Method and system for optimizing geometric structure of shaped charge liner based on numerical simulation and shaped charge liner

Abstract

The invention relates to the technical field of shaped charge liners, and provides a shaped charge liner geometric structure optimization method and system based on numerical simulation and a shaped charge liner.The method comprises the following steps that a perforation numerical calculation model of an underground environment is established, and numerical simulation is conducted on the process of energy-gathered jet flow penetration sandstone; mutually independent geometric structure parameters are selected according to the geometric relationship between the shaped charge liner structures, the influence rule of the geometric structure parameters on the perforation depth is recognized, and key parameters influencing the perforation depth are screened out to serve as optimization variables; carrying out epitaxy on the value range of the optimization variable, carrying out epitaxy based on the value range of the optimization variable, constructing an equation of the target variable about the optimization variable, and iterating distribution of an optimal solution by using a genetic algorithm; and the solution with the maximum perforation depth is selected in the constraint condition range to serve as the optimal shaped charge liner geometric structure. According to the method, numerical simulation and an optimization solution algorithm are fused, and a parameter epitaxy method is adopted during equation construction, so that the accuracy and efficiency of geometric structure optimization of the shaped charge liner can be improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field related to liner, and in particular, to a method and system for optimizing the geometric structure of a liner based on numerical simulation, and a liner. Background Art

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

[0003] Oil is a non-renewable resource, but it is also one of the main energy and chemical raw materials in the world today. Efficient oil extraction will have increasingly important significance. Completion is the last important link before oil and gas extraction. The quality of completion directly determines the production capacity and life of oil and gas wells. At present, perforation completion technology is widely used in oil and gas fields at home and abroad, that is, the high-speed metal jet generated by the shaped charge perforator during the detonation process is used to penetrate the casing, cement ring and formation rock in a very short time to establish an oil and gas channel between the reservoir and the wellbore. This technology has the characteristics of low cost and high recovery rate, and is widely used in the exploration and development process of oil and gas fields. Among them, the liner is the heart of the shaped charge perforator and the core component for forming the metal jet to complete the perforation operation, which has a significant impact on the perforation effect.

[0004] At present, oil and gas development is gradually expanding to deep-ultra-deep buried hill oil and gas fields and offshore oil and gas fields. The oil reservoir has the characteristics of multiple structures, multiple genesis, and multiple forms, and the rock is a hard rock with complex lithology and denseness (uniaxial compressive strength> 60MPa). Affected by various factors such as high wellbore pressure and dense reservoirs, the use of conventional perforating bullets can no longer effectively release oil and gas layers like in shallow and medium layers. Many oil and gas wells have difficulty in producing fluid after perforation, or even no fluid, and it is difficult to determine whether the mud contaminated zone has been penetrated.

[0005] In order to make the perforating bullet suitable for the development of deep and ultra-deep oil and gas, and to make it still have high penetration in complex environments, it is necessary to optimize the geometric structure of the key component of the perforating bullet, the liner; the inventors found in the research that, at present, the structural optimization of the liner is mainly carried out by collecting data through experimental observation, and the geometric structure of the liner in the perforating bullet is optimized on the basis of the experiment, but the actual experimental process is difficult, especially for hard rocks, and the collected data are often scattered, which makes it difficult to describe the entire physical process of the perforating bullet perforating hard rocks. Therefore, it is extremely difficult, expensive and time-consuming to optimize the geometric structure of the liner in the perforating bullet on the basis of experiments. Summary of the invention

[0006] In order to solve the problem that the penetration depth of the existing perforating bullets in complex geological environments is limited and it is difficult to optimize them through experiments, the present invention proposes a liner geometry optimization method, system and liner based on numerical simulation, which can improve the accuracy and efficiency of the liner geometry optimization.

[0007] In order to achieve the above objectives, the present disclosure adopts the following technical solutions:

[0008] One or more embodiments provide a method for optimizing the geometric structure of a liner based on numerical simulation, comprising the following steps:

[0009] A numerical calculation model of perforation in downhole environment was established to numerically simulate the process of shaped energy jet penetrating sandstone;

[0010] According to the geometric relationship between the liner structures, independent geometric structure parameters are selected, the influence of geometric structure parameters on perforation depth is identified, and key parameters with greater influence on perforation depth are selected as optimization variables;

[0011] Based on the value range of the optimization variable, the equation of the target variable about the optimization variable is constructed, and the distribution of the optimal solution is iterated using the genetic algorithm;

[0012] The solution with the largest perforation depth within the constraints is selected as the optimal liner geometry.

