A method for classifying the permeability of tight reservoir pores

Abstract

The application discloses a kind of classification methods of dense reservoir pore percolation capacity.Geological engineering, oil and gas field development and rock physics technical field are involved, including obtaining dense reservoir rock sample, and polishing treatment is carried out to the surface of dense reservoir rock sample, dense reservoir rock sample is scanned using field emission scanning electron microscope, and high-resolution micro-pore structure SEM image is obtained;High-resolution micro-pore structure SEM image is imported into image processing software, and image segmentation method based on machine learning is used to train classifier to distinguish pore space in high-resolution micro-pore structure SEM image and mineral matrix background, and generate binary pore image;Based on binary pore image, the geometric parameters of each independent pore are measured, and the geometric parameters at least include pore area S and pore perimeter C;According to pore area S and pore perimeter C, define the percolation capacity evaluation index of single pore as (S / C) 4 ;Set key threshold, and divide pore into different grades.

Description

Technical Field

[0001] This invention relates to the fields of geological engineering, oil and gas field development and rock physics, and more specifically to a method for classifying the porosity and permeability of tight reservoirs. Background Technology

[0002] Currently, the International Union of Pure and Applied Chemistry (IUPAC) classifies pores solely based on pore size. This classification standard is widely adopted, providing a unified scale for comparing different porous materials. In addition, in the field of petroleum geology, pores are often classified according to their origin (e.g., intergranular pores, intragranular pores, dissolution pores, fractures, etc.) or their positional relationship with rock particles.

[0003] However, the IUPAC classification relies solely on the one-dimensional dimensional parameter of "pore size." In reality, the pore morphology in reservoirs, especially tight reservoirs such as shale, is extremely complex, often exhibiting flattened, slotted, or irregular shapes. The geometric complexity of pores cannot be adequately described using only "equivalent diameter" or "pore size," and pore shape (such as the ratio of perimeter to area) has a decisive influence on fluid flow resistance.

[0004] In tight reservoirs, macroscopic permeability is often controlled by a very small number of dominant flow channels (such as highly conductive microfractures or large pore throats). Traditional pore size distribution analysis cannot effectively identify and quantify the contribution of these key channels from a vast amount of pores, resulting in a lack of accurate microscopic basis for predicting production capacity and designing fracturing schemes.

[0005] Therefore, how to provide a classification method that can directly and quantitatively correlate the microscopic geometric characteristics of pores with their macroscopic seepage function is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] In view of the above problems, the present invention is proposed to provide a method for classifying the porosity and permeability of tight reservoirs to overcome or at least partially solve the above problems.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, embodiments of the present invention provide a method for classifying the porosity and permeability of tight reservoirs, characterized in that it includes: S1. Obtain tight reservoir rock samples and polish the surface of the tight reservoir rock samples. Use field emission scanning electron microscopy to scan the tight reservoir rock samples and obtain high-resolution SEM images of the micropore structure. S2. Image processing is performed on the high-resolution microporous structure SEM image. A machine learning-based image segmentation method is used to train a classifier to distinguish the pore space from the mineral matrix background in the high-resolution microporous structure SEM image and generate a binarized pore image. S3. Based on the binarized pore image, measure the geometric parameters of each independent pore, including at least the pore area S and the pore perimeter C; S4. Based on the pore area S and pore perimeter C, the seepage capacity evaluation index for a single pore is defined as (S / C). 4 ; S5. The evaluation index for seepage capacity based on a single pore is (S / C). 4 Set a critical threshold and classify the pores into different levels based on the critical threshold.

[0008] Furthermore, the polishing method for the surface of the tight reservoir rock sample in S1 is argon ion polishing.

[0009] Furthermore, the specific steps for defining the seepage capacity evaluation index of a single pore in S4 are as follows: Based on Hagen-Poiseuille's law:

[0010] in Δp It is the pressure difference; d i The diameter is the capillary tube diameter. L H The length of the capillary tube; μ For fluid dynamic viscosity; q This represents the volumetric flow rate of the fluid in the pores. From the above equation, we can see that the volumetric flow rate q of the fluid in the pores is related to the capillary diameter. d i It is directly proportional to the fourth power; High-resolution SEM images of microscopic pore structures are two-dimensional interfaces. d i for: d i =S / C Therefore, the seepage capacity evaluation index for a single pore is defined as (S / C). 4 .

[0011] Furthermore, the key thresholds in S5 include: First threshold 1×10 -6 Second threshold 1×10 -5 .

