Shale gas well tubing depth optimization method and system, storage medium and equipment

Abstract

The invention belongs to the technical field of oil and gas development, and particularly relates to a shale gas well tubing depth optimization method and system, a storage medium and equipment, and the method comprises the steps: collecting basic data of a shale gas well; the steady-state model is used for evaluating pressure drop loss, liquid carrying capacity and daily gas production rate of different oil pipe depth lowering positions, and an oil pipe depth lowering scheme is determined; dividing production stages of the shale gas well and formulating an optimization strategy; based on the basic data, establishing a shale gas well shaft multiphase flow dynamic model, and verifying the accuracy; using the multiphase flow dynamic model to simulate working conditions of different oil pipe depths, adjusting oil pipe depth positions and recording simulation results; and based on a multiphase flow dynamic model simulation result and an optimization strategy, establishing a multi-objective optimization model, optimizing an oil pipe depth scheme, obtaining a Pareto optimal solution, and determining an optimal oil pipe depth scheme. According to the method, the complex production environment of the shale gas well can be effectively handled, and scientific basis and technical support are provided for improving the economic benefit of shale gas development.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas development technology, and in particular relates to a method, system, storage medium and equipment for optimizing the depth of tubing run-in of shale gas wells. Background Technology

[0002] Due to the low permeability of shale reservoirs, their commercial development requires horizontal drilling and multi-stage hydraulic fracturing technology. This causes the production decline pattern of shale gas wells to differ from that of conventional gas wells. In the initial stage of shale gas well production, due to abundant formation energy and a large flowback volume, casing blowout production is typically employed to quickly remove fracturing fluid from the near-wellbore area, thereby releasing the well's production capacity. However, as formation energy rapidly depletes, well pressure, gas production, and fluid production decrease rapidly. Furthermore, the complex wellbore structure with long horizontal sections and the fluid-carrying capacity of large-size casing can also lead to earlier fluid accumulation in the wellbore, thus affecting the stable production of the gas well.

[0003] To address the aforementioned issues, timely tubing installation and optimal parameter control are crucial measures for delaying formation energy decay and ensuring high and stable shale gas well production. Tubing depth is a key parameter in tubing production, directly impacting the production dynamics and stability of shale gas wells. A reasonable tubing depth can effectively control wellbore pressure, reduce fluid accumulation, and prevent production problems such as excessive reservoir back pressure and low gas production efficiency.

[0004] Currently, there is extensive research on the optimal tubing depth for vertical wells, but these findings are not entirely applicable to horizontal wells. The tubing depth for horizontal wells primarily relies on laboratory gas-liquid two-phase flow experiments. Existing technologies for determining tubing depth are mostly based on laboratory experiments and static analysis, lacking sufficient consideration of dynamic changes under actual production conditions. Furthermore, the evaluation indicators are relatively singular, lacking comprehensive and systematic parameter optimization, resulting in limitations in the optimization results. Summary of the Invention

[0005] To address the aforementioned problems, this invention provides a method, system, storage medium, and device for optimizing the tubing depth in shale gas wells.

[0006] This invention is achieved through the following scheme:

[0007] A method for optimizing the tubing depth in shale gas wells, the method comprising:

[0008] Collect basic data on shale gas wells; the basic data includes: formation data, wellbore parameter data, fluid composition data, production data, pressure drop test data, and production test data;

[0009] The pressure drop loss, fluid carrying capacity and daily gas production at different tubing depths were evaluated using a steady-state model to determine the tubing depth scheme.

[0010] Divide the production stages of shale gas wells and develop optimization strategies;

[0011] Based on the aforementioned basic data, a dynamic model of multiphase flow in shale gas wellbore was established, and its accuracy was verified.

[0012] Using the aforementioned multiphase flow dynamic model, simulations were performed on different tubing depths, the tubing depth was adjusted, and the simulation results were recorded.

[0013] Based on the simulation results of the multiphase flow dynamic model and the optimization strategy, a multi-objective optimization model is established to optimize the tubing depth scheme, obtain the Pareto optimal solution, and determine the optimal tubing depth scheme.

[0014] Furthermore, the optimal tubing depth scheme was applied to actual shale gas horizontal well operations, and the optimization effect was verified through field testing.

[0015] Furthermore, the evaluation of pressure drop loss, fluid carrying capacity, and daily gas production at different tubing depths using a steady-state model to determine the tubing depth plan specifically includes:

[0016] The pressure drop loss at different depths of the tubing was estimated using a pressure drop calculation model, and the energy consumption was assessed.

