Topological structure-based ultra-deep crack plugging layer mechanical structure evaluation method

Abstract

The invention discloses an ultra-deep crack plugging layer mechanical structure evaluation method based on a topological structure, and relates to the technical field of leakage control. The method comprises the following steps that reservoir conditions of a well leakage position are obtained, and a plugging layer of a plugging formula to be evaluated is obtained in combination with an indoor plugging experiment; cT scanning is carried out on the plugging layer, and the geometric center and the particle size of the particle material are obtained through a digital image processing technology; judging whether particles are in contact or not based on the geometric center and the particle size of the particle material, and constructing a particle material contact network topology model according to the contact relationship; and calculating the matching property, the particle node betweenness, the network betweenness centrality and the clustering coefficient of the particle contact network based on the particle material contact network topology model, and evaluating the plugging layer based on the parameters. The method disclosed by the invention is relatively simple and rapid, the core characteristics of the mechanical structure of the plugging layer can be extracted by directly analyzing the particle contact network representing the geometric skeleton of the plugging layer, and the evaluation process is more direct and efficient.

Description

Technical Field

[0001] This invention relates to the field of leakage control technology, specifically to a method for evaluating the mechanical structure of ultra-deep crack sealing layers based on topology. Background Technology

[0002] In the drilling and development of ultra-deep and unconventional oil and gas resources and geothermal resources, drilling fluid loss is a common and costly technical problem. Especially when drilling into naturally developed deep fractured formations, the working fluid can leak along the fractures under pressure differential. To effectively seal the fractures and restore wellbore pressure integrity, engineers often add plugging materials to the drilling fluid, allowing them to bridge and accumulate within the fractures to form a "plugging layer" with a certain load-bearing capacity. The mechanical stability of this plugging layer directly determines the success or failure of the plugging operation; its instability can lead to re-leakage, seriously affecting drilling safety, efficiency, and economy.

[0003] Currently, obtaining information on the micromechanical structure of sealing layers mainly relies on two indirect and costly technical methods: one is discrete element numerical simulation, which, while capable of reconstructing particle motion and force chain evolution, heavily depends on complex model construction, precise microscopic parameter calibration, and enormous computational resources; the other is advanced physical observation techniques such as photoelastic experiments, which, although capable of visually displaying force chains, require expensive specialized equipment, transparent simulation materials, and complex experimental preparation, making them difficult to apply quickly in field or engineering design. Both methods are cumbersome and time-consuming, and their results are highly dependent on specific assumptions or experimental conditions, making them unsuitable for direct and rapid evaluation and comparative analysis of the mechanical structure of actual sealing materials or field conditions.

[0004] Therefore, there is an urgent need for a method that can directly and quickly evaluate the mechanical structure of the sealing layer without the need for complex simulations or specialized experiments. Summary of the Invention

[0005] To address at least one of the aforementioned problems, this invention provides a method for evaluating the mechanical structure of ultra-deep crack sealing layers based on topology.

[0006] The technical solution of this invention to solve the above problems is as follows: a method for evaluating the mechanical structure of ultra-deep crack sealing layers based on topology, comprising the following steps: S1. Obtain the reservoir conditions of the well leakage zone and, in conjunction with indoor plugging experiments, obtain the plugging layer of the plugging formula to be evaluated, wherein the plugging formula to be evaluated includes particulate materials. S2. Perform a CT scan on the sealing layer to obtain a photograph of the particulate material inside the sealing layer. Use digital image processing technology to obtain the geometric center and particle size of the particulate material. S3. Based on the geometric center and particle size of the particulate material, determine whether the particles are in contact, and construct a topological model of the particulate material contact network according to the contact relationship. S4. Based on the topology model of the particle contact network, calculate the matching property, particle node betweenness, network betweenness centrality, and clustering coefficient of the particle contact network, and evaluate the sealing layer based on the aforementioned parameters: nodes with a cumulative particle node betweenness distribution of more than 95% are strong chain structures, and the greater the matching property, the greater the network betweenness centrality, and the smaller the clustering coefficient, the worse the stability of the sealing layer.

[0007] In one embodiment of the present invention, in S1, the reservoir conditions are in-situ stress, formation pressure, and target bearing capacity.

[0008] One embodiment of the present invention is that, in S3, when the particles come into contact, the following condition is met: In the formula, x i , y i Particles i and granules j The coordinates of the center point, r i , r j Particles i and granules j The radius is denoted by Δ, where Δ represents the image recognition error.

