Method and system for constructing cross-scale multi-component pore network model of shale media

Abstract

The present invention discloses a method and system for constructing a cross-scale multi-component pore network model of a shale medium, which obtains microstructure images of shale at different resolution scales; extracts pore and fracture structure characteristic parameters from low-resolution images, and characterizes the distribution and content of different mineral components; constructs a pore network model corresponding to the low-resolution image based on the extracted data; extracts pore structure characteristic parameters of corresponding scales from high-resolution images, characterizes the mineral components around the pores, marks the pore types, and obtains a pore network model corresponding to the high-resolution image; adds the pore network model corresponding to the high-resolution image to the pore network model corresponding to the low-resolution image, and integrates the pore and fracture structure characteristic parameters at different resolutions; cross-scales connects pores at different resolutions based on constraints, and constructs a cross-scale multi-component pore network model that reflects the real pore and fracture structure characteristics of shale.

Description

Technical Field

[0001] The present invention relates to the technical field of shale network model construction, and in particular to a method and system for constructing a cross-scale multi-component pore network model of a shale medium. Background Art

[0002] The statements in this section merely mention background art related to the present invention and do not necessarily constitute prior art.

[0003] Tight reservoirs have complex and diverse composition and structure, and show heterogeneity and anisotropy in physical properties. The pore structure is complex and diverse, with widespread micro-nano pore throats, and very low porosity and permeability. Due to the characteristics of micro-nano porous structure, the material migration in the rock mass is complex and has significant multi-scale effects. Therefore, accurately evaluating the microscopic pore structure characteristics and seepage of shale is the focus of unconventional oil reservoirs.

[0004] Conventional microscopic pore throat characterization methods can no longer meet the requirements of fine characterization research on the millimeter-micrometer-submicrometer-nanometer pore throat structure system of tight shale reservoirs. Image analysis methods such as micro-nano X-CT scanning technology and focused ion beam scanning electron microscopy (FIB-SEM) have become the main means of finely characterizing the microscopic pore structure of tight shale reservoirs. Although high-resolution images can characterize finer pore structures, there is a contradiction between imaging resolution and field of view, and it is difficult to obtain high-resolution and large-size images at the same time. Due to the high heterogeneity of shale and the limitations of current imaging technology, all relevant features cannot be resolved at a single scale, so multi-scale imaging and integration between different scales are important conditions for accurately characterizing shale characteristics. Summary of the invention

[0005] In order to address the deficiencies of the prior art, the present invention provides a method and system for constructing a cross-scale multi-component pore network model of shale media, so as to solve the problem that the multi-scale and multi-component characteristics of shale cannot be effectively characterized.

[0006] On the one hand, a method for constructing a cross-scale multi-component pore network model of shale media is provided, including:

[0007] Obtain microstructural images of shale at different resolution scales;

[0008] Extract pore and fracture structural characteristic parameters, as well as the distribution and content of different mineral components from low-resolution images; construct a pore network model corresponding to the low-resolution image based on the extracted data;

[0009] Extract the pore structure characteristic parameters of the corresponding scale from the high-resolution image, characterize the mineral composition around the pores, mark the pore types according to the mineral composition around the pores, and obtain the pore network model corresponding to the high-resolution image;

[0010] The pore network model corresponding to the high-resolution image is added to the pore network model corresponding to the low-resolution image to integrate the characteristic parameters of pore and fracture structures at different resolutions. During the integration process, pores at different resolutions are connected across scales based on constraints to construct a cross-scale multi-component pore network model. Based on the cross-scale multi-component pore network model, the shale microstructure is visualized. Among them, the constraint condition is the relationship between total porosity and mineral component content.

[0011] On the other hand, a system for constructing a cross-scale multi-component pore network model of shale media is provided, including:

[0012] An acquisition module, which is configured to: acquire microstructure images of shale at different resolution scales;

[0013] A low-resolution model building module is configured to: extract pore and fracture structure characteristic parameters, as well as distribution and content characterization of different mineral components from the low-resolution image; and build a pore network model corresponding to the low-resolution image based on the extracted data;

[0014] A high-resolution model building module is configured to: extract pore structure characteristic parameters of corresponding scales from the high-resolution image, characterize the mineral composition around the pores, mark the pore types according to the mineral composition around the pores, and obtain a pore network model corresponding to the high-resolution image;

[0015] The integration module is configured as follows: adding the pore network model corresponding to the high-resolution image to the pore network model corresponding to the low-resolution image, and integrating the pore and fracture structure characteristic parameters at different resolutions; in the integration process, cross-scale connection of pores at different resolutions is performed based on constraints to construct a cross-scale multi-component pore network model; based on the cross-scale multi-component pore network model, visualization of shale microstructure is achieved; wherein the constraint condition is the relationship between total porosity and mineral component content.

