Multi-mineral-phase digital core model construction and microreaction flow simulation method

Abstract

The invention discloses a multi-mineral-phase digital core model construction and microreaction flow simulation method, and belongs to the field of petroleum and natural gas extraction. The method comprises the following steps: carrying out image processing on a rock core image obtained based on X-ray computed tomography (CT), respectively carrying out identification and threshold segmentation on different minerals, and obtaining a data matrix; utilizing a grid generation tool to construct initialized grids, mapping the initialized grids one by one through the image data matrix, and calculating the content of each mineral in different grids; and combining the content data of each mineral to construct a multi-mineral-phase digital core model. And the contents of different mineral phases are introduced into a solid-phase continuity equation, and a microreaction flow simulation method is established. By combining a multi-mineral-phase digital core physical model and a microscopic reaction flow direct numerical simulation method, numerical simulation research can be carried out, sensitivity analysis is carried out on factors such as reaction rate and flow velocity, and a rock structure evolution law and a porosity-permeability change relationship are obtained.

Description

Technical Field

[0001] The present invention belongs to the field of oil and gas exploitation, and particularly relates to a method for constructing a multi-mineral-phase digital core model and simulating micro-reaction flow. Background Art

[0002] Reaction flow plays an important role in many fields such as oil and gas exploitation, geological sequestration of carbon dioxide, and geothermal energy development, involving flow, transport, and geochemical reactions. During the process of oil and gas development, acidification is a key measure to improve the recovery rate of carbonate reservoirs. Injecting acid dissolves the rock minerals near the wellbore to create more flow channels for oil and gas. During the geological sequestration of carbon dioxide in deep saline aquifers, the injected carbon dioxide exists in a supercritical state. The complex interfacial behavior between liquid-liquid and liquid-solid makes carbon dioxide trapped in the pore space in the form of droplets. Carbon dioxide will dissolve in the nearby salt water to form carbonic acid, thereby reducing the pH value of the salt water. Acid radicals will be transported to the rock mineral surface through convective diffusion, resulting in the occurrence of mineral dissolution and precipitation processes. These dynamic processes cause significant changes in the pore space, thereby changing the macroscopic properties of the reservoir, such as permeability, porosity, etc.

[0003] The flow and geochemical reaction processes underground are very slow. Whether it is to evaluate the acidification effect in improving the recovery rate of oil and gas reservoirs or to evaluate the sequestration safety in carbon dioxide geological sequestration, comprehensive research cannot be carried out only through indoor physical experiments. Based on microscopic numerical simulation, the three-dimensional distribution of fluids, ions, pressure, etc. in the pore space can be obtained, and the change of the solid phase structure of porous media with reactions can be explicitly observed. It is a bridge connecting the macroscopic scale and the microscopic scale. Conducting micro-reaction flow simulation helps to determine the occurrence conditions and criteria of dissolution and precipitation, quantify its influence on the seepage law, and provide basic parameters for macroscopic numerical simulation.

[0004] However, underground natural rock reservoirs do not contain only a single mineral, but usually contain multiple minerals. Sandstone and carbonate reservoirs are the most widely distributed reservoirs. Sandstone reservoirs are mainly composed of quartz and feldspar, containing some carbonate minerals and a small amount of clay minerals. Carbonate reservoirs are mainly limestone and dolomite. Limestone has calcite as the main component, containing a small amount of detrital minerals such as dolomite, quartz, and clay minerals; dolomite has dolomite as the main mineral, containing some quartz, feldspar, calcite, and clay minerals. These minerals have reaction rates that differ by several orders of magnitude, which greatly increases the difficulty of micro-reaction flow simulation. Therefore, accurately describing natural rocks containing multi-mineral phases and constructing a multi-mineral-phase digital rock model that can be used for micro-reaction flow simulation is of great significance for the engineering practice of safe and efficient geological carbon sequestration and improving the recovery rate of oil and gas reservoirs by acidification. Summary of the Invention

[0005] In view of the above technical problems existing in the prior art, the present invention proposes a method for constructing a multi-mineral-phase digital core model and simulating micro-reaction flow, which is reasonably designed, overcomes the deficiencies of the prior art, and has good effects.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for constructing a multi-mineral-phase digital core model and simulating micro-reaction flow includes the following steps:

