Method and device for predicting microscopic rock mechanical characteristic parameters of tight sandstone reservoir

Abstract

The invention discloses a method and a device for predicting microscopic rock mechanics characteristic parameters of a compact sandstone reservoir. The method comprises the following steps: acquiring Young modulus of rock debris; lithology and particle size parameters; porosity and pore throat parameters; and mineral composition; the Young modulus serves as a target parameter, the lithology, the particle size parameter, the porosity, the pore throat parameter and the mineral composition serve as input parameters, a rock debris microscopic rock mechanics model is established, and predicted rock mechanics characteristic parameters are output. And prediction of microscopic rock mechanics parameters of the rock debris is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field, and in particular relates to a method and a device for predicting microscopic rock mechanical characteristic parameters of a tight sandstone reservoir. Background Art

[0002] my country has abundant tight oil and gas reserves and good development potential, but the development is difficult. Inspired by the effective development of shale gas reservoirs, the practice of fracture network fracturing has been carried out in tight oil and gas reservoirs. In fracture network fracturing, the brittleness of the rock is an important parameter for judging the ability of reservoir fracturing to form a fracture network. At present, the most commonly used mineral brittleness index and dynamic rock mechanics parameter methods for calculating rock brittleness in the field of oil and gas development in the petroleum industry are summaries of the experience of shale gas fracturing in the United States. The rock brittleness in the field of rock mechanics is mainly calculated based on the stress-strain curve and mechanical characteristics of rock indoor tests. It has not been combined with the field of oil and gas development and is difficult to use in field applications.

[0003] At present, the oil and gas resources that can be developed on a large scale are mainly shale oil and gas, coalbed methane and tight sandstone gas, and the corresponding reservoirs are mud shale, coal seam and tight sandstone. The rock mechanical characteristics affect the reservoir transformation, such as the direction, length and shape of the fracturing cracks, so the accurate characterization of rock mechanical characteristics is the key to the success of fracturing.

[0004] In the prior art, rock mechanical characteristic parameters are usually measured by pressure testing machines, but most testing machines can only provide three-axis pressure control adjustment changes, making it difficult to control the type of fracturing cracks in specific experiments, and unable to achieve the expected cracking effect of the experiment, resulting in a small test measurement range and strong destructiveness to the samples; at the same time, for rock samples containing different gas concentrations, it is difficult to adjust the gas injection in the samples again, resulting in gas leakage before the experiment, which is lower than the ideal value of the experiment. Since coring operations in some formations are time-consuming and labor-intensive, and development wells are not cored, it is difficult to obtain macroscopic mechanical parameters of the reservoir, and the rock cuttings contain microscopic mineral composition, pore structure and physical parameters. These characteristic parameters determine the microscopic mechanical characteristics of the rock, and to a certain extent, also determine the macroscopic mechanical properties of the reservoir rock.

[0005] Related patent 1: "Correction and prediction method of static mechanical parameters of rocks under reservoir confining pressure conditions" CN108195669A. This patent predicts macroscopic rock mechanical parameters.

[0006] Related Patent 2: "A method for predicting deep-ultra-deep rock mechanical parameters considering temperature effects" CN111537332A. This patent also predicts macroscopic rock mechanical parameters based on triaxial compression tests.

[0007] As a means of measuring microscopic mechanical properties, micro-nano indentation test breaks through the limitation of core size requirements in macroscopic mechanical tests. Only 0.5 cm rock cuttings samples are needed to carry out the test, and the core cost is relatively low. This method makes it easier to obtain continuous mechanical parameters of the reservoir. Summary of the invention

[0008] The technical problem to be solved by the present invention is to provide a method and device for predicting microscopic rock mechanical characteristic parameters of tight sandstone reservoirs in view of the deficiencies in the above-mentioned prior art, so as to solve the technical problem of inaccurate prediction of microscopic rock mechanical parameters of existing rock fragments.

[0009] The present invention adopts the following technical solutions:

[0010] A method for predicting microscopic rock mechanical characteristic parameters of a tight sandstone reservoir comprises the following steps:

[0011] Obtain Young's modulus of rock fragments; lithology, particle size parameters; porosity and pore throat parameters; and mineral composition;

[0012] Taking Young's modulus as the target parameter, lithology, particle size parameters, porosity and pore throat parameters, and mineral composition as input parameters, a microscopic rock mechanics model of rock cuttings is established and the predicted rock mechanics characteristic parameters are output.