[0016] On the other hand, there is also provided an electronic device, comprising:

[0017] a memory for non-transitory storage of computer-readable instructions; and

[0018] a processor for executing the computer readable instructions,

[0019] When the computer-readable instructions are executed by the processor, the method described in the first aspect is executed.

[0020] On the other hand, a storage medium is provided, which non-temporarily stores computer-readable instructions, wherein when the non-temporary computer-readable instructions are executed by a computer, the instructions of the method described in the first aspect are executed.

[0021] On the other hand, a computer program product is provided, comprising a computer program, wherein the computer program is used to implement the method described in the first aspect when running on one or more processors.

[0022] The above technical solution has the following advantages or beneficial effects:

[0023] Using a variety of image analysis methods, we obtain microstructure images of shales of different sizes, i.e., images of shales at millimeter-micrometer-submicrometer-nanometer scales, spanning four orders of magnitude, and construct a cross-scale multi-component pore network model for shale media to comprehensively study the heterogeneous and complex microstructure and pore system of microfractured shales. This model takes into account the heterogeneity of intermediate scale integration, more accurately characterizes shale reservoir space, simulates the real microscopic pore structure of shale, and provides an important basis for subsequent flow simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0025] Figure 1 It is a flow chart of the method for constructing a cross-scale multi-component pore network model for shale media;

[0026] Figure 2 Schematic diagram of pore cross-scale connection methods at different resolutions. DETAILED DESCRIPTION

[0027] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0028] Terminology explanation:

[0029] Pore ​​network model: It is a porous media model established by simplifying the geometric shape and spatial connection relationship of the real core pore space. It is also a basic platform for studying pore-level microscopic seepage theory and microstructural characteristics. In rocks, the larger pore space is regarded as the pore body, which is generally represented by a sphere in the pore network model; and the narrow space connecting the pore body is regarded as the throat, which is represented by a cylinder with equal cross section in the pore network model.

[0030] Threshold: is a gray level or pixel value used in image processing to segment an image into different regions.

[0031] As a basic platform for the study of pore-level microscopic seepage theory, the pore network model provides an important research method for studying the flow of fluids in porous media at the microscopic pore scale. The pore network model can reproduce complex pore spaces, reduce computational and experimental costs, shorten the experimental data acquisition cycle, and obtain experimental data that is difficult to measure in the laboratory. Based on this, in order to cope with the high heterogeneity of shale and the limitations of current imaging technology, and to accurately characterize the organic pores, inorganic pores, and microcracks in the shale reservoir space, the present invention constructs a cross-scale multi-component pore network model for shale media, laying the foundation for subsequent flow mechanism simulation.

[0032] Embodiment 1

[0033] like Figure 1 As shown, this embodiment provides a method for constructing a cross-scale multi-component pore network model of shale media, including:

[0034] S101: Acquire microstructure images of shale at different resolution scales;

[0035] S102: Extract pore and fracture structure characteristic parameters, as well as distribution and content characterization of different mineral components from the low-resolution image; construct a pore network model corresponding to the low-resolution image based on the extracted data;

[0036] S103: extracting pore structure characteristic parameters of corresponding scale from the high-resolution image, characterizing the mineral composition around the pores, marking the pore types according to the mineral composition around the pores, and obtaining a pore network model corresponding to the high-resolution image;

[0037] S104: Add the pore network model corresponding to the high-resolution image to the pore network model corresponding to the low-resolution image, and integrate the characteristic parameters of pore and fracture structures at different resolutions; in the integration process, cross-scale connection is performed on pores at different resolutions based on constraints to construct a cross-scale multi-component pore network model; based on the cross-scale multi-component pore network model, visualization of shale microstructure is achieved; wherein the constraint condition is the relationship between total porosity and mineral component content.

[0038] Furthermore, the S101: obtains microstructure images of shale at different resolution scales, including: shale images at millimeter scale, micrometer scale, submicrometer scale and nanometer scale; regards the shale images at millimeter scale, micrometer scale and submicrometer scale as low-resolution images, and regards the shale images at nanometer scale as high-resolution images.

