GBM model; S32: Take the randomly generated Voronoi polygons as the basic structural units of the model, and fill the interior of the units with particles to ensure that each mineral block contains at least M particles, where M is a preset value; S33: Use the parallel bond contact model to characterize the contact characteristics within and between rock grains.

[0010] Furthermore, step S4 includes the following sub-steps: S41: Input the mesoscopic rock mechanical parameters obtained from nanoindentation experiments into the GBM model; S42: By comparing the results and rock failure modes of numerical simulation and laboratory rock mechanics experiments, calibrate the remaining mechanical parameters, and finally establish a digital rock model.

[0011] Further, the remaining mechanical parameters include one or more of cohesion, tensile strength, and friction angle.

[0012] Further, in the digital rock model, the intergranular parameter assignment is carried out according to the following formula: ; where x is a certain parameter in the PBM model; is the value coefficient of parameter x, n1 is the total number of contacts of particle A at one end of the contact, n2 is the total number of contacts of particle B at the other end of the contact, represents the sum of the values of parameter x in the contacts of particle A, represents the sum of the values of parameter x in the contacts of particle B.

[0013] Further, step S5 includes the following sub-steps: S51: Digitally process the collected large-size outcrop photos, establish different layers, and extract large-size holes and mineral characteristics respectively; S52: Perform upscaling modeling based on the digital rock model to establish a large-size rock block model; S53: Carry out multi-size uniaxial compression rock mechanics simulation experiments based on the large-size rock block model, and finally obtain the rock mechanics parameters of carbonate rocks of different sizes.

[0014] The beneficial effects of the present invention are as follows: Compared with the conventional method for obtaining rock mechanics parameters, the present invention has high reliability and repeatability, and makes up for the problems of strong discreteness of the experimental results of rock mechanics parameters, high economic cost of coring, and difficulty in ensuring timeliness of parameter acquisition in the existing methods.

[0015] The present invention uses numerical simulation to establish a digital rock model and a rock block model respectively, can predict the rock mechanics parameters of carbonate rocks of different sizes, provides a theoretical basis for the establishment of a high-precision geological model and the assignment of mechanical parameters, and provides technical support for preventing downhole complications. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on the structures shown in these drawings.

[0017] Figure 1 is the flow chart of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Components of the embodiments of the present invention described and illustrated herein generally may be arranged and designed in a variety of different configurations.

[0019] It should be noted that like reference numerals and letters denote like items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0020] The following will describe in detail some embodiments of the present invention with reference to the accompanying drawings. Without conflict, the following embodiments and the features in the embodiments may be combined with each other.

[0021] See Figure 1 , a method for evaluating the rock mechanical parameters of multi-scale carbonate rocks, comprising the following steps: Step 1: Conduct a core micro-CT scanning experiment, import the grayscale image into three-dimensional visualization analysis software for processing, extract the pore volume after median filtering for noise reduction and threshold segmentation, and back-calculate the equivalent pore radius r.

[0022] Step 2: Conduct a nanoindentation experiment, record the load-displacement curve, and calculate the mesoscopic rock mechanical parameters of different minerals through the experimental curve.

[0023] Step 3: Based on the experimental results of Step 2, establish a Weibull distribution model of elastic modulus controlled by two parameters using statistical methods, and then test the reliability of the model through a significance level test.

[0024] Step 4: Select the Grain-Based Model (GBM) for model construction in combination with the results of rock thin section analysis, randomly generate Voronoi polygons within the set range of the model radius, use them as the basic structural units of the model, and fill the inside of the units with particles to ensure that each mineral block contains at least 15 particles.

[0025] Step 5: Based on the GBM model generated in Step 4, input the mesoscopic rock mechanical parameters obtained from the nanoindentation experiment into the GBM model, and calibrate the relevant parameters in combination with indoor rock mechanical experiments to obtain a digital rock model.

