Rock elasticity modulus prediction method and device, electronic equipment and storage medium

Abstract

The invention discloses a rock elasticity modulus prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: in the process of carrying out nanoindentation test on at least two position points of a rock sample, respectively obtaining nanoindentation test data corresponding to each position point in real time; determining a mineral component type corresponding to each position point; wherein the mineral components comprise a hard component and a soft component; determining a hard component elastic modulus and a soft component elastic modulus of the rock sample according to the nanoindentation test data corresponding to each position point and the mineral component type corresponding to each position point; and determining the rock elasticity modulus of the rock sample based on the hard component elasticity modulus and the soft component elasticity modulus. Through the technical scheme provided by the embodiment of the invention, the problem that part of parameters are assumed and are difficult to calculate in an existing model is solved, and accurate prediction of the elastic modulus of the rock is realized.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field development technology, and in particular to a method, apparatus, electronic device and storage medium for predicting the elastic modulus of rocks. Background Technology

[0002] Currently, the recoverable reserves of conventional oil and gas resources are decreasing year by year, while the demand for oil and gas resources is increasing daily. Ultra-deep and unconventional oil and gas formations are accounting for an increasingly larger proportion of energy supply. Deep oil and gas formations have complex geological conditions, high drilling risks, and high development costs for unconventional oil and gas resources. The key technologies and fundamental theoretical systems for safe and efficient drilling and completion need further development and improvement. Rock mechanics parameter evaluation is fundamental to drilling rock breaking, formation pressure monitoring, wellbore stability evaluation, and reservoir fracturing, among which the rock elastic modulus is crucial.

[0003] Traditional methods involve conducting uniaxial compression tests on rocks using core samples obtained from drilling. Laboratory testing is time-consuming and labor-intensive, increasing the cost and time of on-site core sampling. To efficiently predict the macroscopic mechanical parameters of rocks, researchers have developed numerous models to address this issue. These include average models based on rock composition, such as the Voigt model, Reuss model, Hill model, and Hashin-Shtrikman model. These models use the volume ratio of the rock's components and the mechanical properties of each component as input parameters to predict the overall mechanical parameters of the rock. The prediction results are often given as a range, typically in the form of upper and lower limits, and their accuracy needs improvement. Another type of method considers the interaction of stress and strain between components to predict macroscopic mechanical behavior. Examples include the Eshelby equivalent inclusion theory, self-consistent methods, the Mori-Tanaka method, generalized self-consistent methods, and differential methods. Compared to the first type of method, these have certain engineering application value. However, the computational parameters in these methods are very complex, and many parameters cannot be obtained through actual testing. Applying them to predict the mechanical parameters of formation rocks requires assuming certain parameters for calculation, making their application in engineering extremely difficult. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and storage medium for predicting the elastic modulus of rocks, which solves the problem of difficulty in calculation due to assumptions about some parameters in existing models, and realizes accurate prediction of the elastic modulus of rocks.

[0005] According to one aspect of the present invention, a method for predicting the elastic modulus of rock is provided, comprising:

[0006] During the nanoindentation test at at least two locations on the rock sample, the nanoindentation test data corresponding to each location point is acquired in real time.

[0007] Determine the mineral component type corresponding to each location point; wherein, the mineral component type includes hard components and soft components;

[0008] Based on the nanoindentation test data corresponding to each location point and the mineral composition type corresponding to each location point, the elastic modulus of the hard component and the elastic modulus of the soft component of the rock sample are determined.

[0009] The rock elastic modulus of the rock sample is determined based on the elastic modulus of the hard component and the elastic modulus of the soft component.

[0010] According to another aspect of the present invention, a device for predicting the elastic modulus of rock is provided, comprising:

[0011] The test data acquisition module is used to acquire the nanoindentation test data corresponding to each location point in real time during the nanoindentation test at at least two locations on the rock sample.

[0012] A mineral component type determination module is used to determine the mineral component type corresponding to each location point; wherein, the mineral component type includes hard components and soft components;

[0013] The component elastic modulus determination module is used to determine the hard component elastic modulus and soft component elastic modulus of the rock sample based on the nanoindentation test data corresponding to each location point and the mineral component type corresponding to each location point.

[0014] The rock elastic modulus determination module is used to determine the rock elastic modulus of the rock sample based on the elastic modulus of the hard component and the elastic modulus of the soft component.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] At least one processor; and

[0017] A memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the method for predicting the elastic modulus of rock according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the method for predicting the rock elastic modulus according to any embodiment of the present invention.

[0020] The rock elastic modulus prediction scheme of this invention involves acquiring nanoindentation test data for each location point in real time during nanoindentation testing of at least two locations on a rock sample; determining the mineral component type corresponding to each location point; wherein the mineral component type includes hard components and soft components; determining the hard component elastic modulus and soft component elastic modulus of the rock sample based on the nanoindentation test data and the corresponding mineral component type; and determining the rock elastic modulus of the rock sample based on the hard component elastic modulus and soft component elastic modulus. The technical solution provided by this invention solves the problem of difficulty in calculation due to assumptions about some parameters in existing models, and achieves accurate prediction of rock elastic modulus.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A flowchart of a method for predicting the elastic modulus of rock provided in Embodiment 1 of the present invention;

[0024] Figure 2 A flowchart of a method for predicting the elastic modulus of rock provided in Embodiment 1 of the present invention;

[0025] Figure 3 Electron scanning images of shale samples from different regions provided in embodiments of the present invention;

[0026] Figure 4 A schematic diagram of a completed rock sample provided in an embodiment of the present invention;

[0027] Figure 5 This is a test image of the indentation surface morphology provided in an embodiment of the present invention;

[0028] Figure 6 This is an indentation depth-load curve obtained by the continuous stiffness method in an embodiment of the present invention.