[0013] One or more embodiments provide a system for optimizing the geometric structure of a liner based on numerical simulation, including:

[0014] Model building module: configured to establish a perforation numerical calculation model of the downhole environment and perform numerical simulation on the process of shaped energy jet penetrating sandstone;

[0015] Variable selection module: It is configured to select independent geometric structure parameters according to the geometric relationship between the liner structures, identify the influence of geometric structure parameters on the perforation depth, and select key parameters with greater influence on the perforation depth as optimization variables;

[0016] Equation building module: It is configured to build an equation of the target variable with respect to the optimization variable based on the extension of the value range of the optimization variable, and iterate the distribution of the optimal solution using the genetic algorithm;

[0017] Optimal solution selection module: It is configured to select the solution with the largest perforation depth within the constraints as the optimal liner geometry.

[0018] An electronic device includes a memory and a processor and computer instructions stored in the memory and running on the processor. When the computer instructions are run by the processor, the steps in the above-mentioned method for optimizing the geometric structure of a medicine liner based on numerical simulation are completed.

[0019] A liner adopts the above-mentioned liner geometric structure optimization method based on numerical simulation to obtain the liner geometric structure.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] This embodiment simulates the process of concentrated jet penetrating sandstone by establishing a numerical calculation model of perforation in the downhole environment, and can conduct a large number of experiments in a virtual environment, significantly reducing the time and cost of actual experiments. This method can quickly and batch collect data, analyze the influence of geometric structure parameters on perforation depth, and avoid design delays caused by scattered or incomplete data in traditional experimental processes; through numerical simulation, the entire penetration process of the liner in hard rock can be displayed in detail, and the influence of each parameter change on the perforation depth can be captured. On this basis, the geometric structure parameters that have the greatest impact on the perforation depth are accurately identified and selected through the optimization algorithm, so as to perform targeted optimization and improve the accuracy and pertinence of the design scheme. The present disclosure combines numerical simulation, optimization solution algorithm, and parameter extension method when constructing equations, which can improve the accuracy and efficiency of the optimization of the geometric structure of the liner.

[0022] The advantages of the present disclosure and the advantages of additional aspects will be described in detail in the following specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings constituting a part of the present disclosure are used to provide a further understanding of the present disclosure. The exemplary embodiments of the present disclosure and the description thereof are used to explain the present disclosure but do not constitute a limitation of the present disclosure.

[0024] Figure 1 is a schematic flow chart of the optimization method of Embodiment 1 of the present disclosure;

[0025] Figure 2 is a schematic diagram of the structure of the perforation numerical calculation model of Example 1 of the present disclosure;

[0026] Figure 3 is a schematic diagram of the geometric structure of the medicine-shaped liner of Example 1 of the present disclosure;

[0027] Figure 4 is an initial numerical simulation result diagram obtained by performing data simulation before optimization in step 1 of embodiment 1 of the present disclosure;

[0028] FIG5( a ) is a schematic diagram showing the parameter extension of the outer wall top angle radius of Example 1 of the present disclosure;

[0029] FIG5( b ) is a schematic diagram of parameter extension of the outer wall opening angle of Example 1 of the present disclosure;

[0030] FIG5( c ) is a schematic diagram showing the parameter extension of the inner wall top angle radius of Example 1 of the present disclosure;

[0031] FIG5( d ) is a schematic diagram of parameter extension of the inner wall opening angle of Example 1 of the present disclosure;

[0032] Figure 6 is a fitted surface diagram of the target variable L of the first embodiment of the present disclosure with respect to the optimization variable;

[0033] Figure 7 is a fitted surface diagram of the target variable D of Example 1 of the present disclosure with respect to the optimization variable;

[0034] Figure 8 is a flow chart of an improved genetic algorithm NSGA-II of Example 1 of the present disclosure;

[0035] Fig. 9 is the optimal solution distribution diagram of the target variable of Example 1 of the present disclosure;

[0036] Fig.10 It is a comparison chart of the numerical simulation results before and after the optimization of the parameters of the medicine liner in Example 1 of the present disclosure. DETAILED DESCRIPTION

[0037] The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0038] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present disclosure. 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 disclosure belongs.