[0026] Step 6: Digitally process the large-size outcrop photos collected, extract rock defect features such as pores, fissures, and cracks, as well as other inhomogeneous features, and perform upscaling modeling in combination with the digital rock model established in Step 5 to establish a large-size rock block model.

[0027] Step 7: Based on the large-sized rock block model in Step 6, simulate on-site multi-sized coring, conduct multi-sized uniaxial compression rock mechanics simulation experiments, and finally obtain the rock mechanics parameters of carbonate rocks with different sizes.

[0028] In this embodiment, Step 1 is specifically as follows: Conduct core micro-CT scanning. For example, use the three-dimensional reconstruction imaging X-ray microscope MicroXCT-400 to conduct core micro-CT scanning. The preset number of scanning layers is set to 1912, and a three-dimensional grayscale matrix (unit size is 26.54um) of 1091×1091×1912 pixels is obtained. Import the obtained grayscale image into three-dimensional visualization analysis software (Avizo) for processing. First, use median filtering to denoise the image, then perform threshold segmentation and three-dimensional reconstruction on the pores and mineral structures, extract the volumes of different minerals and pores according to density, and calculate the equivalent pore radius r of the rock by the spherical volume formula. The calculation method is as follows: ; where r is the equivalent pore radius, mm; V is the pore volume, mm 3 .

[0029] In this embodiment, Step 2 is specifically as follows: Conduct nanoindentation experiments. For example, use the Agilent NanoIndenter G200 nanoindentation tester in the United States. The specific parameters are: load resolution is 50nN; the maximum load is greater than 500mN; the displacement resolution is less than 0.01nm. The CSM test dynamic frequency is 0.1~300Hz, and the Berkovich drill bit curvature radius is 20nm. Then calculate the mesoscopic rock mechanics parameters of different minerals through the load-displacement curve, and the calculation formula for the material hardness H is: ; In the formula: P max is the maximum test load, N; A c is the contact projection area between the indenter and the material, nm 3 , A c and the relationship with the contact depth h c is: ; ; The reduced modulus of indentation E r is: ; The elastic modulus of the material to be tested E can be expressed as: ; In the formula: h max is the maximum indentation depth, in nm; ε , β are constants related to the indenter shape. For a wave-shaped indenter ε = 0.75, β = 0.15; S is the contact stiffness, which can be calculated based on the load-displacement curve; ν is the Poisson's ratio of the specimen; E i and ν i are the elastic modulus and Poisson's ratio of the indenter respectively, E i = 1140 GPa, ν i = 0.07. The experiment is based on the grid indentation method proposed by Constantinides. Multiple nano-indentation points are arranged on the sample to be measured to qualitatively and quantitatively evaluate the mechanical properties of the rock. Continuous stiffness testing is carried out. The loading rate and unloading rate of both loading models are 0.05 nm / s, the maximum displacement is 1000 nm, and the holding time is 5 s.

[0030] Furthermore, statistical processing is performed on the experimental results to establish a Weibull distribution model of elastic modulus controlled by two parameters. The reliability of the model is tested through the significance level (P = 0.05). The specific expression of the Weibull distribution is: ; In the formula: λ is the size parameter, dimensionless; β is the shape parameter, dimensionless; x is the Weibull distribution random number. After calculating the P value, if P > 0.05, it proves that the Weibull distribution holds, otherwise it does not.

[0031] In this embodiment, steps 3 and 4 are specifically as follows: Combining the analysis results of rock thin sections, a Grain-Based Model (GBM) including the mineral crystal morphology is selected for model establishment. In PFC 2D , first, circular particles are generated according to the mineral component image, where each particle has at least 2 effective contacts. Then, Voronoi cells are generated with the center of the circular particle as the unit centroid. Each cell comes from a circular particle, and the calculation formula is: ; In the formula: d is the Euclidean distance, P is the Euclidean space, C iRepresents a Voronoi cell, S j is the seed, i and j represent different seed numbers.