[0013] Specifically, the Young's modulus of the rock cuttings is obtained as follows:

[0014] The rock cuttings samples were embedded with resin, polished to expose the test surface, cut into blocks and placed on the micro-indentation test bench;

[0015] Use the diamond indenter of the micro-nano indentation tester to press into the surface of the rock cuttings, collect the indentation depth, continuously record the load size and depth information of the indented sample, and draw a load-depth curve;

[0016] Based on the obtained load-depth curve, the Young's modulus of the specimen was obtained using the Oliver-Pharr method.

[0017] Furthermore, the thickness of the block is less than or equal to 5 mm.

[0018] Specifically, the lithology and particle size parameters of the cuttings are obtained as follows:

[0019] The samples were observed under normal transmitted light and orthogonal polarized light to make qualitative evaluations of lithology, mineral composition, particle size and sorting characteristics.

[0020] Specifically, the porosity and pore throat parameters of the cuttings are obtained as follows:

[0021] Micrometer CT scanning was performed on the rock cuttings samples, and the scanned images were denoised and threshold segmented to obtain the pore-skeleton model;

[0022] The maximum sphere algorithm is used to count the pores in the pore-skeleton model to obtain the porosity and pore throat parameters of the sample.

[0023] Based on the obtained sample porosity and pore throat parameters, the sample permeability was simulated by the lattice Boltzmann method.

[0024] Furthermore, the resolution of CT scans is 1.5 μm.

[0025] Specifically, the mineral composition of the obtained rock cuttings is as follows:

[0026] By performing X-ray diffraction on the sample, a series of diffraction angle characteristics and corresponding intensities of the sample are obtained. The mineral type is determined by the diffraction angle characteristics, and the content is determined by the diffraction intensity.

[0027] Specifically, the establishment of the microscopic rock mechanics model of rock fragments is as follows:

[0028] Evaluate the correlation of each input parameter, clean and reduce the dimension of the input parameters;

[0029] The Young's modulus obtained from the micron indentation test of all rock cuttings samples is used as the target parameter. The big data method is used to establish the relationship between the input parameters and the target parameters, and the rock cuttings micro-rock mechanics model is obtained. The trained rock cuttings micro-rock mechanics model is used to output the predicted rock mechanics characteristic parameters.

[0030] Furthermore, the microscopic rock mechanics model of rock fragments is trained as follows:

[0031] 70% of the samples were randomly selected to train the rock cuttings micro-rock mechanics model, and then the trained rock cuttings micro-rock mechanics model was used to calculate the input parameters of the remaining 30% groups of samples to obtain the Young's modulus. The rock mechanics characteristic parameters were predicted by comparing the calculated Young's modulus with the Young's modulus obtained by micro-nano indentation experiments.

[0032] In a second aspect, an embodiment of the present invention provides a device for predicting microscopic rock mechanical characteristic parameters of a tight sandstone reservoir, comprising:

[0033] The data module obtains the Young's modulus of the rock fragments; lithology and particle size parameters; porosity and pore throat parameters; and mineral composition;

[0034] The prediction module takes Young's modulus as the target parameter, lithology, particle size parameters, porosity and pore throat parameters, and mineral composition as input parameters, establishes a microscopic rock mechanics model of rock cuttings and outputs the predicted rock mechanics characteristic parameters.

[0035] Compared with the prior art, the present invention has at least the following beneficial effects:

[0036] A method for predicting characteristic parameters of microscopic rock mechanics of tight sandstone reservoirs is proposed. Thin section analysis is performed to obtain parameters such as rock properties and particle size of cuttings, micrometer CT scanning is performed to obtain porosity, permeability and pore throat parameters of cuttings, and XRD testing is performed to obtain mineral composition of cuttings. Then, the thin section test results of cuttings, physical properties of micrometer CT scanning and XRD mineral composition of cuttings are used as input parameters, and Young's modulus of micro-nano indentation is used as target parameter. A decision tree big data analysis method is used to establish a microscopic rock mechanics model of cuttings, thereby realizing the prediction of microscopic rock mechanics parameters of cuttings.