[0039] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof. It should be noted that, in the absence of conflict, the various embodiments in the present disclosure and the features in the embodiments can be combined with each other. The embodiments will be described in detail below in conjunction with the accompanying drawings.

[0040] Example 1

[0041] In the technical solutions disclosed in one or more embodiments, Figures 1 to 10 As shown, a method for optimizing the geometric structure of a liner based on numerical simulation includes the following steps:

[0042] Step 1: Establish a numerical calculation model of the perforation in the downhole environment and perform numerical simulation on the process of the shaped energy jet penetrating the sandstone;

[0043] Step 2: select independent geometric structure parameters according to the geometric relationship between the liner structures, identify the influence of the geometric structure parameters on the perforation depth, and select the key parameters that have a greater impact on the perforation depth as optimization variables;

[0044] Step 3: Based on the value range of the optimization variable, the equation of the target variable about the optimization variable is constructed, and the distribution of the optimal solution is iterated using the genetic algorithm;

[0045] Step 4: Select the solution with the largest perforation depth within the constraints as the optimal liner geometry.

[0046] Wherein, in step 4, the range of the constraint condition may be within the range allowed by the process;

[0047] In this embodiment, by establishing a numerical calculation model of perforation in the downhole environment and simulating the process of concentrated jet penetrating sandstone, a large number of experiments can be carried out in a virtual environment, significantly reducing the time and cost of actual experiments. This method can quickly and batch collect data, analyze the influence of geometric parameters on perforation depth, and avoid design delays caused by scattered or incomplete data in traditional experiments; numerical simulation can show in detail the entire penetration process of the liner in hard rock, and capture the influence of each parameter change on the perforation depth. On this basis, the geometric parameters that have the greatest impact on the perforation depth are accurately identified and selected through optimization algorithms (such as genetic algorithms), so as to perform targeted optimization and improve the accuracy and pertinence of the design scheme.

[0048] In the above steps, the optimization variable is the variable to be designed. When the optimal solution of the variable to be designed is obtained so that the target variable can reach the target value, the performance of the corresponding designed liner meets the requirements;

[0049] The numerical simulation method used in this embodiment can provide a relatively comprehensive and complete description of the processes such as the collapse of the charge liner and the formation of the jet in the perforating bullet. By adopting this method, the perforation depth can be effectively improved, and the design of the geometric structure of the charge liner can be optimized to maximize the perforation effect. Compared with traditional experimental methods, this numerical simulation method can save a lot of time and resources. Due to the use of genetic algorithms for optimization, this method can automatically identify and optimize the key geometric parameters that affect the perforation effect, making the final result more accurate and reliable.

[0050] The method for optimizing the geometric structure of the liner based on numerical simulation method proposed in this embodiment also provides a more convenient idea for studying structural optimization problems in engineering.

[0051] In step 1, a perforation model is established according to the actual reservoir environment, and numerical simulation is carried out to obtain the depth and aperture of the perforating charge cap penetrating the sandstone target in the initial state;

[0052] The process of shaped charge jet penetrating sandstone refers to the use of a shaped charge jet (SCJ) generated by an explosive device to penetrate rock (usually sandstone) through its high speed and high pressure characteristics to perform effective perforation underground.

[0053] According to the actual reservoir environment, a complete perforation numerical calculation model simulating the reservoir environment is established, such as Figure 2 As shown, the model is set as a 1 / 2 axisymmetric model, including a simulated rock formation, a casing in the simulated rock formation, the casing is hollow inside to form a wellbore, the casing is filled with water, a perforating gun is set at a set depth in the casing, and the perforating gun is connected with an oil pipe; a perforating bullet is set in the perforating gun, and a cement ring is set in the space between the wellbore and the formation;

[0054] Perforating gun: placed in the wellbore and used to fire perforating bullets;

[0055] Perforating bullets: The holes formed by the bullets are used for oil and gas production;

[0056] Tubing and casing: The tubing is located in the center of the well, and the casing surrounds the tubing to provide structural support;

[0057] Cement ring and formation: seal the space between the wellbore and the simulated rock formation to prevent fluid exchange between the wellbore and the reservoir;

[0058] Rock: The formation material surrounding the wellbore, usually the area where the reservoir is located, where oil and gas are stored.