[0032] The randomly generated Voronoi polygon is used as the basic structural unit of the model, and particles are filled inside the unit to ensure that each mineral block contains at least 15 particles. The Parallel Bond Model is used to characterize the intra-granular and inter-granular contact characteristics of the rock. In the parallel bond model, the normal force F n The calculation formula is: ; In the formula: is the normal bonding stiffness, N / m 3 ; A is the area of the bonding region, m 2 ; is the normal overlap, m.

[0033] The tangential force F s The calculation formula is: ; In the formula: is the tangential bonding stiffness, N / m 3 ; A is the area of the bonding region, m 2 ; is the tangential overlap, m.

[0034] The bending moment M b The calculation formula is: ; In the formula: is the tangential bonding stiffness, N / m 3 ; J is the moment of inertia of the bonding region, m 4 ; is the bending angle, rad.

[0035] The torque M t The calculation formula is: ; In the formula: is the normal bonding stiffness, N / m 3 ; I is the polar moment of inertia of the bonding region, m 4 ; is the torsional angle, rad.

[0036] In the bond fracture condition, the maximum normal stress The calculation formula is: ; In the formula: F n is the normal force, N; A is the area of the bonding region, m 2 ; M b is the bending moment, N·m; R is the bonding radius, m; I is the polar moment of inertia of the bonding region, m 4 .

[0037] The maximum shear stress The calculation formula is: ; In the formula: F s is the shear force, N; A is the area of the bonding region, m 2 ; M t is the torque, N·m; R is the bonding radius, m; J is the moment of inertia of the bonding region, m 4 .

[0038] In this embodiment, step 5 is specifically: input the elastic modulus obtained from the nano-indentation experiment into the GBM model, and calibrate the remaining mechanical parameters (contact stiffness ratio (dimensionless), cohesion ( c , MPa), tensile strength ( T , MPa), friction angle ( θ , °)) by comparing the results of numerical simulation and indoor rock mechanics experiment and the rock failure mode, and finally establish a digital rock model. Considering that the intergranular contact strength of different minerals is different, the intergranular parameter assignment is carried out according to the following formula: ; In the formula: x is a certain parameter in the PBM model; is the value coefficient of parameter x , n 1 is the total number of contacts of particle A at one end of the contact, n 2 is the total number of contacts of particle B at the other end of the contact, represents the sum of the values of parameter x in the contacts of particle A, represents the sum of the values of parameter x in the contacts of particle B.

[0039] In this embodiment, steps 6 and 7 are specifically as follows: digitize the collected large-scale outcrop photos. By using CAD software, establish different layers, extract large-scale holes and mineral features respectively, and generate a.dxf file. Import the.dxf file into PFC 2D and, according to the generation method of the digital rock model, enlarge the model size, delete the particles at the positions of large-scale holes, and group and adjust the mechanical parameters of large-scale minerals. Finally, upscale to establish a large-scale rock block model, and simulate on-site multi-size coring based on the rock block model to obtain rock mechanical parameters of different sizes.

[0040] Based on the above embodiments, the present invention has at least the following technical effects: Compared with the conventional method for obtaining rock mechanical parameters, the present invention has high reliability and repeatability, and makes up for the problems of strong discreteness of the experimental results of rock mechanical parameters, high economic cost of coring, and difficulty in ensuring timeliness of parameter acquisition in the existing methods. The present invention adopts the method of numerical simulation to establish a digital rock model and a rock block model respectively, which can predict the rock mechanical parameters of carbonate rocks of different sizes, provide a theoretical basis for the establishment of a high-precision geological model and the assignment of mechanical parameters, and provide technical support for preventing downhole complications.

[0041] For the foregoing embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, some steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification belong to preferred embodiments, and the actions involved are not necessarily essential to the present application.

[0042] In the above embodiments, the basic principles, main features and advantages of the present invention are described. Those skilled in the art of this industry should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, any modifications and changes made by those skilled in the art shall fall within the protection scope of the appended claims of the present invention.