[0008] Step 1: Read the core image data and perform image processing;

[0009] Step 2: Identify the mineral components according to the different gray values of the rock image and perform threshold segmentation to obtain the core image data matrix;

[0010] Step 3: Construct an initial grid using a grid generation tool according to the core image size;

[0011] Step 4: Initialize the three-dimensional arrays of different minerals;

[0012] Step 5: Read the core image matrix, traverse each pixel point of the core image in turn, and accumulate the attributes for assignment;

[0013] According to the values in the core image matrix, accumulate the corresponding attributes into the three arrays of porosity, calcite, and dolomite;

[0014] Step 6: Combine the data of each mineral content to construct a multi-mineral-phase three-dimensional digital core model;

[0015] Step 7: Based on the constructed multi-mineral-phase three-dimensional digital core model, introduce various mineral contents into the mineral mass conservation equation in the direct numerical simulation method of micro-reaction flow, and conduct numerical simulation research on micro-reaction flow.

[0016] Preferably, in Step 1, it specifically includes the following steps:

[0017] Step 1.1: Read the X-ray computed tomography (CT) core image data to determine the image size and resolution;

[0018] Step 1.2: Perform noise reduction, filtering, and connectivity processing on the core image;

[0019] Remove the noise in the image through noise reduction processing, and improve the smoothness and contrast of the image through filtering processing.

[0020] Preferably, in Step 2, the core image data matrix includes the total number and distribution of pore voxels, the total number and distribution of rock skeleton voxels, and the total number and distribution information of pixels of various minerals.

[0021] Preferably, in step 3, a grid generation tool is used to initialize the background grid as a uniform Cartesian grid.

[0022] Preferably, in step 4, the initialized grid is mapped one by one through the image data matrix to calculate the content of each mineral in different grids.

[0023] Preferably, in step 5, the core image matrix is a list containing the phase types of each pixel point.

[0024] Preferably, in step 6, when the number of initialized Cartesian grids is the same as the number of core image voxels, the grid properties are the same as the properties of each voxel in the core image; when the grid consists of multiple pixels, the properties of each pixel are accumulated and divided by the number of pixels to calculate the average property value of each grid.

[0025] The beneficial technical effects brought by the present invention:

[0026] The method of the present invention introduces the content of different mineral phases into the solid-phase continuity equation, and then establishes a microscopic reaction flow simulation method that can dynamically characterize the dissolution of multiple mineral phases; by combining the multi-mineral-phase digital core physical model and the microscopic reaction flow direct numerical simulation method, numerical simulation research can be carried out to conduct sensitivity analysis on factors such as reaction rate and flow velocity, and obtain the rock structure evolution law and the relationship between porosity and permeability changes. Brief Description of the Drawings

[0027] Figure 1 is a flow chart of the method of the present invention;

[0028] Figure 2 is a schematic diagram of a three-dimensional digital core based on CT scanning;

[0029] Figure 3 is a schematic diagram of the image (pores, calcite, dolomite) after threshold segmentation;

[0030] Figure 4 is a schematic diagram of the traversal assignment of digital core data;

[0031] Figure 5 is a schematic diagram of a multi-mineral-phase three-dimensional digital core model;

[0032] Figure 6 is a flow chart of numerical solution. Detailed Embodiments

[0033] The present invention will be further described in detail below in conjunction with the drawings and specific embodiments:

[0034] A method for constructing a multi-mineral-phase digital core model and simulating microscopic reaction flow, the process of which is as Figure 1 shown, including the following steps:

[0035] Step 1: Read core image data and perform image processing.

[0036] Read X-ray computed tomography (CT) core image data to determine the image size and resolution. To improve the core image quality, perform noise reduction and filtering on the image while retaining the image information. Noise reduction is to remove the noise in the image, and filtering is to improve the smoothness and contrast of the image. For cores with low permeability or poor pore-permeability properties, connectivity processing is particularly important. The pores in such cores may be very small and scattered, making it difficult to form effective fluid flow paths. Therefore, appropriate connectivity processing can be carried out to facilitate subsequent flow simulation.