[0009] One embodiment of the present invention is as follows: the method for constructing the topology model of the particulate material contact network is as follows: taking a single particulate material as a node and the contact relationship between particles as an edge, all contact relationships are transformed into network connection relationships.

[0010] In one embodiment of the present invention, in S4, the mechanical structure of the sealing layer is evaluated in descending order of priority: matching, network betweenness centrality, and clustering coefficient.

[0011] The beneficial effects of this invention are as follows: It fundamentally changes the evaluation approach, eliminating the need for discrete element mechanical simulations that rely on numerous microscopic parameters and enormous computational resources, and also eliminating the need for expensive and complex specialized observation equipment such as photoelastic experiments. The core of this method lies in the computational analysis of static network topology data, whose computational complexity is far lower than that of dynamic mechanical simulations. By directly analyzing the particle contact network that characterizes the geometric skeleton of the sealing layer, the core features of its mechanical structure can be extracted, making the evaluation process more direct and efficient. Attached Figure Description

[0012] Figure 1 A diagram illustrating the evolution of network compatibility for leak-sealing formulations under different pressures; Figure 2 Correlation diagram of high-junction particles and strong-chain particles in the contact network of the sealing layer particles; Figure 3 Diagram showing the betweenness centrality of the particle contact network in the sealing layer and the stress concentration in the sealing layer; Figure 4 This is a correlation diagram showing the relationship between the clustering coefficient of the contact network of the sealing layer particles and the number of strong chains. Detailed Implementation

[0013] The specific embodiments of the present invention will be clearly and completely described below with reference to examples. Obviously, the described examples are only some embodiments of the present invention, and not all embodiments.

[0014] A method for evaluating the mechanical structure of ultra-deep crack sealing layers based on topology includes the following steps: S1. Obtain the reservoir conditions of the well leakage zone and, in conjunction with indoor plugging experiments, obtain the plugging layer of the plugging formula to be evaluated, wherein the plugging formula to be evaluated includes particulate materials. In this step, obtaining the reservoir conditions of the well leakage zone is mainly to facilitate the indoor plugging experiment, so that the indoor plugging experiment is carried out under the well leakage zone conditions, and the final results are more accurate.

[0015] The reservoir conditions include in-situ stress, formation pressure, and target bearing capacity, as well as reservoir temperature and other conditions. In-situ stress and formation pressure are used to simulate the indoor plugging experiment, while the target bearing capacity is mainly used for the screening of plugging formulas to be evaluated.

[0016] Meanwhile, since the embodiments of the present invention are used to evaluate particulate materials, the plugging formulation to be evaluated is a conventional plugging formulation, which typically includes rigid particles and elastic particles.

[0017] S2. Perform a CT scan on the sealing layer to obtain a photograph of the particulate material inside the sealing layer. Use digital image processing technology to obtain the geometric center and particle size of the particulate material. Digital image processing techniques typically include the following steps: image preprocessing, used to enhance the contrast between the particulate material and the background, thereby reducing noise; particle segmentation, which mainly involves segmenting each pixel of the particulate material from the background to generate a binary image or label image; and measurement and characterization, which involves measuring the segmented binary image or label image to obtain the size and position of each particulate material. For each of these three steps, existing techniques can be used. For example, for image preprocessing, existing operations such as Gaussian filtering, median filtering, and histogram equalization can be used; for particle segmentation, existing operations such as full threshold segmentation and Canny edge detection can be used; and for measurement and characterization, three-dimensional coordinates are typically used for auxiliary representation. These methods are all conventional in the field, and those skilled in the art can choose appropriate methods to process them.

[0018] S3. Based on the geometric center and particle size of the particulate material, determine whether the particles are in contact, and construct a topological model of the particulate material contact network according to the contact relationship. In this step, the following conditions must be met when the particles come into contact: In the formula, x i , y i Particles i and granules j The coordinates of the center point, r i , r j Particles i and granules j The radius is denoted by Δ, where Δ represents the image recognition error.

[0019] The above formula provides the contact relationship between each particle.

[0020] Subsequently, a topological model of the particulate material contact network is established using the following method: taking individual particulate materials as nodes and the contact relationships between particles as edges, all contact relationships are transformed into network connection relationships.

[0021] S4. Based on the topology model of the particle contact network, calculate the matching property, particle node betweenness, network betweenness centrality, and clustering coefficient of the particle contact network, and evaluate the sealing layer based on the aforementioned parameters: nodes with a cumulative particle node betweenness distribution of more than 95% are strong chain structures, and the greater the matching property, the greater the network betweenness centrality, and the smaller the clustering coefficient, the worse the stability of the sealing layer.