Claims

1. A multi-scale carbonate rock mechanical parameter evaluation method, characterized in that: The steps include: S1: Perform micro-CT scanning of the core and extract different minerals and pore volumes according to density; S2: Conduct nanoindentation experiments to obtain the mesoscopic rock mechanical parameters of different minerals; S3: Generate GBM model based on rock thin section analysis results; S4: Input the mesoscopic rock mechanical parameters obtained from the nanoindentation experiment into the GBM model, calibrate the remaining mechanical parameters, and establish a digital rock model; S5: Digitally process the collected large-scale outcrop photos, extract rock defect characteristics and heterogeneity characteristics, establish a large-scale rock block model, and simulate on-site multi-size coring based on the large-scale rock block model to finally obtain rock mechanical parameters of different sizes.

2. A multi-scale carbonate rock mechanical parameter evaluation method according to claim 1, characterized in that: Step S1 includes the following sub-steps: S11: Perform micro-CT scanning of the core to obtain a grayscale image; S12: input the grayscale image into the 3D visualization analysis software for median filtering noise reduction, threshold segmentation and 3D reconstruction processing; S13: Extract different minerals and pore volumes according to density, and calculate the equivalent pore radius r of the rock based on the spherical volume formula.

3. A multi-scale carbonate rock mechanical parameter evaluation method according to claim 1, characterized in that: Step S2 includes the following sub-steps: S21: Conduct nanoindentation experiment and record load-displacement curve; S22: Calculate the mesoscopic rock mechanical parameters of different minerals based on the load-displacement curves; S23: Perform statistical processing on the experimental results and establish a dual-parameter controlled elastic modulus Weibull distribution model to improve reliability.

4. A multi-scale carbonate rock mechanical parameter evaluation method according to claim 1, characterized in that: Step S3 includes the following sub-steps: S31: Combined with the rock thin section analysis results, GBM containing mineral crystal morphology is selected to establish the model, a GBM model is obtained, and Voronoi polygons are randomly generated within the GBM model radius setting range; S32: using the randomly generated Voronoi polygon as a basic structural unit of the model, and filling particles inside the unit to ensure that each mineral block contains at least M particles, where M is a preset value; S33: The parallel bond contact model is used to characterize the contact characteristics within and between rock particles.

5. A multi-scale carbonate rock mechanical parameter evaluation method according to claim 1, characterized in that: Step S4 includes the following sub-steps: S41: Input the mesoscopic rock mechanical parameters obtained from the nanoindentation experiment into the GBM model; S42: By comparing the results of numerical simulation and indoor rock mechanics experiments and rock failure modes, the remaining mechanical parameters are calibrated and finally a digital rock model is established.

6. A multi-scale carbonate rock mechanical parameter evaluation method according to claim 5, characterized in that: The remaining mechanical parameters include one or more of cohesion, tensile strength and friction angle.

7. A multi-scale carbonate rock mechanical parameter evaluation method according to claim 5, characterized in that: In the digital rock model, the intergranular parameter assignment is performed as follows: ； In the formula, x is a parameter in the PBM model; is the coefficient of parameter x, n1 is the total number of contacts of particle A at one end of the contact, n2 is the total number of contacts of particle B at the other end of the contact, represents the sum of the values ​​of parameter x in the contact of particle A, Represents the sum of the values ​​of parameter x in the contact of particle B.

8. A multi-scale carbonate rock mechanical parameter evaluation method according to claim 1, characterized in that: Step S5 includes the following sub-steps: S51: Digitally process the collected large-scale outcrop photos, establish different layers, and extract large-scale holes and mineral features respectively; S52: Upscaling modeling based on digital rock model to establish large-scale rock block model; S53: Carry out multi-size uniaxial compression rock mechanics simulation experiments based on large-size rock block models to finally obtain the rock mechanics parameters of carbonate rocks of different sizes.

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