[0029] Figure 7 According to Figure 6 The depth-elastic modulus curve obtained by calculating the indentation depth-load curve in the indentation test;

[0030] Figure 8a The indentation depth-load curves are for four indentation tests on sample F2.

[0031] Figure 8b The indentation depth-elastic modulus curves are for four indentation tests on sample F2.

[0032] Figure 9 A scanning electron microscope image of an indentation experiment on a rock sample provided in an embodiment of the present invention;

[0033] Figure 10 Typical mineral indentation experimental SEM images and corresponding EDS images and indentation depth-modulus test curves are provided in the embodiments of the present invention.

[0034] Figure 11 This is a schematic diagram illustrating the relationship between soft components and elastic modulus.

[0035] Figure 12 This is a comparison chart of the predicted rock elastic modulus and experimental data provided in the embodiments of the present invention;

[0036] Figure 13 This is a schematic diagram of the structure of a rock elastic modulus prediction device provided in Embodiment 3 of the present invention;

[0037] Figure 14 A schematic diagram of the structure of an electronic device for implementing the rock elastic modulus prediction method of this embodiment of the invention. Detailed Implementation

[0038] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0039] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0040] Example 1

[0041] Figure 1 This is a flowchart of a method for predicting the elastic modulus of rock according to Embodiment 1 of the present invention. This embodiment is applicable to the prediction of the elastic modulus of rock. The method can be executed by a rock elastic modulus prediction device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0042] S110. During the nanoindentation test at at least two locations on the rock sample, the nanoindentation test data corresponding to each location point is acquired in real time.

[0043] In this embodiment of the invention, drilling cuttings, spalling fragments, or rock samples can be fixed to generate a rock sample. The rock sample can be placed in a circular mold, and epoxy resin can be poured in. After the resin hardens, the sample can be fixed and encapsulated. Then, the rock encapsulated in the epoxy resin is revealed by polishing. Finally, the sample surface is polished with 3000-grit sandpaper to make it smooth and clean, and then polished with a soft cloth. The polished sample is placed in a 100℃ drying oven for 24 hours, and the sample preparation is complete. Nanoindentation tests are performed on at least two locations on the rock sample. The surface indentation rate used in the nanoindentation test can be 30 nm / s, the loading strain rate can be 0.05, the loading frequency can be 45 Hz, the loading displacement can be 2 nm, the indentation depth limit of each location can be 5000 nm, and the indentation load limit can be 800 mN. The nanoindentation test at each location includes three parts: loading, holding, and unloading, and the holding time can be 5 s. The indentation test spacing corresponding to each location on the rock sample can be 0.8 mm. The number of nanoindentation testing sites can be determined by indenting rock samples. This number of sites should ensure that the indentation covers all minerals. During nanoindentation testing at at least two sites on the rock sample, nanoindentation data for each site is acquired in real time. This data includes indentation depth, load, and elastic modulus as a function of indentation depth. It is understood that the elastic modulus changes continuously with increasing indentation depth during nanoindentation testing at each site on the rock sample; therefore, the elastic modulus as a function of indentation depth for each site can form an elastic modulus curve.

[0044] S120. Determine the mineral component type corresponding to each location point; wherein, the mineral component type includes hard components and soft components.

[0045] For example, the residual indentation after nanoindentation testing at each location can be observed using an electron microscope, and the mineral composition type corresponding to each location can be determined based on the residual indentation. The mineral composition type includes hard components and soft components. For instance, if the residual indentation at a location is deep, the mineral composition type at that location can be determined to be a soft component; if the residual indentation at a location is shallow, the mineral composition type at that location can be determined to be a hard component. Hard components may include quartz, feldspar, calcite, and pyrite, etc.; soft components may include clay, mica, organic matter, etc.

[0046] S130. Based on the nanoindentation test data corresponding to each location point and the mineral component type corresponding to each location point, determine the elastic modulus of the hard component and the elastic modulus of the soft component of the rock sample.

[0047] In this embodiment of the invention, at least two locations for nanoindentation testing are divided into two categories based on the mineral composition type corresponding to each location point: locations where the mineral composition type is hard (can be referred to as hard component locations) and locations where the mineral composition type is soft (can be referred to as soft component locations). The elastic modulus of the hard component of the rock sample is determined based on the nanoindentation test data corresponding to the hard component locations, and the elastic modulus of the soft component of the rock sample is determined based on the nanoindentation test data corresponding to the soft component locations.

[0048] Optionally, the nanoindentation test data includes indentation depth and depth elastic modulus varying with indentation depth; determining the hard component elastic modulus of the rock sample based on the nanoindentation test data corresponding to each location point and the mineral component type corresponding to each location point includes: determining a first location point where the mineral component type is hard and a second location point where the mineral component type is soft from the at least two location points; determining a first location elastic modulus corresponding to the first location point based on the depth elastic modulus varying with indentation depth corresponding to the first location point, and determining a second location elastic modulus corresponding to the second location point based on the depth elastic modulus varying with indentation depth corresponding to the second location point; taking the average of the first location elastic moduli corresponding to all first location points as the hard component elastic modulus of the rock sample, and taking the average of the second location elastic moduli corresponding to all second location points as the soft component elastic modulus of the rock sample.

[0049] In this embodiment of the invention, the nanoindentation test data may include the indentation depth and the elastic modulus varying with the indentation depth. For ease of description, the elastic modulus varying with the indentation depth is referred to as the depth elastic modulus. Locations where the mineral component type is hard are referred to as first location points (i.e., hard component location points), and locations where the mineral component type is soft are referred to as second location points (i.e., soft component location points). The sum of the number of first location points and second location points is the total number of location points for which nanoindentation testing is performed. For each first location point, the first location elastic modulus corresponding to the first location point is determined based on the depth elastic modulus varying with the indentation depth. For example, the average value of the depth elastic modulus varying with the indentation depth corresponding to the first location point can be used as the first location elastic modulus corresponding to the first location point. For each second location point, the second location elastic modulus corresponding to the second location point is determined based on the depth elastic modulus varying with the indentation depth corresponding to the second location point. For example, the average value of the depth elastic modulus varying with the indentation depth corresponding to the second location point can be used as the second location elastic modulus corresponding to the second location point.