[0039] For example, the Haynesville shale, which is a relatively common reservoir, was selected, and a variety of image analysis methods were used, including X-CT scanning and focused ion beam scanning electron microscopy (FIB-SEM) technology, to obtain microstructure images of shales of different sizes in order of resolution from low to high, namely, shale images of millimeter, micrometer, submicrometer and nanometer scales.

[0040] Furthermore, the S102: constructing a pore network model corresponding to the low-resolution image based on the extracted data specifically includes:

[0041] The pore space whose size meets the set threshold range is regarded as the pore body, the pore body is represented by a sphere, the cracks connecting the pore body are regarded as the throat, and the throat is represented by a cylinder with equal cross-section, so as to obtain the pore network model corresponding to the low-resolution image.

[0042] Furthermore, in S102: the pore network model corresponding to the low-resolution image includes pores, fractures and mineral components; in the pore network model corresponding to the low-resolution image, inorganic matter positions and organic matter positions are marked.

[0043] For example, the low-resolution image is processed, and through a series of image processing processes such as size cropping, noise reduction filtering, and threshold segmentation, the characteristic parameters of the larger pores and fracture structures in the shale, as well as the distribution and content of different mineral components, are extracted, and a pore and fracture network model reflecting the mineral composition at the corresponding scale of low resolution is constructed to determine the size of the characterization unit of the shale reservoir. The representative positions in the low-resolution image (such as inorganic matter positions, organic matter positions, etc.) are calibrated.

[0044] Furthermore, the S103: extracting pore structure characteristic parameters of corresponding scales from the high-resolution image, characterizing the mineral components around the pores, marking the pore types according to the mineral components around the pores, and obtaining a pore network model corresponding to the high-resolution image; specifically includes:

[0045] The pore space whose size meets the set threshold range is regarded as the pore body, and the pore body is represented by a sphere. The cracks connecting the pore body are regarded as the throat, and the throat is represented by a cylinder with equal cross-section. The characteristic parameters of the pore structure of the corresponding scale are extracted from the high-resolution image, and the mineral composition around the pore is characterized. According to the mineral composition around the pore, the pore type is marked to obtain the pore network model corresponding to the high-resolution image.

[0046] Furthermore, the S103: extracting pore structure characteristic parameters of corresponding scales from the high-resolution image to characterize the mineral components around the pores includes:

[0047] The characteristic parameters of pore structure that meet the set size threshold range are extracted to characterize the mineral composition around the pores.

[0048] Furthermore, the S103: marking the pore type according to the mineral composition around the pore, includes:

[0049] If the mineral composition around the pore is diverse or unclear, the pore is assumed to be an organic pore, and the pore structure characteristics of the organic pore are marked as "1" and the inorganic pore structure characteristics are marked as "0".

[0050] For example, a high-resolution image analysis method is used for the micro-pore space that cannot be characterized under low-resolution conditions to extract the characteristic parameters of the pore structure of smaller sizes, and to characterize the mineral composition around the pores. Different types of pores are marked according to the mineral composition around the pores.

[0051] Furthermore, in the S104: in the integration process, pores at different resolutions are connected across scales based on constraint conditions to construct a cross-scale multi-component pore network model that reflects the real pore and fracture structure characteristics of shale, including:

[0052] According to the marking result of the pore network model corresponding to the low-resolution image, the position of the pore network model corresponding to the high-resolution image is determined;

[0053] Calculate the distance between the outermost pores of the pore network model in the high-resolution image and the pores of the pore network model in the low-resolution image;

[0054] Prioritize connecting the outermost pores of the pore network model corresponding to the high-resolution image with the nearest pores in the pore network model corresponding to the low-resolution image, and calculate the volume fractions of different types of pores in the connected model;

[0055] If the volume fractions of different types of pores in the connected model do not meet the set constraints, the current connection line is deleted, and the outermost pores of the pore network model corresponding to the high-resolution image are connected to the next closest pore in the pore network model corresponding to the low-resolution image;

[0056] If the volume fractions of different types of pores in the connected model meet the set constraints, the current connection line will be retained.

[0057] It should be understood that the effective connection between the pore network model under the high-resolution image and the pore network model under the low-resolution image can be achieved through the constraint conditions. The constraint conditions determine the volume fraction of different types of pores in the pore network model corresponding to the high-resolution image in the pore network model corresponding to the low-resolution image.