[0037] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic diagram of the process of the present invention;

[0039] Figure 2 A schematic diagram of an embodiment of the present invention;

[0040] Figure 3 A schematic diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0043] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an" and "the" are intended to include plural forms unless the context clearly indicates otherwise.

[0044] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0045] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0046] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0047] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0048] The present invention provides a method for predicting characteristic parameters of microscopic rock mechanics of tight sandstone reservoirs. The method comprises the following steps: thin section analysis is performed to obtain parameters such as rock properties and particle size of cuttings; micrometer CT scanning is performed to obtain porosity, permeability and pore throat parameters of cuttings; and XRD testing is performed to obtain mineral composition of cuttings. Then, the thin section test results of cuttings, physical property parameters of micrometer CT scanning and XRD mineral composition of cuttings are used as input parameters, Young's modulus of micro-nano indentation is used as target parameter, and a decision tree big data analysis method is adopted to establish a microscopic rock mechanics model of cuttings, thereby realizing the prediction of microscopic rock mechanics parameters of cuttings. The present invention provides a new idea for obtaining microscopic rock mechanics parameters.

[0049] The present invention provides a method for predicting microscopic rock mechanical characteristic parameters of a tight sandstone reservoir, comprising the following steps:

[0050] S1. Conduct micro-nano indentation test on rock cuttings to obtain Young's modulus;

[0051] S101, embed the rock cuttings sample with resin, polish it flat to expose the test surface, cut it into small pieces with a thickness of no more than 5 mm and place them on the micron indentation test bench;

[0052] S102, the diamond indenter of the micro-nano indentation tester is pressed into the surface of the rock cuttings, the indentation depth is collected by the displacement sensor, the load size and depth information of the pressed sample are continuously recorded, and the load-depth curve is drawn;

[0053] S103. The Oliver-Pharr method is used to obtain the Young's modulus of the sample.

[0054] S2. Conduct thin section analysis on rock cuttings to obtain parameters such as lithology and grain size;

[0055] The samples were observed under normal transmitted light and orthogonal polarized light, and qualitative evaluations were made on the lithology, mineral composition, particle size, sorting and other characteristics.

[0056] S3, conduct micron CT scanning on the cuttings to obtain porosity and pore throat parameters;

[0057] S301, performing micrometer CT scanning (resolution 1.5 μm) on the rock cuttings sample, performing denoising and threshold segmentation on the scanned image, and obtaining a pore-skeleton model;

[0058] S302, using the maximum sphere algorithm to perform statistics on the pore part in the pore-skeleton model to obtain the porosity and pore throat parameters of the sample;

[0059] S303. The sample permeability is obtained by simulating the lattice Boltzmann method.

[0060] S4. Conduct whole-rock mineral testing on the cuttings to obtain the mineral composition;

[0061] By performing X-ray diffraction (XRD) on the sample, a series of diffraction angle characteristics and corresponding intensities of the sample are obtained. The mineral type is determined by the diffraction angle characteristics, and the content is determined by the diffraction intensity.

[0062] S5. Establish a micro rock mechanics model.

[0063] S501, evaluating the correlation of each input parameter, removing input parameters with strong correlation, and performing cleaning and dimensionality reduction processing on the input parameters;

[0064] S502, after the input parameters are determined, the Young's modulus obtained by the micron indentation test of all rock cuttings samples is used as the target parameter, and the relationship between the input parameters and the target parameters is established by using the big data method (decision tree algorithm in machine learning), that is, the rock cuttings microscopic rock mechanics model. The specific steps are as follows:

[0065] 70% of the samples are randomly selected for input and output training. The trained model calculates the output results (i.e., predicts Young's modulus) for the remaining 30% groups of samples using input parameters, so that the Young's modulus predicted by the model is closest to the Young's modulus obtained by micro-nano indentation experiments.

[0066] In yet another embodiment of the present invention, a device for predicting microscopic rock mechanical characteristic parameters of a tight sandstone reservoir is provided, and the device can be used to implement the above-mentioned method for predicting microscopic rock mechanical characteristic parameters of a tight sandstone reservoir. Specifically, the device for predicting microscopic rock mechanical characteristic parameters of a tight sandstone reservoir includes a data module and a prediction module.