[0050] Optionally, determining the first positional elastic modulus corresponding to the first position point based on the depth elastic modulus varying with the indentation depth at the first position point includes: determining the maximum depth elastic modulus within a preset indentation depth range from the depth elastic modulus varying with the indentation depth at the first position point, and using the maximum depth elastic modulus as the first positional elastic modulus corresponding to the first position point. For example, since the elastic modulus varying with the indentation depth at each position point can form an elastic modulus curve, the depth elastic modulus corresponding to the highest point within the indentation depth range of 0-500 nm (i.e., the maximum depth elastic modulus) can be taken from the elastic modulus curve corresponding to the first position point as the first positional elastic modulus corresponding to the first position point.

[0051] Optionally, determining the second positional elastic modulus corresponding to the second position point based on the depth elastic modulus varying with the indentation depth at the second position point includes: determining a stable and unchanging depth elastic modulus within a preset depth length from the depth elastic modulus varying with the indentation depth at the second position point, and using the stable and unchanging depth elastic modulus as the second positional elastic modulus corresponding to the second position point. For example, since the elastic modulus varying with the indentation depth at each position point can form an elastic modulus curve, the depth elastic modulus with a smaller initial depth elastic modulus and approximately horizontal stability on the elastic modulus curve at the second position point can be used as the stable and unchanging depth elastic modulus within a preset depth length from the depth elastic modulus varying with the indentation depth at the second position point, and this stable and unchanging depth elastic modulus is used as the second positional elastic modulus corresponding to the second position point.

[0052] Following the method described above, the first position elastic modulus corresponding to each first position point and the second position elastic modulus corresponding to each second position point can be determined. The average of the first position elastic moduli corresponding to all first position points is calculated, and this average is taken as the elastic modulus of the hard component of the rock sample. The average of the second position elastic moduli corresponding to all second position points is calculated, and this average is taken as the elastic modulus of the soft component of the rock sample.

[0053] S140. Determine the rock elastic modulus of the rock sample based on the elastic modulus of the hard component and the elastic modulus of the soft component.

[0054] In this embodiment of the invention, the rock elastic modulus of a rock sample is calculated based on the elastic modulus of its hard and soft components, wherein the rock elastic modulus is the macroscopic elastic modulus of the rock sample. For example, the sum of the elastic modulus of the hard and soft components can be used as the rock elastic modulus of the rock sample. Optionally, before determining the rock elastic modulus of the rock sample based on the elastic modulus of the hard and soft components, the method further includes: determining the volume content of the hard components of the rock sample; determining the rock elastic modulus of the rock sample based on the elastic modulus of the hard and soft components includes: determining the volume modulus of the hard components of the rock sample based on the elastic modulus of the hard components, and determining the volume modulus of the soft components of the rock sample based on the elastic modulus of the soft components; determining the volume modulus of the rock sample based on the volume modulus of the hard components, the volume modulus of the soft components, and the volume content of the hard components; and determining the rock elastic modulus of the rock sample based on the volume modulus of the rock samples.

[0055] For example, conventional X-ray diffraction can be used to test the mineral composition of a rock sample, obtaining the mass percentage of each mineral component. This mass percentage is then converted to a volume percentage. The volume percentage of the hard component is determined from the volume percentages of all mineral components, and the average volume percentage of the hard component is taken as the volume content of the hard component in the rock sample. Based on the mapping relationship between elastic modulus and bulk modulus, the elastic modulus of the hard component is converted to its corresponding bulk modulus, and the elastic modulus of the soft component is converted to its corresponding bulk modulus. For example, the bulk modulus of the hard component and the soft component can be determined using the modulus conversion formula E = 3K(1-2ν), where E represents the elastic modulus, K represents the bulk modulus, and ν represents a pre-set constant. It should be noted that ν in the modulus conversion formula can be different constants or the same constant when determining the bulk modulus of the hard and soft components. The bulk modulus of the rock sample is determined based on the bulk modulus of the hard component, the bulk modulus of the soft component, and the volume content of the hard component. Then, the bulk modulus of the rock sample is converted into the elastic modulus of the rock sample using the above conversion formula.

[0056] Optionally, before determining the bulk modulus of the rock sample based on the bulk modulus of the hard component, the bulk modulus of the soft component, and the volume content of the hard component, the method further includes: determining the particle size of the hard component minerals corresponding to the hard component location points among the at least two location points, and calculating the average particle size of the hard component minerals at all hard component location points; wherein, the hard component location points are location points where the mineral component type is hard component; determining the cementation surface modulus corresponding to each of the at least two location points based on the nanoindentation test data, and calculating the average cementation surface modulus at all location points; calculating intermediate variables based on the average particle size of the hard component minerals and the average cementation surface modulus; determining the bulk modulus of the rock sample based on the bulk modulus of the hard component, the bulk modulus of the soft component, and the volume content of the hard component, including: determining the bulk modulus of the rock sample based on the bulk modulus of the hard component, the bulk modulus of the soft component, the volume content of the hard component, and the intermediate variables.