[0017] The Turner model was used to calculate the critical fluid carrying velocity and evaluate the stability of wellbore flow.

[0018] Using a linear regression model based on the production data, the daily gas production at different tubing depths is estimated.

[0019] Based on pressure drop loss, liquid carrying capacity, and daily gas production, the deep tubing plan was determined.

[0020] Furthermore, the process of dividing shale gas wells into production stages and formulating optimization strategies specifically includes:

[0021] In the initial high-yield stage, maximize output and rationally control pressure loss;

[0022] During the medium-term decline phase, balance production and energy consumption, reduce pressure drop, and prevent the effects of liquid accumulation;

[0023] In the later low-production stage, reduce the risk of liquid accumulation, ensure stable flow, and maintain the remaining production.

[0024] Furthermore, based on the aforementioned basic data, establishing a multiphase flow dynamic model for the shale gas wellbore and verifying its accuracy specifically includes:

[0025] Based on the fluid composition data, Multiflash software is used to generate a fluid package for import into OLGA software;

[0026] Based on the pressure drop test data and production test data, obtain the wellbore inflow dynamic data, and determine the inflow characteristics of the shale gas well by plotting the inflow dynamic curves of bottom hole flowing pressure and production.

[0027] Based on the wellbore parameter data, production data, fluid composition data, and inflow characteristics of shale gas wells, a multiphase flow dynamic model of shale gas wells is established in OLGA software.

[0028] The multiphase flow dynamic model was tested and verified using the aforementioned basic data and field test data.

[0029] Furthermore, the testing and verification of the multiphase flow dynamic model using the basic data and field test data specifically includes:

[0030] Based on the test results of the multiphase flow dynamic model, the relative error between the daily gas production and the actual production data is calculated.

[0031] The reliability of the simulation results of the OLGA model is judged based on the relative error.

[0032] Furthermore, based on the simulation results of the multiphase flow dynamic model and the optimization strategy, a multi-objective optimization model is established to optimize the tubing depth scheme, obtain the Pareto optimal solution, and determine the optimal tubing depth scheme, specifically including:

[0033] Determine the optimization objective: Based on the simulation results of the multiphase flow dynamic model, the optimization objective is determined to be maximizing output, minimizing energy loss, and achieving optimal flow state;

[0034] Establish a multi-objective optimization model: Based on the simulation results of the multiphase flow dynamic model and the optimization strategy, establish a multi-objective optimization model and define the objective function;

[0035] Optimize the tubing depth scheme: Optimize the tubing depth scheme using the multi-objective optimization model to obtain a set of Pareto optimal solutions;

[0036] By combining historical production data and expert experience, we analyzed various tubing depth options at the Pareto frontier and used machine learning algorithms to identify key factors affecting production and energy loss. Based on production priorities, we determined the optimal tubing depth option.

[0037] The present invention also provides a shale gas well tubing depth optimization system for implementing the aforementioned shale gas well tubing depth optimization method, the system comprising:

[0038] The data collection module is used to collect basic data from shale gas wells;

[0039] The module for confirming tubing depth schemes is used to evaluate the pressure drop loss, fluid carrying capacity, and daily gas production at different tubing depth locations using a steady-state model, and to determine the tubing depth scheme.

[0040] The production stage segmentation module is used to segment shale gas wells into production stages and formulate optimization strategies.

[0041] The multiphase flow dynamic model establishment and verification module is used to establish a multiphase flow dynamic model of the shale gas wellbore based on the aforementioned basic data, and to verify its accuracy.

[0042] The multiphase flow dynamic simulation module is used to simulate the working conditions of different tubing depths using the multiphase flow dynamic model, adjust the tubing depth position, and record the simulation results.

[0043] The module for determining the optimal tubing depth scheme is used to establish a multi-objective optimization model based on the simulation results of the multiphase flow dynamic model and the optimization strategy, optimize the tubing depth scheme, obtain the Pareto optimal solution, and determine the optimal tubing depth scheme.

[0044] The present invention also proposes a computer-readable storage medium storing a program or instructions that, when run on a computer, cause the computer to execute the aforementioned shale gas well tubing depth optimization method.

[0045] The present invention also proposes an apparatus including a processor coupled to a memory; the processor is used to read and execute a computer program stored in the memory to implement the aforementioned shale gas well tubing depth optimization method.