[0059] Specifically, the perforation numerical calculation model uses a finite element model to divide the rock, casing and perforation area through meshing;

[0060] In this embodiment, the calculation domain of the entire model is divided by finite element meshing, and the distribution of the perforation area and formation materials is refined to ensure accurate calculation results. The 1 / 2 axisymmetric model can effectively reduce the calculation time and computing resource consumption without affecting the accuracy of the perforation numerical simulation. The model can also accurately simulate the downhole perforation environment, including the influence of casing and cement sheath, thereby providing reliable data support for optimizing the geometry of the liner.

[0061] The perforation numerical calculation model constructed in this implementation is used to study the impact of the perforation process on the surrounding reservoir geology by establishing the interaction between the reservoir, wellbore, casing and perforating bullets. The numerical simulation of the model helps to evaluate the penetration depth of the perforating bullet, the perforating efficiency and the degree of damage to the well wall and formation caused by perforation.

[0062] As a further technical solution, the numerical simulation adopts the arbitrary Lagrangian-Euler algorithm, in which air covers the entire model and shares nodes with explosives, powder caps and water to ensure smooth simulation; a set pressure such as 30MPa confining pressure is applied to the grid nodes of the rock, and a set pressure such as 25MPa wellbore pressure is applied to the grid nodes of the water layer to restore the actual reservoir environment.

[0063] Specifically, the process of shaped energy jet penetrating sandstone is numerically simulated. During the simulation, the steel target, cartridge case, holster and casing materials are set to 45# steel with a density of 7.83g / cm3, a yield limit of 425MPa, an elastic modulus of 2.1×105MPa, and a Poisson's ratio of 0.3, which are specifically set to be described by the Plasticv_Kinematic material model. The type of explosive is RDX, with a density of 1.72g / cm3 and a detonation velocity of 7980m / s. It is described by the high-energy explosive material model High_Explosive and the JWL state equation. The mechanical behavior of the charge liner in the jet forming process is generally described by the Johnson-Cook (JC) constitutive model and the Grunensin state equation. The charge liner material used in this embodiment is a new powder with a density of 14.96g / cm3. The water layer and air domain are jointly described by the Null material model and the Grunensin state equation. The cement sheath and sandstone target are described by the HJC model, which takes into account the compression damage accumulation and also has corresponding descriptions of the strain rate effect, compression strength and confining pressure effect of the material. It can better reflect the dynamic damage of the material under large deformation and high strain rate.

[0064] The above settings can be used to perform numerical simulations, and the following can be obtained: Figure 4 The perforation results shown are obtained by changing the geometric structure parameters of the numerical calculation model and performing multiple simulations to obtain perforation results with different parameters, so as to provide the influence of subsequent geometric structure parameters on the perforation depth;

[0065] Among them, Plasticv_Kinematic material model, High_Explosive and JWL state equations, Null material model, Grunensin state equation, HJC model are all constitutive models and equations for numerical simulation. They are built-in in the simulation software LS-dyna and are set by the software when used.

[0066] In step 2, independent geometric structure parameters are selected according to the geometric relationship between the liner structures, and then the influence of the geometric structure parameters on the perforation depth is analyzed based on the numerical simulation method in step 1, and the parameters are selected as the design variables to be optimized, referred to as optimization variables, and the perforation depth and perforation aperture are used as target variables;

[0067] The parameters of the liner structure are interrelated and in order to ensure the comprehensiveness of the subsequent fitting, this embodiment appropriately expands the parameter research range beyond the range allowed by the process. A further technical solution, the specific process of optimizing variable screening, includes the following:

[0068] Step 21, determine the geometric parameters of the liner structure and select variable parameters;

[0069] There are eight items that can be changed in the charge liner structure, including the outer wall top angle radius R1, the inner wall top angle radius R2, the outer wall opening angle θ1, the inner wall opening angle θ2, the straight edge height L1, the bottom edge thickness L2, the caliber, and the height. However, the charge liner needs to be installed in the perforating gun, so the caliber and height cannot be changed. Therefore, there are six variable parameters.