[0057] In this embodiment of the invention, when observing the residual indentation after nanoindentation testing at each location point using an electron microscope, the particle size of the mineral particles at each location point can be further determined using the electron microscope. Since the location points are divided into hard component location points and soft component location points, the particle size of the mineral particles corresponding to the hard component location points is referred to as the hard component mineral particle size. The average particle size of the hard component mineral particles at all hard component location points is calculated. For each location point where nanoindentation testing is performed, the cementing modulus corresponding to that location point is determined based on the nanoindentation test data corresponding to that location point. Optionally, the nanoindentation test data includes the indentation depth and the depth elastic modulus that varies with the indentation depth; determining the cementing modulus corresponding to each of the at least two location points based on the nanoindentation test data includes: for each of the at least two location points, determining the maximum depth elastic modulus and the stationary elastic modulus that remains unchanged with increasing indentation depth based on the depth elastic modulus that varies with the indentation depth corresponding to that location point; and taking the difference between the maximum depth elastic modulus and the stationary elastic modulus as the cementing modulus corresponding to that location point. Since the elastic modulus at each location point, varying with indentation depth, can form an elastic modulus curve, the maximum depth elastic modulus (i.e., the maximum depth elastic modulus) and the stationary elastic modulus that remains constant with increasing indentation depth can be determined from the elastic modulus curve at each location point. The stationary elastic modulus can also be understood as the elastic modulus after the elastic modulus curve stabilizes. The difference between the maximum depth elastic modulus and the stationary elastic modulus at each location point is calculated, and this difference is used as the cemented surface modulus at that location point.

[0058] In this embodiment of the invention, after determining the cementation surface modulus corresponding to each location point in the manner described above, the average cementation surface modulus of all location points is calculated. An intermediate variable is calculated based on the average particle size of the hard component minerals and the average cementation surface modulus. Then, based on the bulk modulus of the hard component, the bulk modulus of the soft component, the volume content of the hard component, and the intermediate variable, the rock bulk modulus of the rock sample is determined. For example, the rock bulk modulus of the rock sample can be calculated using the following formula:

[0059]

[0060] Where K represents the bulk modulus of the rock, K r K represents the bulk modulus of the hard component. m Let λ represent the bulk modulus of the soft component, f represent the volume content of the hard component, and Λ1 and λ2 be intermediate variables. The intermediate variables can be calculated using the following formula:

[0061]

[0062] in,

[0063]

[0064] The weak interfaces of rocks are mainly manifested in the parameters α0 and β0, which are calculated as follows: Where α0 and β0 are the shear compliance coefficient and normal compliance coefficient of the cementation surface between the hard and soft components, respectively, a represents the average particle size of the hard component minerals, and μ m Modulus conversion formula Calculate, ν r ν m The values ​​are typically taken as 0.25 and 0.3, respectively. Microscopically, the shear and normal compliance of the cemented surface can be considered to be consistent; therefore, α = β is chosen here. The mean modulus of the cemented surface is ΔE, and its reciprocal is the compliance. Therefore, the values ​​of α and β are...

[0065] The method for predicting the elastic modulus of rocks according to embodiments of the present invention involves acquiring nanoindentation test data at each location point in real time during nanoindentation testing of at least two locations on a rock sample; determining the mineral component type corresponding to each location point; wherein the mineral component type includes hard components and soft components; determining the hard component elastic modulus and soft component elastic modulus of the rock sample based on the nanoindentation test data and the corresponding mineral component type; and determining the rock elastic modulus of the rock sample based on the hard component elastic modulus and soft component elastic modulus. The technical solution provided by the embodiments of the present invention solves the problem of difficulty in calculation due to assumptions about some parameters in existing models, and achieves accurate prediction of the rock elastic modulus.

[0066] Example 2

[0067] Figure 2 A flowchart of a method for predicting the elastic modulus of rock provided in Embodiment 2 of the present invention is shown below. Figure 2 As shown, the method includes:

[0068] S210. During the nanoindentation test at at least two locations on the rock sample, the nanoindentation test data corresponding to each location is acquired in real time; wherein, the nanoindentation test data includes the indentation depth and the depth elastic modulus that varies with the indentation depth.

[0069] S220. Determine the mineral component type corresponding to each location point; wherein, the mineral component type includes hard components and soft components.

[0070] S230. Determine a first location point where the mineral component type is hard and a second location point where the mineral component type is soft from the at least two location points.

[0071] S240. Based on the depth elastic modulus corresponding to the first position point that varies with the indentation depth, determine the first position elastic modulus corresponding to the first position point, and based on the depth elastic modulus corresponding to the second position point that varies with the indentation depth, determine the second position elastic modulus corresponding to the second position point.

[0072] S250, take the average value of the first position elastic modulus corresponding to all the first position points as the hard component elastic modulus of the rock sample, and take the average value of the second position elastic modulus corresponding to all the second position points as the soft component elastic modulus of the rock sample.

[0073] S260. Determine the volume content of the hard components in the rock sample.

[0074] S270. Determine the bulk modulus of the hard component of the rock sample based on the elastic modulus of the hard component, and determine the bulk modulus of the soft component of the rock sample based on the elastic modulus of the soft component.

[0075] S280. Determine the particle size of the hard component mineral particles corresponding to the hard component location points among the at least two location points, and calculate the average particle size of the hard component mineral particles at all hard component location points; wherein, the hard component location point is a location point where the mineral component type is hard component.

[0076] S290. Determine the bonding surface modulus corresponding to each of the at least two location points based on the nanoindentation test data, and calculate the average bonding surface modulus of all location points.

[0077] S2100. Calculate intermediate variables based on the average particle size of the hard component mineral particles and the average surface modulus of the cementation surface.

[0078] S2110. Determine the rock bulk modulus of the rock sample based on the bulk modulus of the hard component, the bulk modulus of the soft component, the volume content of the hard component, and the intermediate variable.

[0079] S2120. Determine the rock elastic modulus of the rock sample based on the rock bulk modulus.