[0037] Figure 2 It is a schematic diagram of a 3D digital core based on CT scanning.

[0038] Step 2: Identify pores and multiple mineral types based on different gray values of the rock image.

[0039] As Figure 3 shown, taking carbonate rocks containing calcite and dolomite as an example, perform threshold segmentation on multiple minerals according to the gray scale of the rock image, that is, set the pore phase to 0, calcite to 1, and dolomite to 2. If the rock also contains other minerals, such as quartz, clay, etc., higher numbers can continue to be used for identification, for example, quartz is 3 and clay is 4, and so on. Obtain the core image data matrix, which contains information such as the total number and distribution of pore voxels, the total number and distribution of rock skeleton voxels, and the total number and distribution of pixels of various minerals.

[0040] Step 3: Set the generated grid size and quantity according to the core image size.

[0041] Use the grid generation tool to initialize the background grid as a uniform Cartesian grid to make the calculation simpler and more efficient. Taking the open-source computational fluid dynamics simulation software OpenFOAM (Open Field Operation and Manipulation) as an example, its grid generation tool blockMesh can be used to create a parameterized grid. The principle is to decompose the domain geometry into one or more three-dimensional hexahedral blocks. The edges of the blocks can be straight lines, arcs, or splines. The grid is essentially specified by specifying the number of cells in each direction of each block, and blockMesh is sufficient to generate the grid data. Each geometric block is defined by 8 vertices, and each vertex is located at the corner of a hexahedron.

[0042] Step 4: Initialize three three-dimensional arrays (porosity, calcite, dolomite) to store the specific properties of each cell, and all their elements are first set to 0. The dimension of the array is n x 、ny , n z , which respectively represent the number of cells in the x, y, and z directions.

[0043] Step 5: Read the core image matrix, traverse each pixel of the image in sequence, and accumulate the corresponding attributes into the three arrays of porosity, calcite, and dolomite according to the values in the core image matrix. Here, the core image matrix is a list containing the phase types of each pixel. For example, it can indicate whether a pixel is a pore, calcite, or dolomite.

[0044] Figure 4 Schematic diagram for assigning values to traverse digital core data.

[0045] Step 6: When the number of initialized Cartesian grids is the same as the number of voxels in the core image, the grid attributes are the same as the attributes of each voxel in the core image. When a grid consists of multiple pixels, accumulate the attributes of each pixel and divide by the number of pixels to calculate the average attribute value of each grid. Combine the data of the three arrays of porosity, calcite, and dolomite and the Cartesian grid to construct a multi-mineral-phase three-dimensional digital core model.

[0046] Step 7: Based on the constructed multi-mineral-phase three-dimensional digital core model, introduce various mineral contents into the mineral mass conservation equation in the microscopic reaction flow direct numerical simulation method, and numerical simulation research can be carried out. Taking the microscopic-continuum scale method based on the Darcy-Brinkman-Stokes (DBS) equation as an example, the equation describing the flow is the DBS equation:

[0047]

[0048] where ρ f is the liquid density; is the local average pressure; is the local average velocity; μ f is the kinematic viscosity of the fluid; k is the local permeability.

[0049] Figure 5 Schematic diagram of the multi-mineral-phase three-dimensional digital core model.

[0050] By the volume averaging method, distinguish the porous medium region and the void region according to the void volume fraction in each control volume V. When , the control volume is a void region, and the Navior-Stokes (N-S) is used to describe the flow process. When is between 0 and 1, the control volume contains a part of voids and a part of rock minerals, which is a porous medium region. In the porous medium region, Darcy's law is used to describe the flow process.

[0051] When there is no solid phase in the control volume, i.e., the solid volume fraction is zero, it is completely reduced to the classical pore-scale hydrodynamic equation, and then pore-scale simulation research is carried out. When using the micro-continuum method to conduct pore-scale research, the control volume V represents a computational grid, represents the pore space, represents the rock minerals, and represents the fluid / solid interface between 0 and 1.