[0070] Step 22, classifying the parameters according to the structure of the liner;

[0071] In step 21, the six parameters screened out can be divided into two categories: parameters related to the outer wall of the liner and parameters related to the inner wall of the liner;

[0072] The parameters related to the outer wall of the liner include: outer wall top angle radius R1, outer wall opening angle θ1, straight edge height L1;

[0073] The parameters related to the inner wall of the liner include: inner wall top angle radius R2, inner wall opening angle θ2, bottom edge thickness L2;

[0074] Step 23: for the obtained classified parameters, determine the relationship between the parameters in each category and select independent parameters;

[0075] The parameters related to the outer wall have the relationship of formula (1), and the parameters related to the inner wall have the relationship of formula (2);

[0076] Under the condition that the height and caliber of the medicine liner remain unchanged, there are six parameters that can be changed, namely, the outer wall top angle radius R1, the inner wall top angle radius R2, the outer wall opening angle θ1, the inner wall opening angle θ2, the straight edge height L1 and the bottom edge thickness L2. The process allowable range of these parameters is: outer wall top angle radius 0.35cm~0.5cm, inner wall top angle radius 0.25cm~0.4cm, outer wall opening angle 40°~45°, inner wall opening angle 40°~43°, straight edge height 0.15cm~0.18cm, bottom edge thickness 0.15cm~0.2cm. However, these six parameters are not independent of each other, and have the following relationship:

[0077]

[0078] Among them, H represents the distance from the center of the vertex to the bottom; r represents half of the bottom diameter (φ4.6);

[0079] Formula (1) contains three parameters related to the outer wall. If two of them are determined, the other one is a fixed value. Therefore, there are two independent parameters in formula (1). Similarly, formula (2) also has two independent parameters.

[0080] Then, the six parameters are screened into four independent parameters, such as Figure 3 The parameters marked in red include: outer wall top angle radius R1, inner wall top angle radius R2, outer wall opening angle θ1, inner wall opening angle θ2. That is, among the six parameters, four are determined and the other two can also be calculated by formula (1) and formula (2).

[0081] Step 24, extending the value range of the independent parameters so that each independent parameter can be simulated in the full process range, and performing numerical simulation based on the numerical calculation model of step 1; according to the numerical simulation results, determining the sensitivity of the screened independent parameters to the target parameters and sorting them, and screening out a set number of independent parameters with higher sensitivity as optimization parameters;

[0082] The amount of calculation for optimizing four parameters is still too large, which will affect the efficiency and effect of optimization. Optimizing three parameters can greatly reduce the difficulty of solving. Figure 2 The established numerical calculation model systematically studies the sensitivity of perforation depth to these four parameters, and selects the three most sensitive ones as the optimization targets of the liner geometry.

[0083] When analyzing the sensitivity of these four parameters, it is necessary to extend the range of the parameters. The specific reasons are as follows: Taking formula (1) as an example, in actual operation, if R1 and θ1 are taken according to the process range, then the value of L1 will exceed the process range. In particular, in some extreme cases, if R1 and θ1 take the maximum value, then L1 will far exceed the process range. Similarly, if the value of L1 is within the process range, the range of possible values ​​of R1 and θ1 will become extremely small. Therefore, in order to ensure more reasonable results and to make the subsequent fitting equations more comprehensive, the range of the values ​​of the four parameters is appropriately extended here, and then their sensitivity is analyzed, so that three of the parameters are selected as optimization variables. The three selected parameters can take into account both accuracy and efficiency.

[0084] Specifically, Figure 3 As shown in the figure, by changing the values ​​of parameters R1, R2, θ1 and θ2 in turn and re-simulating, the perforation depth of the perforating bullet penetrating the sandstone target under different parameter values ​​can be obtained. The parameter extension example is as follows Figure 5(a) to Figure 5(d)As shown. Although neither R2 nor θ2 has a monotonic influence law, θ2 is less sensitive, so the optimization targets of the three liner geometric dimensions are selected as: outer wall top angle radius R1, inner wall top angle radius R2 and outer wall opening angle θ1. The inner wall opening angle θ2 is fixed as the initial value, and the optimization of the liner geometry is carried out on the basis of the initial structure. The initial value is the original size of the liner structure before optimization, that is, the opening angle θ2 is fixed as the initial value before optimization;

[0085] In step 3, determine the equation of the target variable with respect to the optimization variable, as follows:

[0086] Step 31, determining a polynomial fitting equation representing the optimization variables and the output variables, the polynomial fitting equation including linear terms, quadratic terms and mixed terms;

[0087]

[0088] Where: xj is the optimization variable; N is the number of targets; regression coefficient represents a constant coefficient, They represent linear coefficients, pure quadratic coefficients and mixed quadratic coefficients respectively.