[0022] In this step, the calculation methods for each parameter are as follows: Matching calculation: In the formula, r represents the matching degree; k i k j Representing edge e ij Two nodes v i v j The degree; M This represents the total number of edges in the network. Calculation of particle node betweenness: In the formula, Bi represents the particle node betweenness number. N jl Represents node v j v l The number of shortest paths between them; N jl(i) Represents node v j vl The shortest path between them passes through the nodes v i The number of entries; Calculation of network betweenness centrality: In the formula, C D For network betweenness centrality, C B for node betweenness B i The normalization result, V max The node with the highest betweenness centrality in the network. N The number of network nodes; Calculation of clustering coefficient: , In the formula, C represents the clustering coefficient. C i represents the clustering coefficient of the granular nodes.

[0023] For the above parameters, network matching characterizes the principal stress direction of the sealing layer, node betweenness number characterizes the strong chain particles, network betweenness centrality characterizes the shear strength of the strong chain in the sealing layer, and clustering coefficient characterizes the degree of strong chain evolution. Specifically, the minimum value of the network matching coefficient corresponds to the maximum value of the vertical strong chain proportion on the fracture surface, which is the critical point where the strong chain direction changes from perpendicular to the fracture surface to the horizontal direction. Nodes with a betweenness number accumulation of over 95% are strong chain particles in the sealing layer, indicating that these particles can withstand strong stress. The maximum value of network betweenness centrality corresponds to the shear failure process of the strong chain in the sealing layer, and the increase and decrease of the clustering coefficient indicate the increase and decrease of the number of strong chains, respectively.

[0024] Meanwhile, when evaluating the plugging formulation, the mechanical structure of the plugging layer is evaluated in descending order of priority: matching, network betweenness centrality, and clustering coefficient; that is, in the actual evaluation process, the weights of the three factors decrease sequentially.

[0025] To further illustrate the methods of the above embodiments, specific test examples are given below.

[0026] Test Example 1: Verification by Photoelasticity Experiment This test case analyzes the leakage situation of a well in the central depression of the Junggar Basin. The plugging formula for this well is 0.5%FCL+4%SHD+2%GYD-1+3%GYD-2+3%GYD-3+3%QSD-1+5%QSD-2+3%QSD-3+6%KGD-3, where FCL is short fiber, SHD is long fiber, GYD is aluminum alloy particles, QSD is walnut shell, KGD is calcium carbonate particles, -1 is coarse, -2 is medium coarse, and -3 is fine, all of which are rigid materials.

[0027] The well experienced well leakage in the Permian formation at a depth of 7500m. The formation pressure equivalent was 2.34, the minimum horizontal principal stress equivalent was 2.06, the maximum horizontal principal stress equivalent was 2.39, and the target pressure equivalent was 2.44. The method described in the above embodiment was used to analyze the well according to the leakage plugging formula and reservoir conditions.

[0028] Photoelasticity Experiment Verification: Photoelastic particles of 1mm, 0.8mm, and 0.5mm were prepared for simulation. These sizes correspond to 10 times the D90 of GYD, QSD, and KGD, respectively, meaning a 10x magnification photoelasticity was used in the experiment. The loading device for this photoelasticity experiment referenced a micro-contact pressurization device for drilling, completion, and plugging materials disclosed in CN202021285127.X. The pressure gradient was 0.5MPa. Each 0.5MPa increase during the experiment was considered a loading process. After each loading process was completed, the experiment was paused, and a high-definition camera recorded the force chain evolution image (0°-45°-45°-0°) and the plugging layer particle structure image (90°-45°-45°-0°). The four angles in parentheses correspond from left to right to the angles of the analyzer, the second quarter-wave plate, the first quarter-wave plate, and the polarizer, respectively.

[0029] See Figure 1 This study demonstrates the evolution of the matching compatibility of the sealing formulation's network under different pressures. The minimum matching compatibility corresponds to the maximum proportion of vertical strong chains on the seam surface, marking the critical point where the strong chain direction shifts from perpendicular to the seam surface to the horizontal direction. The maximum matching compatibility also indicates the beginning of the evolution of the strong chain direction distribution from horizontal to perpendicular to the seam surface. A continuously decreasing matching coefficient indicates that the strong chain network mainly evolves along the vertical direction, while an increasing matching coefficient indicates that the strong chain network mainly evolves along the horizontal direction.