[0012] Furthermore, the porosity is further classified into different levels, specifically including: When (S / C) 4 <1×10 -6 At that time, the pores were those with low seepage potential. When 1×10 -6 ≤(S / C) 4 <1×10 -5 At that time, it was a medium-permeability pore; When (S / C) 4 ≥1×10 -5 At that time, it is a pore with high seepage potential.

[0013] Furthermore, it also includes statistical analysis: counting the number and proportion of low-permeability-potential pores, medium-permeability-potential pores, and high-permeability-potential pores in tight reservoir rock samples.

[0014] Secondly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon for executing the classification method for the porosity permeability of tight reservoirs as described in the first aspect. Thirdly, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the classification method for the porosity permeability of tight reservoirs described in the first aspect. The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following: This invention provides a method for classifying the porosity and permeability of tight reservoirs, based on (area / perimeter). 4 Pores are classified into three categories: low-permeability potential pores, medium-permeability potential pores, and high-permeability potential pores. This method is primarily used for quantitative characterization and dynamic analysis of the pore structure of porous media (especially low-maturity oil shale) under different environmental and experimental conditions, systematically revealing the intrinsic relationship between microscopic pore structure and macroscopic fluid transport capacity. This invention not only provides a new quantitative method for pore classification of porous media but also offers theoretical basis and technical support for the efficient exploration and development of oil and gas resources. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0016] Figure 1 This is an overall flowchart of the method provided in the embodiments of the present invention; Figure 2This is a pore classification statistical chart provided in an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] This invention discloses a method for classifying the porosity and permeability of tight reservoirs, such as... Figure 1 As shown, it includes: S1. Obtain tight reservoir rock samples and polish the surface of the tight reservoir rock samples. Use field emission scanning electron microscopy to scan the tight reservoir rock samples and obtain high-resolution SEM images of the micropore structure. S2. Image processing is performed on the high-resolution microporous structure SEM image. A machine learning-based image segmentation method is used to train a classifier to distinguish the pore space from the mineral matrix background in the high-resolution microporous structure SEM image and generate a binarized pore image. S3. Based on the binarized pore image, measure the geometric parameters of each independent pore, including at least the pore area S and the pore perimeter C; S4. Based on the pore area S and pore perimeter C, the seepage capacity evaluation index for a single pore is defined as (S / C). 4 ; S5. The evaluation index for seepage capacity based on a single pore is (S / C). 4 Set a critical threshold and classify the pores into different levels based on the critical threshold.

[0019] The specific implementation of this invention is as follows: (1) Sample acquisition and image acquisition A tight reservoir rock sample to be tested was selected, and the sample surface was polished with argon ion. The sample was then scanned using a field emission scanning electron microscope (FE-SEM) to obtain high-resolution images of the micropore structure.

[0020] (2) Image processing and aperture recognition The acquired scanning electron microscope (SEM) images were imported into the image processing software ImageJ, and the machine learning-based image segmentation plugin Trainable Weka Segmentation was used. By training a classifier, the pore space and mineral matrix in the image were distinguished, realizing the automatic identification and extraction of complex pore structures and generating binary pore images.

[0021] (3) Geometric parameter extraction Using the measurement function of image processing software, the geometric parameters of each identified individual pore are measured to obtain two key parameters: pore area S: the projected area of ​​a single pore on a two-dimensional image; pore perimeter C: the length of the edge contour of a single pore.

[0022] (4) Constructing seepage capacity evaluation indicators Based on the Hagen-Poiseuille equation, the volumetric flow rate q of a fluid in a pore is related to the characteristic size d of the pore. i It is proportional to the fourth power. Considering the relationship between the hydraulic radius of pores and S and C in a two-dimensional SEM image, the seepage capacity evaluation index of a single pore is defined as (S / C). 4 This index integrates the size of the pore in both one-dimensional and two-dimensional dimensions, and can more intuitively reflect the contribution of a single pore to fluid transport under the same fluid medium and pressure difference.

[0023] (5) Pore classification scheme Based on the statistical distribution characteristics of a large amount of sample data, two key thresholds of 1×10⁻⁶ are set. -6 and 1×10 -5 The porosity is divided into three levels: ① when (S / C) 4 <1×10 -6 When the pore size is 1×10⁻⁶, it is considered a "low permeability potential pore"; ② When the pore size is 1×10⁻⁶, it is considered a "low permeability potential pore". -6 ≤(S / C) 4 <1×10 -5 When the flow potential is medium, it is considered a "medium-permeability pore"; ③ When (S / C) 4 ≥1×10 -5 When the porosity is high, it is considered a "high permeability potential pore".