[0080] In this embodiment of the invention, the collected rock samples came from three basins in China: the Sichuan Basin, the Songliao Basin, and the Ordos Basin. The Upper Ordovician Wufeng Formation and the Lower Triassic Longmaxi Formation shale in the Sichuan Basin are the main producing layers for shale gas in China. The Longmaxi Formation shale collected in this experimental study came from the Fuling and Qianjiang areas of Chongqing. The development of shale oil in the Upper Cretaceous Nenjiang Formation of the Songliao Basin is still in its early stages, but its development potential is enormous. This experiment collected a shale sample from a section of the Nenjiang Formation in Songyuan, Jilin Province. The Upper Triassic Yanchang Formation shale in the Ordos Basin is the main producing layer for continental shale oil development in China; this study collected shale samples from seven sections of the Yanchang Formation in Yan'an, Shaanxi Province.

[0081] The mineral composition of the shale was analyzed using X-ray diffraction (XRD), with a precision of one decimal place. The total organic carbon content was measured using a Leco CS230 Carbon-Sulfur instrument, with a precision of 0.5%. Since clay and organic carbon are both soft components in shale and significantly affect its micromechanical properties, the total organic carbon content was substituted into the mineral composition specific gravity obtained from the XRD analysis for easier pattern finding. The total clay and organic matter content of the collected shale ranged from 19.7% to 58.1%. Other major components were quartz, feldspar, calcite, dolomite, and pyrite, all belonging to hard minerals. For example... Figure 3The images provided in this embodiment of the invention are electronically scanned images of shale samples from different regions, where F represents a shale sample from Fuling, Q represents a shale sample from Qianjiang, J represents a shale sample from Jilin, and Y represents a shale sample from Yan'an. Figure 3 As shown in the microscopic images, the hard minerals such as quartz, feldspar, and calcite contained in the shale are granular with a diameter of 2–31 μm.

[0082] The nanoindentation experiment in this embodiment of the invention may include four steps: sample preparation, nanoindentation testing, indentation feature observation, and data analysis.

[0083] Step 1: Fix the sample by placing it in a circular mold and pouring epoxy resin. After the resin hardens, it will fix and encapsulate the sample. Then, polish the sample to reveal the shale encapsulated in the epoxy resin. Finally, polish the sample surface with 3000-grit sandpaper to make it smooth and clean, and then polish it with a soft cloth. Place the polished sample in a 100℃ drying oven for 24 hours. Sample preparation is now complete. Figure 4 This is a schematic diagram of a completed rock sample provided in an embodiment of the present invention.

[0084] Step 2: The surface approach velocity used in the nanoindentation test is 30 nm / s, the strain rate target is 0.05, the CSM loading frequency target is 45 Hz, and the CSM loading displacement target is 2 nm. The depth limit is 5000 nm, and the indentation load limit is 800 mN. Each test consists of three parts: loading, holding, and unloading, with a holding time of 5 seconds. For example... Figure 5 This is a test image of the indentation surface morphology provided in an embodiment of the present invention. Figure 5 Figure (a) is a scanning electron microscope image of the indentation surface, (b) is a three-dimensional image of the indentation surface, (c) is a two-dimensional cross-sectional image of the indentation surface, and (d) is a surface morphology height curve along the line connecting AB and CD in Figure c. Figure 5 As shown, the indentation test spacing was 0.8 mm, with a 5×5 dot matrix, and a total of 25 tests were conducted on each sample. During the experiment, data such as indentation depth, load, elastic modulus with indentation depth, and hardness were recorded.

[0085] Step 3: Observe the residual indentation after testing using an electron microscope, and analyze the correspondence between the mechanical parameters and the indentation obtained by the continuous stiffness test method.

[0086] Step 4: Different indentation depths during the nanoindentation test represent the mechanical properties of the rock at different scales.

[0087] In this embodiment of the invention, the indentation depth-load curve corresponding to each indentation test point in each shale sample is determined. For example, Figure 6 The indentation depth-load curve obtained by the continuous stiffness method in this embodiment of the invention is shown in the figure. Figure 6 In the graph, the horizontal axis *h* represents the indentation depth, and the vertical axis *F* represents the applied force. Different colored curves represent the 25 indentation test curves for each sample group. Figure 6 Differences in indentation depth and load were observed in the curves for different shale samples, reflecting differences in their mechanical properties. A larger indentation depth and a smaller load indicated lower shale strength. Conversely, a smaller indentation depth and a larger load indicated higher shale strength. Although the experiment was designed with an indentation depth of 5000 nm and a load limit of 800 mN, not all samples actually reached these conditions during indentation testing, which is related to the mechanical properties of the samples. With increasing clay and organic matter content, the load decreased at the same indentation depth. Conversely, with decreasing clay and organic matter content, the indentation depth decreased at the same load.

[0088] Figure 7 According to Figure 6 The depth-elastic modulus curve is obtained from the indentation depth-load curve calculated in the indentation test. Figure 7 The horizontal axis h represents the indentation depth, and the vertical axis E represents the elastic modulus. Different colored curves represent 25 indentation test curves for each group of samples. Figure 7 Each curve represents the mechanical parameters obtained from dynamic indentation tests based on the continuous stiffness method as the indenter is pressed into the sample. These results represent the local mechanical properties of the surface at different indentation depths and indentation areas.

[0089] Figure 8a The graphs show the indentation depth-load curves corresponding to four indentation tests on sample F2. Figure 8b These are the indentation depth-elastic modulus curves corresponding to four indentation tests on sample F2. Figure 8b It can be observed that with increasing indentation depth, the elastic modulus of the four curves tends to a fixed value after approximately 1000 nm of indentation depth, and these values ​​are quite similar. For example... Figure 8bThe blue and red elastic modulus results show that the elastic modulus increases rapidly with indentation depth to a peak, then decreases, and finally tends to a fixed value. However, the peak values ​​differ, which is related to the mineral composition at the indentation site. The green curve shows that the elastic modulus increases rapidly with indentation depth and then tends to a stable value. The purple curve shows that the elastic modulus increases slowly with indentation depth to a certain fixed value. The red curve corresponds to the pop-in phenomenon at an indentation depth of 2407.48 nm, which is related to the fracturing and breakage of surface minerals.