[0052] Multiple mineral phase types are described by the mineral volume fraction Y s,i : Y s,i (x, y, z, t), i ∈ [1, N s , where N s represents the number of minerals contained in the rock. In the microscopic reactive flow simulation, the mineral mass conservation equation is:

[0053]

[0054] The fluid mass conservation is:

[0055]

[0056] where ρ s,i is the density of rock mineral i; m s,i is the liquid / solid mass exchange term.

[0057] When considering the reaction of chemical component A with rock minerals, its concentration evolves as

[0058]

[0059] where C f,A is the concentration of chemical component A; D A is the effective diffusion coefficient of component A.

[0060] The above control equations can be directly discretized and solved by the finite volume method (FVM) or the finite element method (FEM).

[0061] Figure 6 is the numerical solution flow chart.

[0062] By setting parameters such as reaction rate and diffusion coefficient under reservoir temperature, pressure, and salinity conditions, the present invention can carry out reactive flow simulation research in real cores, analyze the influence laws of flow and reaction parameters on rock structure evolution and the porosity-permeability change relationship under different conditions through the Péclet number and Damköhler number, upgrade the reaction rate scale to the continuous scale, and provide theoretical guidance for engineering practices such as acidizing for enhanced oil recovery in oil and gas reservoirs and geological carbon sequestration.

[0063] Certainly, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions, or substitutions made by those skilled in the art within the scope of the essence of the present invention should also fall within the protection scope of the present invention.

Claims

1. A method for constructing a multi-mineral phase digital core model and simulating microscopic reactive flow, characterized in that: The steps include: Step 1: Read core image data and perform image processing; Step 2: According to the different gray values ​​of the rock image, identify the mineral components and perform threshold segmentation to obtain the core image data matrix; Step 3: Use the mesh generation tool to construct the initial mesh according to the core image size; Step 4: Initialize the three-dimensional array of different minerals; Step 5: Read the core image matrix, traverse each pixel of the core image in turn, and accumulate attributes for assignment; According to the values ​​in the core image matrix, the corresponding attributes are accumulated into the three arrays of porosity, calcite, and dolomite; Step 6: Combine the content data of each mineral to construct a three-dimensional digital core model of multiple mineral phases; Step 7: Based on the constructed multi-mineral phase three-dimensional digital core model, various mineral contents are introduced into the mineral mass conservation equation in the direct numerical simulation method of microscopic reactive flow to conduct numerical simulation research on microscopic reactive flow.

2. The method for constructing a multi-mineral phase digital core model and simulating microscopic reactive flow according to claim 1, characterized in that: Step 1 specifically includes the following steps: Step 1.1: Read the X-ray computed tomography core image data and determine the image size and resolution; Step 1.2: De-noise, filter and connectivize the core image; Noise reduction is used to remove noise from the image, and filtering is used to improve the smoothness and contrast of the image.

3. The method for constructing a multi-mineral phase digital core model and simulating microscopic reactive flow according to claim 1, characterized in that: In step 2, the core image data matrix includes the total number and distribution of pore voxels, the total number and distribution of rock skeleton voxels, and the total number and distribution of pixels of various minerals.

4. The method for constructing a multi-mineral phase digital core model and simulating microscopic reactive flow according to claim 1, characterized in that: In step 3, the background grid is initialized as a uniform Cartesian grid using a grid generation tool.

5. The method for constructing a multi-mineral phase digital core model and simulating microscopic reactive flow according to claim 1, characterized in that: In step 4, the initialized grids are mapped one by one through the image data matrix to calculate the content of each mineral in different grids.

6. The method for constructing a multi-mineral phase digital core model and simulating microscopic reactive flow according to claim 1, characterized in that: In step 5, the core image matrix is ​​a list containing the phase type of each pixel.

7. The method for constructing a multi-mineral phase digital core model and simulating microscopic reactive flow according to claim 4, characterized in that: In step 6, when the number of initialized Cartesian grids is the same as the number of voxels in the core image, the grid attributes are the same as the attributes of each voxel in the core image; when the grid is composed of multiple pixels, the attributes of each pixel are accumulated and divided by the number of pixels to calculate the average attribute value of each grid.

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