[0046] Compared with the prior art, the present invention has the following advantages:

[0047] This invention, through systematic optimization methods and advanced simulation technology, achieves optimized design of deep tubing runs in shale gas wells, significantly improving gas production efficiency, stability, and economic benefits. This method has significant practical application value and broad prospects for promotion, providing reliable technical support for the efficient development of shale gas and other unconventional oil and gas resources. Attached Figure Description

[0048] 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 some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 A schematic diagram of the process for optimizing the tubing depth in shale gas wells according to an embodiment of the present invention is shown.

[0050] Figure 2 The diagram shows the phase composition of natural gas from a shale gas well.

[0051] Figure 3 A schematic diagram of the dynamic model of multiphase flow in a wellbore in OLGA is shown;

[0052] Figure 4 This diagram illustrates the comparison between OLGA simulation results and on-site production data.

[0053] Figure 5 The graph shows the daily gas production variation of a shale gas well under different tubing depth conditions;

[0054] Figure 6 The diagram shows the wellbore pressure distribution under different tubing depths in a shale gas well.

[0055] Figure 7 The diagram shows the distribution of liquid holdup in the wellbore profile under different tubing depths in a shale gas well.

[0056] Figure 8 The graph shows the variation of wellbore fluid accumulation under different tubing depths in a shale gas well.

[0057] Figure 9 A schematic diagram of the shale gas well tubing depth optimization system according to an embodiment of the present invention is shown;

[0058] Figure 10 A schematic diagram of the device structure according to an embodiment of the present invention is shown. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0060] The shale gas well tubing depth optimization method of this invention, such as... Figure 1 As shown, the method includes:

[0061] S1. Collect basic data of shale gas wells; the basic data includes: formation data, wellbore parameter data, fluid composition data, production data, pressure drop test data, and production test data.

[0062] S2. Using a steady-state model, evaluate the pressure drop loss, fluid carrying capacity, and daily gas production at different tubing depths to determine the tubing depth plan, specifically including:

[0063] Pressure drop loss at different tubing depths is estimated using a pressure drop calculation model to assess energy consumption; the pressure drop calculation model can be either the Hagedorn-Brown model or the GRAY model.

[0064] The Turner model is used to calculate the critical fluid carrying velocity, determine whether there is fluid accumulation, and assess the stability of wellbore flow.

[0065] Using a linear regression model based on the production data, the daily gas production at different tubing depths is estimated.

[0066] Based on pressure drop loss, liquid carrying capacity, and daily gas production, the deep tubing plan was determined.

[0067] S3. Divide the production stages of shale gas wells and formulate optimization strategies, specifically including:

[0068] In the initial high-yield stage, maximize output and rationally control pressure loss;

[0069] During the medium-term decline phase, balance production and energy consumption, reduce pressure drop, and prevent the effects of liquid accumulation;

[0070] In the later low-production stage, reduce the risk of liquid accumulation, ensure stable flow, and maintain the remaining production.

[0071] S4. Based on the aforementioned basic data, establish a multiphase flow dynamic model for shale gas wellbore and verify its accuracy, specifically including:

[0072] S41. Based on the fluid component data, use Multiflash software to generate a fluid package for import into OLGA software;

[0073] S42. Obtain wellbore inflow dynamic data based on the pressure drop test data and production test data, and determine the inflow characteristics of the shale gas well by plotting the inflow dynamic curve (IPR curve) of bottom hole flowing pressure and production.

[0074] S43. Based on the wellbore parameter data, production data, fluid composition data and inflow characteristics of the shale gas well, establish a multiphase flow dynamic model of the shale gas wellbore in OLGA software;

[0075] S44. Using the aforementioned basic data and field test data, the multiphase flow dynamic model is tested and verified, specifically including:

[0076] Based on the test results of the multiphase flow dynamic model, the relative error between the daily gas production and the actual production data is calculated.

[0077] The reliability of the simulation results of the OLGA model is judged based on the relative error.

[0078] S5. Using the multiphase flow dynamic model, simulate the working conditions of different tubing depths, adjust the tubing depth position and record the simulation results; the simulation results include daily gas production, wellbore pressure distribution, wellbore profile liquid holdup, liquid accumulation and flow pattern.

[0079] S6. Based on the simulation results of the multiphase flow dynamic model and the optimization strategy, establish a multi-objective optimization model to optimize the tubing depth scheme, obtain the Pareto optimal solution, and determine the optimal tubing depth scheme, specifically including:

[0080] S61. Determine the optimization objective: Based on the simulation results of the multiphase flow dynamic model, the optimization objective is determined to be to maximize the output, minimize the energy loss, and achieve the optimal flow state.