[0067] The data module obtains the Young's modulus of the rock fragments, the lithology and particle size parameters, the porosity and pore throat parameters, and the mineral composition.

[0068] The prediction module takes Young's modulus as the target parameter, lithology, particle size parameters, porosity and pore throat parameters, and mineral composition as input parameters, establishes a microscopic rock mechanics model of rock cuttings and outputs the predicted rock mechanics characteristic parameters.

[0069] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the method for predicting the characteristic parameters of microscopic rock mechanics of tight sandstone reservoirs, including:

[0070] Obtain the Young's modulus, lithology, particle size parameters, porosity and pore throat parameters, and mineral composition of the rock cuttings; use the Young's modulus as the target parameter, and the lithology, particle size parameters, porosity and pore throat parameters, and mineral composition as input parameters to establish a microscopic rock mechanics model of the rock cuttings and output the predicted rock mechanics characteristic parameters.

[0071] In another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating device of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory (Non-Volatile Memory), such as at least one disk memory.

[0072] The processor may load and execute one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the method for predicting microscopic rock mechanical characteristic parameters of tight sandstone reservoirs in the above embodiment; the processor may load and execute the following steps:

[0073] Obtain the Young's modulus, lithology, particle size parameters, porosity and pore throat parameters, and mineral composition of the rock cuttings; use the Young's modulus as the target parameter, and the lithology, particle size parameters, porosity and pore throat parameters, and mineral composition as input parameters to establish a microscopic rock mechanics model of the rock cuttings and output the predicted rock mechanics characteristic parameters.

[0074] See also Figure 3 The computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the method for calculating the fluid composition in the reservoir transformation wellbore in the embodiment is realized. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the device for predicting microscopic rock mechanical characteristic parameters of a tight sandstone reservoir in the embodiment are realized. To avoid repetition, it is not described one by one here.

[0075] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 3 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0076] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0077] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the computer device 60.

[0078] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0079] In order to make the purpose, 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 in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0080] Example

[0081] See also Figure 2 , taking the workflow of obtaining rock mechanical parameters of a well cuttings as an example:

[0082] 1. First, carry out basic rock cutting tests, conduct micron CT scanning, polarized microscopic testing and XRD testing of the target rock cuttings, and obtain the 13 parameters required for the microscopic rock mechanics calculation model;

[0083] 2. The micro rock mechanics model of rock fragments established by big data method (decision tree algorithm in machine learning) is used to calculate the micro rock mechanics parameters;

[0084] 3. Comparing the microscopic Young's modulus obtained by the big data model with the dynamic rock modulus data calculated by logging data, it can be seen that the two are well consistent, which confirms the accuracy of the results obtained by the method of the present invention.

[0085] The Young's modulus predicted by big data modeling is compared with the micro-nano Young's modulus. The Young's modulus predicted by the big data model is consistent with the Young's modulus obtained by the micron indentation experiment, and the correlation coefficient R 2 It is 0.853, which indicates that the established rock mechanics model has good accuracy.

[0086] In summary, the method and device for predicting microscopic rock mechanical characteristic parameters of tight sandstone reservoirs of the present invention can obtain rock mechanical parameters from rock cuttings data. Compared with the conventional core test acquisition method and logging calculation method, the accuracy is higher than 85%. This opens up a new way to obtain rock mechanical parameters, which is of great significance for wells that cannot be cored and wells that cannot obtain rock mechanical parameters due to incomplete data collection. This makes it possible for such complex wells to obtain rock mechanical parameters using the technology of the present invention and then realize reservoir engineering parameter evaluation. The present invention fills the gap that such wells cannot carry out formation engineering parameters, which is of great significance for the exploration and development of oil and gas wells.

[0087] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0088] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0089] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0090] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0091] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0092] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0093] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0094] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (apparatus), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0095] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0096] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0097] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A method for predicting microscopic rock mechanical characteristic parameters of tight sandstone reservoirs, characterized in that: The following steps are involved: Obtain Young's modulus of rock fragments; lithology, particle size parameters; porosity and pore throat parameters; and mineral composition; Taking Young's modulus as the target parameter, lithology, particle size parameters, porosity and pore throat parameters, and mineral composition as input parameters, a microscopic rock mechanics model of rock cuttings is established and the predicted rock mechanics characteristic parameters are output.