[0058] Furthermore, the constraints include:

[0059] V all =Vf +V1+V2

[0060] Among them, V all Represents the total porosity, V f represents the volume fraction of cracks, V1 represents the volume fraction of organic pores, and V2 represents the volume fraction of inorganic pores;

[0061] V1=(1-ω1)×V organic

[0062] V2=(1-ω2)×V inorganic

[0063] Among them, ω1 represents the proportion of organic matter content, ω2 represents the proportion of inorganic matter content, and V organic represents the volume of organic matrix, V inorganic Represents the volume of inorganic matrix.

[0064] Schematic diagram of pore cross-scale connection methods at different resolutions, such as Figure 2 As shown, the solid circles in the figure represent fracture pores, the hollow circles represent pores in the constructed network model, the thick solid lines represent fracture throats, the thin solid lines represent throats in the constructed network model, and the dotted lines represent cross-scale throats.

[0065] The cross-scale multi-component pore network model can distinguish different types of pores, more accurately characterize shale reservoir space, comprehensively utilize image data from a variety of image analysis methods, accurately characterize shale media, and simulate the real microscopic pore structure of shale, providing an important basis for subsequent flow simulation.

[0066] Embodiment 2

[0067] This embodiment provides a system for constructing a cross-scale multi-component pore network model of shale media;

[0068] The construction system of the cross-scale multi-component pore network model of shale media includes:

[0069] An acquisition module, which is configured to: acquire microstructure images of shale at different resolution scales;

[0070] A low-resolution model building module is configured to: extract pore and fracture structure characteristic parameters, as well as distribution and content characterization of different mineral components from the low-resolution image; and build a pore network model corresponding to the low-resolution image based on the extracted data;

[0071] A high-resolution model building module is configured to: extract pore structure characteristic parameters of corresponding scales from the high-resolution image, characterize the mineral composition around the pores, mark the pore types according to the mineral composition around the pores, and obtain a pore network model corresponding to the high-resolution image;

[0072] The integration module is configured as follows: adding the pore network model corresponding to the high-resolution image to the pore network model corresponding to the low-resolution image, and integrating the pore and fracture structure characteristic parameters at different resolutions; in the integration process, cross-scale connection of pores at different resolutions is performed based on constraints to construct a cross-scale multi-component pore network model; based on the cross-scale multi-component pore network model, visualization of shale microstructure is achieved; wherein the constraint condition is the relationship between total porosity and mineral component content.

[0073] It should be noted that the acquisition module, the low-resolution model construction module, the high-resolution model construction module and the integration module correspond to steps S101 to S104 in Embodiment 1, and the examples and application scenarios implemented by the modules and the corresponding steps are the same, but are not limited to the contents disclosed in Embodiment 1. It should be noted that the modules as part of the system can be executed in a computer system such as a set of computer executable instructions.

[0074] The description of each embodiment in the above embodiments has different emphases. For parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0075] The proposed system can be implemented in other ways. For example, the system embodiment described above is only illustrative, and the division of the modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0076] Embodiment 3

[0077] This embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, and the one or more computer programs are stored in the memory. When the electronic device is running, the processor executes the one or more computer programs stored in the memory so that the electronic device executes the method described in the above embodiment one.

[0078] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0079] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0080] In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in a processor or an instruction in the form of software.

[0081] The method in the first embodiment can be directly embodied as a hardware processor, or a combination of hardware and software modules in the processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.

[0082] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with this embodiment 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.

[0083] Embodiment 4

[0084] This embodiment further provides a computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the method described in the first embodiment is completed.

[0085] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for constructing a cross-scale multi-component pore network model of shale media, characterized by: include: Obtain microstructural images of shale at different resolution scales; Extract pore and fracture structural characteristic parameters, as well as the distribution and content of different mineral components from low-resolution images; construct a pore network model corresponding to the low-resolution image based on the extracted data; Extract the pore structure characteristic parameters of the corresponding scale from the high-resolution image, characterize the mineral composition around the pores, mark the pore types according to the mineral composition around the pores, and obtain the pore network model corresponding to the high-resolution image; The pore network model corresponding to the high-resolution image is added to the pore network model corresponding to the low-resolution image, and the characteristic parameters of pore and fracture structures at different resolutions are integrated; in the integration process, the pores at different resolutions are connected across scales based on constraints to construct a cross-scale multi-component pore network model; based on the cross-scale multi-component pore network model, the visualization of shale microstructure is achieved; among which, the constraint condition is the relationship between total porosity and mineral component content; The constraints include: V all =V f +V1+V2 Among them, V all Represents the total porosity, V f represents the volume fraction of cracks, V1 represents the volume fraction of organic pores, and V2 represents the volume fraction of inorganic pores; V1=(1-ω1)×V organic V2=(1-ω2)×V inorganic Among them, ω1 represents the proportion of organic matter content, ω2 represents the proportion of inorganic matter content, and V organic represents the volume of organic matrix, V inorganic Represents the volume of inorganic matrix.