[0081] Maximize production: By analyzing and recording the daily gas production under different tubing depth conditions, evaluate the gas production efficiency and select the tubing depth scheme with high daily gas production.

[0082] Minimize energy loss: Select locations with low pressure drop by evaluating wellbore pressure drop and energy loss under different tubing depth conditions;

[0083] Optimal flow state: By analyzing liquid holdup, liquid accumulation height, and flow pattern, the flow stability and liquid accumulation under different depth conditions are evaluated.

[0084] S62. Establish a multi-objective optimization model: Based on the simulation results of the multiphase flow dynamic model and the optimization strategy, establish a multi-objective optimization model, define the objective function, and thus quantify the different optimization performances of each tubing depth position;

[0085] S63. Optimize the tubing depth scheme: Optimize the tubing depth scheme using the multi-objective optimization model to obtain a set of Pareto optimal solutions; the optimal solution represents the tubing depth scheme that achieves a reasonable balance among different objectives; wherein, the multi-objective optimization algorithm can be the NSGA-II algorithm or the MOEA algorithm;

[0086] Each tubing depth plan is evaluated based on three optimization objectives: maximizing production, minimizing energy loss, and optimizing flow state. Through multiple iterative calculations, the optimal solution is automatically searched in the solution space, gradually approaching the Pareto front to ensure that each objective is reasonably balanced.

[0087] S64. Combining historical production data and expert experience, analyze various tubing depth options at the Pareto frontier, and use machine learning algorithms to determine the key factors affecting production and energy loss; determine the optimal tubing depth option based on production priorities. The machine learning algorithm can be a random forest algorithm.

[0088] The optimal tubing depth scheme was applied to actual shale gas horizontal well operations. The optimization effect was verified through field testing, and the tubing depth parameters in actual applications were continuously tracked and optimized.

[0089] Example 1

[0090] Using the methods described in the above embodiments, the method for deepening the tubing in a shale gas well is optimized, such as... Figures 2 to 8 As shown, the specific method is as follows:

[0091] Collect basic data on shale gas wells, including formation data, wellbore parameter data, fluid composition data, production data, pressure drop test data, and production test data.

[0092] The pressure drop loss at different tubing depths was estimated using the Hagedorn-Brown model.

[0093] The Turner model was used to calculate the critical fluid carrying velocity and evaluate the stability of wellbore flow.

[0094] Using a linear regression model based on the production data, the daily gas production at different tubing depths is estimated.

[0095] The pressure drop loss, fluid carrying capacity, and daily gas production at different tubing depths were evaluated using a steady-state model. Based on the scheme with low pressure drop loss, strong fluid carrying capacity, and high daily gas production, the tubing depth scheme was confirmed.

[0096] The production stages of shale gas wells are divided into an initial high-production stage, a mid-term declining stage, and a late low-production stage.

[0097] Based on the fluid composition data, Multiflash software is used to generate a fluid package for import into OLGA software, such as... Figure 2 As shown;

[0098] Based on the pressure drop test data and production test data, obtain the wellbore inflow dynamic data, and determine the inflow characteristics of the shale gas well by plotting the inflow dynamic curve (IPR curve) of bottom hole flowing pressure and production.

[0099] Based on wellbore parameter data, production data, fluid composition data, and inflow characteristics of shale gas wells, a multiphase flow dynamic model of the shale gas wellbore is established in OLGA software, such as... Figure 3 As shown;

[0100] The multiphase flow dynamic model was tested and verified using the aforementioned basic data and field test data.

[0101] Based on the test results of the multiphase flow dynamic model, the relative error between the daily gas production and the actual production data is calculated.

[0102] A multiphase flow dynamic model with a relative error of less than 15% was selected for dynamic simulation of different tubing depth conditions in the target shale gas well, such as... Figure 4 As shown.

[0103] Using the aforementioned multiphase flow dynamic model, simulations were performed for operating conditions at different tubing depths. The tubing depth was adjusted, and the daily gas production, wellbore pressure distribution, wellbore profile liquid holdup, liquid accumulation, and flow pattern were recorded for each simulation. Figures 5 to 8 As shown.

[0104] Based on the simulation results of the multiphase flow dynamic model, the optimization objectives are determined to be maximizing output, minimizing energy loss, and achieving optimal flow state.