[0089] Step 32: perform numerical simulation according to different parameter combinations, solve the coefficients of the polynomial fitting equation, and obtain the equation of the target variable with respect to the optimization variable;

[0090] The parameters of the outer wall top angle radius R1 (assumed to be a for the convenience of subsequent description), the inner wall top angle radius R2 (assumed to be b) and the outer wall opening angle θ1 (assumed to be c) are used as optimization variables, and the perforation depth L and the perforation aperture D are used as target variables. On this basis, numerical simulations are performed on the combination of different parameters to obtain the fitting surface of the target variables (L, D) with respect to the design variables (a, b, c). Figure 6 and Figure 7 Taking parameters b and c as an example, the fitting surface of perforation depth and perforation aperture under the interaction of the two parameters is given. As can be seen from the figure, the response surface fits these data points well, and the local fluctuation of the surface is small, so a reliable equation of the target variable with respect to the optimization variable can be obtained, as shown in formula (3) and formula (4);

[0091]

[0092]

[0093] In the above scheme, in the process of obtaining the explicit expressions of the target variable with respect to the optimization variable (Formula (3) and Formula (4), the parameter value range is extended to avoid the possibility of missing the optimal solution. The perforation depth and perforation aperture corresponding to different parameters within the range can be obtained without repeating experiments or simulations.

[0094] In step 3, based on the obtained equation of the objective variable about the optimization variable, the distribution of the optimal solution is obtained through the improved genetic algorithm NSGA-II, and the solution with the maximum perforation depth is found within the range allowed by the liner process. The liner geometric structure parameters corresponding to this solution can be used as the optimal geometric structure of the liner.

[0095] Specifically, an improved non-dominated sorting genetic algorithm NSGA-II is used, an elite strategy is introduced, and shared parameters are eliminated, which not only ensures the diversity of the population but also improves the optimization accuracy. The equation of the target variable obtained in 3 about the optimization variable is iteratively solved, including the following steps:

[0096] Step 3.1: Construct a population based on the optimization variables and initialize it;

[0097] There are three optimization variables: outer wall top corner radius R1, inner wall top corner radius R2, outer wall opening angle θ1;

[0098] There are two target variables: perforation depth L and perforation aperture D;

[0099] The population size is set to 100, the crossover rate of the population is 0.85, the mutation rate is 0.2, and the number of iterations is 200;

[0100] Step 3.2: Randomly generate a parent population of set population size, perform crossover, mutation, and combination to form a child population; use the elite strategy to merge the parent population with the child population to obtain a new population;

[0101] Specifically, a parent population of 100 is randomly generated, and the parent population undergoes crossover and mutation to form a child population of 100, and the parent population and child population are merged (ensuring that elite individuals are retained) to form a new population of 200. 100 data points are randomly generated through the equation, and these data points undergo crossover, mutation, and combination to form 200 new data points).

[0102] Step 3.3, calculate the number of individuals dominated by each new individual and the number of individuals dominated by each new individual;

[0103] Among them, if an individual xj is better than xi, then xj is marked as the dominant individual and xi is the dominated individual; if no individual xj is better than xi, then xi is marked as the non-dominated individual;

[0104] Step 3.4: stratify the data according to the dominance relationship to obtain the dominant layer and the non-dominated layer; that is, the dominant individuals are divided into the dominant layer, and the dominated individuals are divided into the non-dominated layer;

[0105] Step 3.5: Calculate the crowding degree of all individuals in each non-dominated layer, and select some individuals and all individuals in the dominant layer to form a new population based on the non-dominated relationship between individuals and the size of the individual crowding degree;

[0106] Specifically, the individual selection of the new population includes:

[0107] 1) All individuals in the dominant layer;

[0108] 2) Select individuals in the non-dominated layer whose crowding degree is greater than the threshold. If they are in the same non-dominated layer, select the individual with a larger crowding degree;

[0109] Furthermore, the crowding degree can be the distance relationship between an individual and the two individuals before and after it, and the calculation formula can be

[0110] CR=(Pn×(1-∑Ci)) / Pm

[0111] Among them, CR represents crowding, Pn represents the current population size, ΣCi represents the sum of the fitness of all individuals, and Pm represents the maximum crowding.