2. The method for constructing a cross-scale multi-component pore network model of shale media according to claim 1, characterized in that: The microstructure images of shale at different resolution scales are obtained, including millimeter scale, micrometer scale, submicrometer scale and nanometer scale shale images; the millimeter scale, micrometer scale and submicrometer scale shale images are regarded as low-resolution images, and the nanometer scale shale images are regarded as high-resolution images.

3. The method for constructing a cross-scale multi-component pore network model of shale media according to claim 1, characterized in that: Based on the extracted data, a pore network model corresponding to the low-resolution image is constructed, including: The pore space whose size meets the set threshold range is regarded as the pore body, the pore body is represented by a sphere, the cracks connecting the pore body are regarded as the throat, and the throat is represented by a cylinder with equal cross-section, so as to obtain the pore network model corresponding to the low-resolution image.

4. The method for constructing a cross-scale multi-component pore network model of shale media according to claim 1, characterized in that: The pore network model corresponding to the low-resolution image includes pores, cracks and mineral components; in the pore network model corresponding to the low-resolution image, the inorganic matter position and the organic matter position are marked.

5. The method for constructing a cross-scale multi-component pore network model of shale media according to claim 1, characterized in that: During the integration process, pores at different resolutions are connected across scales based on constraints to construct a cross-scale multi-component pore network model, including: According to the marking result of the pore network model corresponding to the low-resolution image, the position of the pore network model corresponding to the high-resolution image is determined; Calculate the distance between the outermost pores of the pore network model in the high-resolution image and the pores of the pore network model in the low-resolution image; Prioritize connecting the outermost pores of the pore network model corresponding to the high-resolution image with the nearest pores in the pore network model corresponding to the low-resolution image, and calculate the volume fractions of different types of pores in the connected model; If the volume fractions of different types of pores in the connected model do not meet the set constraints, the current connection line is deleted, and the outermost pores of the pore network model corresponding to the high-resolution image are connected to the next closest pore in the pore network model corresponding to the low-resolution image; If the volume fractions of different types of pores in the connected model meet the set constraints, the current connection line will be retained.

6. A system for constructing a cross-scale multi-component pore network model of shale media, characterized by: include: An acquisition module, which is configured to: acquire microstructure images of shale at different resolution scales; A low-resolution model building module is configured to: extract pore and fracture structure characteristic parameters, as well as distribution and content characterization of different mineral components from the low-resolution image; and build a pore network model corresponding to the low-resolution image based on the extracted data; A high-resolution model building module is configured to: extract pore structure characteristic parameters of corresponding scales from the high-resolution image, characterize the mineral composition around the pores, mark the pore types according to the mineral composition around the pores, and obtain a pore network model corresponding to the high-resolution image; The integration module is configured to: add the pore network model corresponding to the high-resolution image to the pore network model corresponding to the low-resolution image, and integrate the pore and fracture structure characteristic parameters at different resolutions; in the integration process, cross-scale connection is performed on pores at different resolutions based on constraint conditions to construct a cross-scale multi-component pore network model; based on the cross-scale multi-component pore network model, the visualization of shale microstructure is realized; wherein the constraint condition is the relationship between total porosity and mineral component content; The constraints include: V all =V f +V1+V2 Among them, V all Represents the total porosity, V f represents the volume fraction of cracks, V1 represents the volume fraction of organic pores, and V2 represents the volume fraction of inorganic pores; V1=(1-ω1)×V organic V2=(1-ω2)×V inorganic Among them, ω1 represents the proportion of organic matter content, ω2 represents the proportion of inorganic matter content, and V organic represents the volume of organic matrix, V inorganic Represents the volume of inorganic matrix.

7. An electronic device, comprising: a memory for non-transitory storage of computer readable instructions; as well as a processor for executing the computer readable instructions, When the computer-readable instructions are executed by the processor, the method described in any one of claims 1 to 5 is executed.

8. A storage medium characterized by being non-temporary The computer-readable instructions are stored, wherein when the non-transitory computer-readable instructions are executed by a computer, the instructions of the method according to any one of claims 1 to 5 are executed.

9. A computer program product, characterized in that The invention comprises a computer program, which is used to implement the method according to any one of claims 1 to 5 when running on one or more processors.

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