[0030] See Figure 2 It quantifies the correlation between strong chains and betweenness by the repetition rate of high betweenness nodes covered by strong chains. The repetition rate is 86.45% for over 93% of the cumulative betweenness distribution and 100% for over 95%, indicating a significant correlation between strong chains and strong betweenness. The betweenness of nodes increases with the node degree value. The greater the particle connectivity, the more concentrated the stress, and the stronger the particle chain.

[0031] See Figure 3 It shows the evolution of network betweenness centrality of the plugging formulation under different pressures. As can be seen from the figure, loading processes 3, 8 and 12 are the maximum values ​​of network betweenness centrality, at which point the strong chains of the plugging layer undergo shear failure.

[0032] See Figure 4 The figure shows the evolution of the clustering coefficient and the number of strong chains of the plugging formulation under different pressures. As can be seen from the figure, the trend of the method in this embodiment is the same as that of the photoelastic experiment. Therefore, it can be shown that the clustering coefficient reflects the relative change of the mechanical structure during the loading process.

[0033] Test Example 2: Optimal Formula Calculation Four leak-sealing formulas were selected, as shown in Table 1.

[0034] Table 1 Leak Sealing Formula Table In this test case, a well in the central depression of the Junggar Basin is also taken as the research object. The well experienced leakage in the Permian formation, with a depth of 7500m, a formation pressure equivalent of 2.34, a minimum horizontal principal stress equivalent of 2.06, a maximum horizontal principal stress equivalent of 2.39, and a target bearing pressure equivalent of 2.44.

[0035] Following the method described in the above embodiments, all four leak-sealing formulations were evaluated. At the same time, the compressive strength of the sealing layer formed by the above leak-sealing formulations was experimentally tested. The final results are shown in Table 2.

[0036] Table 2 Evaluation Results As shown in Table 2, formulation #4 has the highest matching degree and is therefore eliminated first. Among the remaining three formulations, #3 has the highest betweenness centrality but lower shear strength. Among the remaining two formulations, #1 has the highest clustering coefficient, making formulation #1 the optimal choice. Furthermore, the compressive strength test results demonstrate the high accuracy of the evaluation results obtained using the method in this embodiment.

[0037] The present invention has been disclosed above with preferred embodiments. However, those skilled in the art should understand that these embodiments are for illustrative purposes only and should not be construed as limiting the scope of the invention. Further improvements can be made without departing from the principles of the invention, and these improvements should also be considered as protections of the present invention.

Claims

1. A method for evaluating the mechanical structure of ultra-deep crack sealing layers based on topology, characterized in that, Includes the following steps: S1. Obtain the reservoir conditions of the well leakage zone and, in conjunction with indoor plugging experiments, obtain the plugging layer of the plugging formula to be evaluated, wherein the plugging formula to be evaluated includes particulate materials. S2. Perform a CT scan on the sealing layer to obtain a photograph of the particulate material inside the sealing layer. Use digital image processing technology to obtain the geometric center and particle size of the particulate material. S3. Based on the geometric center and particle size of the particulate material, determine whether the particles are in contact, and construct a topological model of the particulate material contact network according to the contact relationship. S4. Based on the topology model of the particle contact network, calculate the matching property, particle node betweenness, network betweenness centrality, and clustering coefficient of the particle contact network, and evaluate the sealing layer based on the aforementioned parameters: nodes with a cumulative particle node betweenness distribution of more than 95% are strong chain structures, and the greater the matching property, the greater the network betweenness centrality, and the smaller the clustering coefficient, the worse the stability of the sealing layer.

2. The method for evaluating the mechanical structure of ultra-deep crack sealing layers based on topology according to claim 1, characterized in that, In S1, the reservoir conditions are in-situ stress, formation pressure, and target bearing capacity.

3. The method for evaluating the mechanical structure of ultra-deep crack sealing layers based on topology according to claim 1, characterized in that, In S3, when particles are in contact, the following conditions must be met: In the formula, x i , y i Particles i and granules j The coordinates of the center point, r i , r j Particles i and granules j The radius is denoted by Δ, where Δ represents the image recognition error.

4. The method for evaluating the mechanical structure of ultra-deep crack sealing layers based on topology according to claim 1, characterized in that, The construction method of the particulate material contact network topology model is as follows: take a single particulate material as a node and the contact relationship between particles as an edge, and transform all contact relationships into network connection relationships.

5. The method for evaluating the mechanical structure of ultra-deep crack sealing layers based on topology according to claim 1, characterized in that, In S4, the mechanical structure of the sealing layer is evaluated in descending order of priority: matching, network betweenness centrality, and clustering coefficient.

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