[0112] Step 4: Iterate the set number of times until the new population becomes stable, the individual update ratio in the population is less than the set value, and the optimal solution is obtained;

[0113] When the new population output at the end is relatively stable, it is considered that the new population at this time is the optimal solution. These optimal solutions are plotted as follows: Fig. 9 As shown in FIG. 1 , the non-dominated sorting genetic algorithm NSGA-II in this embodiment introduces an elite strategy and eliminates shared parameters, which not only ensures the diversity of the population but also improves the optimization accuracy.

[0114] Based on the optimal liner structure parameters, the model was re-established and numerical simulation was performed. The simulation results after the liner structure was optimized were compared with those before optimization to verify the optimization effect.

[0115] The genetic algorithm process used is as follows Figure 8 As shown, after 200 iterations, the results tend to be stable, and we can get the following Fig. 9 The distribution of optimal solutions is shown in the figure, and each optimal solution corresponds to a set of structural parameters of the liner. It can be seen from the figure that the greater the perforation depth, the smaller the perforation aperture, which is similar to the conclusion we have drawn in engineering practice, which also indirectly confirms the rationality of these optimal solutions.

[0116] The optimal solution in which all three parameters meet the process requirements is marked in red. When the perforation depth is selected as the main criterion, the green mark is the optimal choice that meets the requirements. The geometric structure parameters of the liner corresponding to this solution are: outer wall top corner radius 0.4m, inner wall top corner radius 0.35cm, outer wall opening angle 44.58°, inner wall opening angle 43°, straight edge height 0.15cm and bottom edge thickness 0.175cm.

[0117] The numerical simulation was re-conducted based on the optimized geometric structure parameters of the liner and compared with the simulation results before parameter optimization. Fig.10 As shown. As for the perforation depth, the perforation depth before structural optimization was 32.14 cm, while after structural optimization, the perforation depth was 36.81 cm. The perforation depth after structural optimization increased by 14.53% compared with that before optimization, and the effect was significant. Therefore, the method proposed in this embodiment can achieve ideal results by optimizing the structure of the liner.

[0118] Example 2

[0119] Based on Example 1, this embodiment provides a system for optimizing the geometric structure of a liner based on numerical simulation, including:

[0120] Model building module: configured to establish a perforation numerical calculation model of the downhole environment and perform numerical simulation on the process of shaped energy jet penetrating sandstone;

[0121] Variable selection module: It is configured to select independent geometric structure parameters according to the geometric relationship between the liner structures, identify the influence of geometric structure parameters on the perforation depth, and select key parameters with greater influence on the perforation depth as optimization variables;

[0122] Equation building module: It is configured to build an equation of the target variable with respect to the optimization variable based on the extension of the value range of the optimization variable, and iterate the distribution of the optimal solution using the genetic algorithm;

[0123] Optimal solution selection module: It is configured to select the solution with the largest perforation depth within the constraints as the optimal liner geometry.

[0124] It should be noted here that the various modules in this embodiment correspond one-to-one to the various steps in Example 1, and the specific implementation process is the same, which will not be repeated here.

[0125] Example 3

[0126] Based on Example 1, this embodiment provides an electronic device, including a memory and a processor, and computer instructions stored in the memory and running on the processor. When the computer instructions are run by the processor, the steps in the above-mentioned method for optimizing the geometric structure of the medicine liner based on numerical simulation are completed.

[0127] Example 4

[0128] Based on Example 1, this example provides a liner, and the liner geometry optimization method based on numerical simulation described in Example 1 is used to obtain the liner geometry structure.

[0129] The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. For those skilled in the art, the present disclosure may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

[0130] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Technical personnel in the relevant field should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A method for optimizing the geometric structure of a liner based on numerical simulation, characterized in that: The steps include: A numerical calculation model of perforation in downhole environment was established to numerically simulate the process of shaped energy jet penetrating sandstone; Select independent geometric structure parameters according to the geometric relationship between the liner structures, identify the influence of geometric structure parameters on the perforation depth, and select the key parameters affecting the perforation depth as optimization variables; Based on the value range of the optimization variable, the equation of the target variable about the optimization variable is constructed, and the distribution of the optimal solution is iterated using the genetic algorithm; The solution with the largest perforation depth within the constraints is selected as the optimal liner geometry.