[0090] Figure 9 This is a scanning electron microscope (SEM) image of an indentation experiment on a rock sample provided in an embodiment of the present invention. To correspond the indentation data with the indentation points, a mark was made near the first indentation point. For each group of samples, a representative indentation data point was selected to analyze the correspondence between the elastic modulus and the indentation process. Figure 10 The present invention provides typical mineral indentation experimental SEM images, corresponding EDS images, and indentation depth-modulus test curves. Figure 10 From top to bottom, the images show SEM images and corresponding EDS images of the indentation experiments for organic matter, calcite, quartz, feldspar, clay, and pyrite, along with indentation depth-modulus curves. Combining the SEM and EDS images, the mineral composition at the indentation points can be clearly identified. F1-10 and Q1-09 initially contain organic matter, with corresponding elastic moduli of 10.25 and 14.43 GPa, respectively. F4-06 and Q2-12 contain calcite, with corresponding elastic moduli of 69.73 and 78.09 GPa, respectively. F5-08 and F5-18 initially contain quartz, with corresponding elastic moduli of 90.51 and 81.97 GPa, respectively. Q1-01 and Q3-11 initially contain feldspar, with corresponding elastic moduli of 51.24 and 41.99 GPa, respectively. Y1-03 and Y1-12 are clays, with corresponding elastic moduli of 19.60 and 18.87 GPa, respectively. F2-13 and F6-06 initially contained pyrite, with corresponding elastic moduli of 172.75 and 194.87 GPa, respectively. These analyses indicate that... Figure 7 , Figure 8a , Figure 8b The results shown can reflect the results at different depths. The mechanical parameters of the single-phase mineral are shown at the initial indentation, and the mechanical parameters of the multi-mineral system are shown after 3000 nm.

[0091] For rock-like multiphase composite media on a macroscopic scale, the average stress and average strain are:

[0092]

[0093] In the formula, These are the average stress and average strain of the hard component, respectively. These represent the average stress and average strain of the soft component, respectively. i [ ] represents the displacement discontinuity on both sides of the weakly cemented surface, V is the total volume of the rock, f is the volume content of the hard component, which is summed with the soft component to 100%, and the area integral domain s includes all weakly cemented surfaces in the rock. The effective elastic modulus of the composite medium is obtained by the following formula:

[0094]

[0095] When a uniform stress is applied in the far field At that time, set up From equations (5) and (6), we can obtain:

[0096]

[0097] In the formula, R pqkl These are the components of the stress concentration factor tensor at the edge of the hard component. Let C be the components of the compliance tensor of the hard component, soft component, and multiphase composite medium, respectively, satisfying C. ijpq D pqkl =I ijkl Solve for R. pqkl and φ ikl The effective elastic modulus of the weakly cemented rock can be calculated using equation (7).

[0098] The Mori-Tanaka method is a relatively simple theoretical method with clear physical meaning. In this paper, a weakly cemented surface is introduced to improve it, offering a possible method for predicting the mechanical parameters of multiphase composite rock media. By introducing a hard component into the multiphase composite medium, the far-field stress and strain of the hard component are equivalent to the average stress and strain of the soft component. The average stress of the hard component is:

[0099]

[0100] The stress concentration factor tensor of the hard component is:

[0101]

[0102] r ijkl =(1-f)I ijkl +fΛ ijkl (11)

[0103] The displacement discontinuities on both sides of the interface are:

[0104]

[0105] Therefore, the bulk modulus of the weakly cemented multiphase composite medium can be obtained from equation (7), resulting in the following equation:

[0106]

[0107] Furthermore, the elastic modulus of the multiphase composite medium of weakly cemented rock can be obtained by using the modulus conversion formula E=3K(1-2ν). The solutions for Λ1 and Λ2 are as follows:

[0108]

[0109] In the multiphase composite medium model of weakly cemented rocks, some key parameters need to be obtained in order to calculate their macroscopic mechanical parameters. When substituting the volume content f of the hard component in the rock, the volume content needs to be converted to a value between 0 and 1 to make it dimensionless. Equation (14) Mechanical parameters K of hard and soft components r K m It can be obtained through test results and modulus conversion formula.

[0110] The weak interfaces of rocks are mainly manifested in the parameters α0 and β0, which are calculated as follows:

[0111]

[0112] In the formula, α0 and β0 are the shear compliance coefficient and normal compliance coefficient of the bonding surface between the hard component and the soft component, respectively, and μ m Modulus conversion formula calculate.

[0113] Microscopically, the shear and normal compliance of the cemented surface can be considered to be consistent; therefore, the value α = β is taken here. The modulus of the cemented surface is ΔE, and its reciprocal is the compliance. Therefore, the values ​​of α and β are... The particle size 'a' of the hard component can be read from the graph. Substituting the data from each group of samples in the nanoindentation experiment, the shear compliance coefficient for each group can be calculated. In the formula, E... r E m Substituting the test results, we take the average value of the single-mineral elastic modulus of Quartz, Feldspar, Calcite, and Dolomite as E. r Take the average elastic modulus of the Clay and Organic single components as E. m Because pyrite has a very high elastic modulus and is present in small quantities, it was not included in the average elastic modulus of the hard component. Pores do not possess an elastic modulus and were therefore not included in the calculation of the elastic modulus of the soft component. r ν m The values ​​are 025 and 0.3 respectively.