[0024] The following explanation uses a low-maturity oil shale sample as an example: A tight oil shale core was selected and polished with argon ion, then its microstructure was imaged using a field emission scanning electron microscope (FE-SEM). The images were imported into ImageJ software, and pores were extracted using the Trainable Weka Segmentation plugin. For each pore, the software automatically measured its area and perimeter. Subsequently, the seepage capacity index (S / C) was calculated according to the formula proposed in this invention. 4 After noise removal, a total of 3735 independent pore sample points were extracted and measured. These 3735 pores were then classified and statistically analyzed using the threshold determined in this invention, and the results are as follows: Figure 2 As shown.

[0025] Statistics show that low-permeability potential pores dominate, accounting for as much as 67.29% of the total, indicating that the vast majority of pores in this oil shale are extremely small or flat in shape (large perimeter, small area), contributing very little directly to macroscopic fluid flow and primarily serving a reservoir function rather than a transport function. Medium-permeability potential pores account for a moderate proportion, forming the background value for reservoir matrix permeability, and may participate in flow under large pressure differentials. High-permeability potential pores account for only 4.31%, and although scarce, their (S / C) ratio is significant. 4 Large pore values ​​(i.e., large hydraulic radius) mean that, according to the Hagen-Poiseuille law, the flow rate per pore in these pores far exceeds that of low-permeability pores. These pores are the main seepage channels for oil and gas production. This classification result quantitatively reveals the microscopic mechanism of tight reservoirs having "large reservoir space but poor seepage capacity," meaning that although the total number of pores is large, the number of high-permeability potential pores is extremely small. This result provides a theoretical basis for the design of subsequent in-situ oil shale exploitation schemes. It is necessary to artificially increase the proportion of high-permeability potential pores and fractures, or to convert low-permeability pores into high-permeability pores, through artificial fracturing or heat treatment, in order to achieve efficient development.

[0026] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0027] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for classifying the porosity and permeability of tight reservoirs, characterized in that, include: S1. Obtain a tight reservoir rock sample and polish the surface of the tight reservoir rock sample. Use a field emission scanning electron microscope to scan the tight reservoir rock sample and obtain a high-resolution SEM image of the micropore structure. S2. The high-resolution microporous structure SEM image is processed by using a machine learning-based image segmentation method to train a classifier to distinguish the pore space from the mineral matrix background in the high-resolution microporous structure SEM image and generate a binarized pore image. S3. Based on the binarized pore image, measure the geometric parameters of each individual pore, wherein the geometric parameters include at least the pore area S and the pore perimeter C; S4. Based on the pore area S and pore perimeter C, the seepage capacity evaluation index for a single pore is defined as (S / C). 4 ; S5. The evaluation index for the seepage capacity of a single pore is (S / C). 4 Set key thresholds and classify the pores into different levels based on these key thresholds.

2. The method as described in claim 1, characterized in that, The polishing method for the surface of tight reservoir rock samples described in S1 is argon ion polishing.

3. The method as described in claim 1, characterized in that, The specific steps for defining the seepage capacity evaluation index of a single pore as described in S4 are as follows: Based on Hagen-Poiseuille's law: in Δp It is the pressure difference; d i The diameter is the capillary tube diameter. L H The length of the capillary tube; μ For fluid dynamic viscosity; q This represents the volumetric flow rate of the fluid in the pores. From the above equation, we can see that the volumetric flow rate q of the fluid in the pores is related to the capillary diameter. d i It is directly proportional to the fourth power; The high-resolution SEM image of the micropore structure is a two-dimensional interface. d i for: d i =S / C Therefore, the seepage capacity evaluation index for a single pore is defined as (S / C). 4 .

4. The method as described in claim 1, characterized in that, The key thresholds mentioned in S5 include: First threshold 1×10 -6 Second threshold 1×10 -5 .

5. The method as described in claim 4, characterized in that, The classification of porosity into different levels specifically includes: When (S / C) 4 <1×10 -6 At that time, the pores were those with low seepage potential. When 1×10 -6 ≤(S / C) 4 <1×10 -5 At that time, it was a medium-permeability pore; When (S / C) 4 ≥1×10 -5 At that time, it is a pore with high seepage potential.

6. The method as described in claim 1, characterized in that, Also includes: Statistical analysis: The number and proportion of low-permeability potential pores, medium-permeability potential pores, and high-permeability potential pores in the tight reservoir rock samples were statistically analyzed.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements a method for classifying the porosity permeability of tight reservoirs as described in any one of claims 1 to 6.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements a method for classifying the porosity permeability of tight reservoirs as described in any one of claims 1 to 6.

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