2. The method for optimizing the geometric structure of a liner based on numerical simulation according to claim 1, characterized in that: The perforation numerical calculation model is set as an axisymmetric model, including a simulated rock formation, a casing in the simulated rock formation, the casing is hollow inside to form a wellbore, the casing is filled with water, a perforating gun is set at a set depth in the casing, and the perforating gun is connected to an oil pipe; a perforating bullet is set in the perforating gun, and a cement ring is set in the space between the wellbore and the formation.

3. The method for optimizing the geometric structure of a liner based on numerical simulation according to claim 1, characterized in that: The numerical simulation adopts the Lagrange-Euler algorithm to cover the entire perforation numerical calculation model with air, and share nodes with explosives, powder liners and water; a set confining pressure is applied to the grid nodes of simulated rocks, and a set wellbore pressure is applied to the grid nodes of the water layer to restore the actual reservoir environment.

4. The method for optimizing the geometric structure of a liner based on numerical simulation according to claim 1, characterized in that: The specific process of optimizing variable screening includes: Determine the geometric parameters of the liner structure and select the variable parameters; According to the structure of the liner, the parameters are classified into parameters related to the outer wall of the liner and parameters related to the inner wall of the liner; For the obtained classified parameters, determine the relationship between the parameters in each category and screen out independent parameters; The value range of independent parameters is extended so that each independent parameter can be simulated in the entire process range. Numerical simulation is performed based on the numerical calculation model. According to the numerical simulation results, the sensitivity of the screened independent parameters to the target parameters is determined and ranked, and a set number of independent parameters with higher sensitivity are screened out as optimization parameters.

5. The method for optimizing the geometric structure of a liner based on numerical simulation according to claim 1, characterized in that: Determine the equation of the target variable with respect to the optimization variable, as follows: Determine a polynomial fitting equation representing the optimization variable and the output variable, the polynomial fitting equation including linear terms, quadratic terms and mixed terms; Numerical simulation is carried out according to different parameter combinations, the coefficients of the polynomial fitting equation are solved, and the equation of the target variable with respect to the optimization variable is obtained.

6. The method for optimizing the geometric structure of a liner based on numerical simulation according to claim 1, characterized in that: Solving the equation of the target variable with respect to the optimization variable includes the following steps: Construct a population based on the optimization variables and initialize it; Randomly generate a parent population of a set population size, perform crossover, mutation, and combination to form a child population; use an elite strategy to merge the parent population with the child population to obtain a new population; Calculate the number of individuals dominated by each new individual, as well as the number of individuals dominated by each new individual; The data are stratified according to the dominance relationship to obtain the dominant layer and the non-dominated layer; The crowding degree of all individuals in each non-dominated layer is calculated. According to the non-dominated relationship between individuals and the size of individual crowding degree, some individuals are selected to form a new population with all individuals in the dominant layer. Iterate the set number of times until the new population becomes stable, the proportion of individual updates in the population is less than the set value, and the optimal solution is obtained.

7. The method for optimizing the geometric structure of a liner based on numerical simulation according to claim 1, characterized in that: The solution with the largest perforation depth is selected within the range of constraints, where the range of constraints is the range allowed by the liner process.

8. The geometric structure optimization system of the liner based on numerical simulation is characterized by: include: Model building module: configured to establish a perforation numerical calculation model of the downhole environment and perform numerical simulation on the process of shaped energy jet penetrating sandstone; Variable selection module: configured to select independent geometric structure parameters according to the geometric relationship between the liner structures, identify the influence of geometric structure parameters on the perforation depth, and select key parameters affecting the perforation depth as optimization variables; Equation building module: It is configured to build an equation of the target variable with respect to the optimization variable based on the extension of the value range of the optimization variable, and iterate the distribution of the optimal solution using the genetic algorithm; Optimal solution selection module: It is configured to select the solution with the largest perforation depth within the constraints as the optimal liner geometry.

9. An electronic device, characterized in that: The invention comprises a memory and a processor and computer instructions stored in the memory and executed on the processor. When the computer instructions are executed by the processor, the steps in the method for optimizing the geometric structure of the medicine liner based on numerical simulation according to any one of claims 1 to 7 are completed.

10. A drug liner, characterized in that: The geometric structure of the liner is obtained by using the method for optimizing the geometric structure of the liner based on numerical simulation as described in any one of claims 1 to 7.

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