[0114] Based on the volumetric composition of components, single-phase mechanical parameters, cementation strength, and mineral grain size in shale, the predicted macroscopic elastic modulus of shale is calculated. For example, Figure 11 This is a schematic diagram showing the relationship between soft components and elastic modulus, where, Figure 11 The horizontal axis represents the volumetric content of the soft component, and the vertical axis represents the elastic modulus. For example... Figure 11 As shown, the predicted results have a high degree of agreement with the experimental data. Table 1 is the standard error analysis table provided in the embodiments of the present invention. The error analysis in Table 1 shows that the error is between 0.24% and 17.90%. The experimental results and predicted results are fitted together... Figure 12 This is a comparison chart of the predicted rock elastic modulus and experimental data provided in the embodiments of the present invention. Figure 12 The horizontal axis represents the experimental test result of the elastic modulus, and the vertical axis represents the predicted result of the elastic modulus determined based on the technical solution provided in the embodiments of the present invention. For example... Figure 12 As shown, the goodness of fit reached 0.92. This confirms that the weakly cemented surface model has a clear physical meaning, easily obtainable parameters, and high prediction accuracy, making it suitable for widespread application in engineering.

[0115] Table 1 Standard Error Analysis Table (Data units in the table are %)

[0116]

[0117] Example 3

[0118] Figure 13 This is a schematic diagram of a rock elastic modulus prediction device provided in Embodiment 3 of the present invention. Figure 13 As shown, the device includes:

[0119] The test data acquisition module 1310 is used to acquire the nanoindentation test data corresponding to each location point in real time during the nanoindentation test at least two locations on the rock sample.

[0120] The mineral component type determination module 1320 is used to determine the mineral component type corresponding to each location point; wherein, the mineral component type includes hard components and soft components;

[0121] The component elastic modulus determination module 1330 is used to determine the hard component elastic modulus and soft component elastic modulus of the rock sample based on the nanoindentation test data corresponding to each location point and the mineral component type corresponding to each location point.

[0122] The rock elastic modulus determination module 1340 is used to determine the rock elastic modulus of the rock sample based on the elastic modulus of the hard component and the elastic modulus of the soft component.

[0123] Optionally, the device further includes:

[0124] The hard component volume content determination module is used to determine the hard component volume content of the rock sample before determining the rock elastic modulus based on the hard component elastic modulus and the soft component elastic modulus.

[0125] The rock elastic modulus determination module includes:

[0126] A component bulk modulus determination unit is used to determine the hard component bulk modulus of the rock sample based on the hard component elastic modulus, and to determine the soft component bulk modulus of the rock sample based on the soft component elastic modulus.

[0127] A rock bulk modulus determination unit is used to determine the rock bulk modulus of the rock sample based on the bulk modulus of the hard component, the bulk modulus of the soft component, and the volume content of the hard component.

[0128] A rock elastic modulus determination unit is used to determine the rock elastic modulus of the rock sample based on the rock bulk modulus.

[0129] Optional, also includes:

[0130] The hard component mineral particle size determination module is used to determine the hard component mineral particle size corresponding to the hard component location point among the at least two location points before determining the rock bulk modulus of the rock sample based on the hard component bulk modulus, the soft component bulk modulus, and the hard component volume content, and to calculate the average hard component mineral particle size of all hard component location points; wherein, the hard component location point is a location point where the mineral component type is hard component.

[0131] The bonding surface modulus determination module is used to determine the bonding surface modulus corresponding to each of the at least two location points based on the nanoindentation test data, and to calculate the average bonding surface modulus of all location points.

[0132] The intermediate variable calculation module is used to calculate intermediate variables based on the average particle size and average surface modulus of the hard component mineral particles.

[0133] The rock bulk modulus determination unit is used for:

[0134] The rock bulk modulus of the rock sample is determined based on the bulk modulus of the hard component, the bulk modulus of the soft component, the volume content of the hard component, and the intermediate variable.

[0135] Optionally, the nanoindentation test data includes the indentation depth and the depth elastic modulus as a function of the indentation depth;

[0136] The adhesive surface modulus determination module is used for:

[0137] For each of the at least two location points, the maximum depth elastic modulus and the stationary elastic modulus that remain unchanged with increasing indentation depth are determined based on the depth elastic modulus that varies with the indentation depth corresponding to the location point.

[0138] The difference between the maximum depth elastic modulus and the steady elastic modulus is taken as the bonding surface modulus corresponding to the location point.

[0139] Optionally, the nanoindentation test data includes the indentation depth and the depth elastic modulus as a function of the indentation depth;

[0140] The component elastic modulus determination module includes:

[0141] A location point classification unit is used to determine, from the at least two location points, a first location point where the mineral component type is a hard component and a second location point where the mineral component type is a soft component;

[0142] The position elasticity module determining unit is used to determine the first position elasticity modulus corresponding to the first position point based on the depth elasticity modulus corresponding to the first position point that varies with the indentation depth, and to determine the second position elasticity modulus corresponding to the second position point based on the depth elasticity modulus corresponding to the second position point that varies with the indentation depth.

[0143] The component elastic modulus determination unit is used to take the average value of the first position elastic modulus corresponding to all the first position points as the hard component elastic modulus of the rock sample, and to take the average value of the second position elastic modulus corresponding to all the second position points as the soft component elastic modulus of the rock sample.

[0144] Optionally, the position elastic modulus determining unit is used for:

[0145] The maximum depth elastic modulus within a preset indentation depth range is determined from the depth elastic modulus that varies with the indentation depth corresponding to the first position point, and the maximum depth elastic modulus is used as the first position elastic modulus corresponding to the first position point.

[0146] Optionally, the position elastic modulus determining unit is used for:

[0147] The stable and unchanging deep elastic modulus within a preset depth length is determined from the deep elastic modulus that varies with the indentation depth corresponding to the second position point, and the stable and unchanging deep elastic modulus is taken as the second position elastic modulus corresponding to the second position point.