2. The method for predicting microscopic rock mechanical characteristic parameters of tight sandstone reservoir according to claim 1, characterized in that: The Young's modulus of the cuttings is obtained as follows: The rock cuttings samples were embedded with resin, polished to expose the test surface, cut into blocks and placed on the micro-indentation test bench; Use the diamond indenter of the micro-nano indentation tester to press into the surface of the rock cuttings, collect the indentation depth, continuously record the load size and depth information of the indented sample, and draw a load-depth curve; Based on the obtained load-depth curve, the Young's modulus of the specimen was obtained using the Oliver-Pharr method.

3. The method for predicting microscopic rock mechanical characteristic parameters of tight sandstone reservoir according to claim 2, characterized in that: The thickness of the block is less than or equal to 5 mm.

4. The method for predicting microscopic rock mechanical characteristic parameters of tight sandstone reservoir according to claim 1, characterized in that: The lithology and particle size parameters of the cuttings are as follows: The samples were observed under normal transmitted light and orthogonal polarized light to make qualitative evaluations of lithology, mineral composition, particle size and sorting characteristics.

5. The method for predicting microscopic rock mechanical characteristic parameters of tight sandstone reservoir according to claim 1, characterized in that: The porosity and pore throat parameters of the cuttings are obtained as follows: Micrometer CT scanning was performed on the rock cuttings samples, and the scanned images were denoised and threshold segmented to obtain the pore-skeleton model; The maximum sphere algorithm is used to count the pores in the pore-skeleton model to obtain the porosity and pore throat parameters of the sample. Based on the obtained sample porosity and pore throat parameters, the sample permeability was simulated by the lattice Boltzmann method.

6. The method for predicting microscopic rock mechanical characteristic parameters of tight sandstone reservoir according to claim 5, characterized in that: The resolution of CT scans is 1.5 μm.

7. The method for predicting microscopic rock mechanical characteristic parameters of tight sandstone reservoir according to claim 1, characterized in that: The mineral composition of the obtained cuttings is as follows: By performing X-ray diffraction on the sample, a series of diffraction angle characteristics and corresponding intensities of the sample are obtained. The mineral type is determined by the diffraction angle characteristics, and the content is determined by the diffraction intensity.

8. The method for predicting microscopic rock mechanical characteristic parameters of tight sandstone reservoir according to claim 1, characterized in that: The establishment of the microscopic rock mechanics model of rock cuttings is as follows: Evaluate the correlation of each input parameter, clean and reduce the dimension of the input parameters; The Young's modulus obtained from the micron indentation test of all rock cuttings samples is used as the target parameter. The big data method is used to establish the relationship between the input parameters and the target parameters, and the rock cuttings micro-rock mechanics model is obtained. The trained rock cuttings micro-rock mechanics model is used to output the predicted rock mechanics characteristic parameters.

9. The method for predicting microscopic rock mechanical characteristic parameters of tight sandstone reservoir according to claim 8, characterized in that: The specific training of the rock cuttings micro-rock mechanics model is as follows: 70% of the samples were randomly selected to train the rock cuttings micro-rock mechanics model, and then the trained rock cuttings micro-rock mechanics model was used to calculate the input parameters of the remaining 30% groups of samples to obtain the Young's modulus. The rock mechanics characteristic parameters were predicted by comparing the calculated Young's modulus with the Young's modulus obtained by micro-nano indentation experiments.

10. A device for predicting microscopic rock mechanical characteristic parameters of tight sandstone reservoirs, characterized in that: include: Data module, respectively obtains the Young's modulus of the rock cuttings; Lithology and grain size parameters; porosity and pore throat parameters; and mineral composition; The prediction module takes Young's modulus as the target parameter, lithology, particle size parameters, porosity and pore throat parameters, and mineral composition as input parameters, establishes a microscopic rock mechanics model of rock cuttings and outputs the predicted rock mechanics characteristic parameters.

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