[0148] The rock elastic modulus prediction device provided in the embodiments of the present invention can execute the rock elastic modulus prediction method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0149] Example X

[0150] Figure 14 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0151] like Figure 14 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0152] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0153] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as methods for predicting the elastic modulus of rocks.

[0154] In some embodiments, the method for predicting the rock elastic modulus may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the rock elastic modulus prediction method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the rock elastic modulus prediction method by any other suitable means (e.g., by means of firmware).

[0155] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0156] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0157] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0158] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0159] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0160] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0161] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0162] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for predicting the elastic modulus of rock, characterized in that, include: During the nanoindentation test at at least two locations on the rock sample, the nanoindentation test data corresponding to each location point is acquired in real time. Determine the mineral component type corresponding to each location point; wherein, the mineral component type includes hard components and soft components; Based on the nanoindentation test data corresponding to each location point and the mineral composition type corresponding to each location point, the elastic modulus of the hard component and the elastic modulus of the soft component of the rock sample are determined. The rock elastic modulus of the rock sample is determined based on the elastic modulus of the hard component and the elastic modulus of the soft component.

2. The method according to claim 1, characterized in that, Before determining the rock elastic modulus of the rock sample based on the elastic modulus of the hard component and the elastic modulus of the soft component, the method further includes: Determine the volume content of the hard components in the rock sample; Determining the rock elastic modulus of the rock sample based on the elastic modulus of the hard component and the elastic modulus of the soft component includes: The bulk modulus of the hard component of the rock sample is determined based on the elastic modulus of the hard component, and the bulk modulus of the soft component of the rock sample is determined based on the elastic modulus of the soft component. The rock bulk modulus of the rock sample is determined based on the bulk modulus of the hard component, the bulk modulus of the soft component, and the volume content of the hard component. The rock elastic modulus of the rock sample is determined based on the rock bulk modulus.

3. The method according to claim 2, characterized in that, Before determining the rock bulk modulus of the rock sample based on the bulk modulus of the hard component, the bulk modulus of the soft component, and the volume content of the hard component, the method further includes: Determine the particle size of the hard component mineral at the hard component location point among the at least two location points, and calculate the average particle size of the hard component mineral at all hard component location points; wherein, the hard component location point is a location point where the mineral component type is hard component. The bonding surface modulus corresponding to each of the at least two location points is determined based on the nanoindentation test data, and the average bonding surface modulus of all location points is calculated. Intermediate variables are calculated based on the average particle size of the hard component minerals and the average surface modulus of the cementation surface. The rock bulk modulus of the rock sample is determined based on the bulk modulus of the hard component, the bulk modulus of the soft component, and the volume content of the hard component, including: The rock bulk modulus of the rock sample is determined based on the bulk modulus of the hard component, the bulk modulus of the soft component, the volume content of the hard component, and the intermediate variable.

4. The method according to claim 3, characterized in that, The nanoindentation test data includes the indentation depth and the depth elastic modulus as a function of the indentation depth; Determining the bonding surface modulus corresponding to each of the at least two location points based on the nanoindentation test data includes: For each of the at least two location points, the maximum depth elastic modulus and the stationary elastic modulus that remain unchanged with increasing indentation depth are determined based on the depth elastic modulus that varies with the indentation depth corresponding to the location point. The difference between the maximum depth elastic modulus and the steady elastic modulus is taken as the bonding surface modulus corresponding to the location point.

5. The method according to claim 1, characterized in that, The nanoindentation test data includes the indentation depth and the depth elastic modulus as a function of the indentation depth; Based on the nanoindentation test data corresponding to each location point and the mineral composition type corresponding to each location point, the elastic modulus of the hard component and the elastic modulus of the soft component of the rock sample are determined, including: Determine a first location point where the mineral component type is hard and a second location point where the mineral component type is soft from the at least two location points; Based on the depth elastic modulus corresponding to the first position point that varies with the indentation depth, determine the first position elastic modulus corresponding to the first position point, and based on the depth elastic modulus corresponding to the second position point that varies with the indentation depth, determine the second position elastic modulus corresponding to the second position point. The average value of the first position elastic modulus corresponding to all the first position points is taken as the hard component elastic modulus of the rock sample, and the average value of the second position elastic modulus corresponding to all the second position points is taken as the soft component elastic modulus of the rock sample.

6. The method according to claim 5, characterized in that, Based on the depth elastic modulus corresponding to the first position point that varies with the indentation depth, the first position elastic modulus corresponding to the first position point is determined, including: The maximum depth elastic modulus within a preset indentation depth range is determined from the depth elastic modulus that varies with the indentation depth corresponding to the first position point, and the maximum depth elastic modulus is used as the first position elastic modulus corresponding to the first position point.

7. The method according to claim 5, characterized in that, Based on the depth elastic modulus corresponding to the second position point that varies with the indentation depth, the second position elastic modulus corresponding to the second position point is determined, including: The stable and unchanging deep elastic modulus within a preset depth length is determined from the deep elastic modulus that varies with the indentation depth corresponding to the second position point, and the stable and unchanging deep elastic modulus is taken as the second position elastic modulus corresponding to the second position point.

8. A device for predicting the elastic modulus of rock, characterized in that, include: The test data acquisition module is used to acquire the nanoindentation test data corresponding to each location point in real time during the nanoindentation test at at least two locations on the rock sample. A mineral component type determination module is used to determine the mineral component type corresponding to each location point; wherein, the mineral component type includes hard components and soft components; The component elastic modulus determination module is used to determine the hard component elastic modulus and soft component elastic modulus of the rock sample based on the nanoindentation test data corresponding to each location point and the mineral component type corresponding to each location point. The rock elastic modulus determination module is used to determine the rock elastic modulus of the rock sample based on the elastic modulus of the hard component and the elastic modulus of the soft component.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the method for predicting the elastic modulus of rock according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method for predicting the rock elastic modulus according to any one of claims 1-7.

Images