[0105] Based on the simulation results of the multiphase flow dynamic model and the optimization strategy, a multi-objective optimization model is established, and the objective function is defined.

[0106] The NSGA-II algorithm was used to optimize the tubing depth scheme, and a set of Pareto optimal solutions were obtained;

[0107] By combining historical production data and expert experience, we analyzed various tubing depth schemes at the Pareto frontier and used the random forest algorithm to identify key factors affecting production and energy loss. Based on production priorities, we determined the optimal tubing depth scheme.

[0108] The optimal tubing depth scheme was applied to actual shale gas horizontal well operations. The optimization effect was verified through field testing, and the tubing depth parameters in actual applications were continuously tracked and optimized.

[0109] Based on the above method, embodiments of the present invention also provide a shale gas well tubing depth optimization system corresponding to the above method, such as... Figure 9 As shown, the system includes:

[0110] Data collection module 901 is used to collect basic data from shale gas wells;

[0111] The module 902 for confirming the tubing depth scheme is used to evaluate the pressure drop loss, liquid carrying capacity and daily gas production at different tubing depth positions using a steady-state model, and to determine the tubing depth scheme.

[0112] Module 903, which is used to divide the production stages of shale gas wells and formulate optimization strategies;

[0113] The multiphase flow dynamic model establishment and verification module 904 is used to establish a multiphase flow dynamic model of the shale gas wellbore based on the aforementioned basic data, and to verify its accuracy.

[0114] The multiphase flow dynamic simulation module 905 is used to simulate the working conditions of different tubing depths using the multiphase flow dynamic model, adjust the tubing depth position, and record the simulation results.

[0115] The module 906 for determining the optimal tubing depth scheme is used to establish a multi-objective optimization model based on the simulation results of the multiphase flow dynamic model and the optimization strategy, optimize the tubing depth scheme, obtain the Pareto optimal solution, and determine the optimal tubing depth scheme.

[0116] The present invention also provides a computer-readable storage medium storing a program or instructions that, when executed on a computer, cause the computer to perform the shale gas well tubing depth optimization method described in the above-described method embodiments.

[0117] like Figure 10 As shown, an embodiment of the present invention also provides a device, including: a processor 1001, the processor 1001 being coupled to a memory 1002, the processor 1001 being used to read and execute a computer program stored in the memory 1002 to implement the shale gas well tubing depth optimization method as described in the above method embodiment.

[0118] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the depth of tubing run-in in shale gas wells, characterized in that, The method includes: Collect basic data on shale gas wells; the basic data includes: formation data, wellbore parameter data, fluid composition data, production data, pressure drop test data, and production test data; The pressure drop loss, fluid carrying capacity and daily gas production at different tubing depths were evaluated using a steady-state model to determine the tubing depth scheme. Divide the production stages of shale gas wells and develop optimization strategies; Based on the aforementioned basic data, a dynamic model of multiphase flow in shale gas wellbore was established, and its accuracy was verified. Using the aforementioned multiphase flow dynamic model, simulations were performed on different tubing depths, the tubing depth was adjusted, and the simulation results were recorded. Based on the simulation results of the multiphase flow dynamic model and the optimization strategy, a multi-objective optimization model is established to optimize the tubing depth scheme, obtain the Pareto optimal solution, and determine the optimal tubing depth scheme.

2. The method according to claim 1, characterized in that, The optimal tubing depth scheme was applied to actual shale gas horizontal well operations, and the optimization effect was verified through field testing.

3. The method according to claim 1, characterized in that, The method of using a steady-state model to evaluate the pressure drop loss, fluid carrying capacity, and daily gas production at different tubing depths to determine the tubing depth plan specifically includes: The pressure drop loss at different depths of the tubing was estimated using a pressure drop calculation model, and the energy consumption was assessed. The Turner model was used to calculate the critical fluid carrying velocity and evaluate the stability of wellbore flow. Using a linear regression model based on the production data, the daily gas production at different tubing depths is estimated. Based on pressure drop loss, liquid carrying capacity, and daily gas production, the deep tubing plan was determined.

4. The method according to claim 1, characterized in that, The process of dividing shale gas wells into production stages and developing optimization strategies specifically includes: In the initial high-yield stage, maximize output and rationally control pressure loss; During the medium-term decline phase, balance production and energy consumption, reduce pressure drop, and prevent the effects of liquid accumulation; In the later low-production stage, reduce the risk of liquid accumulation, ensure stable flow, and maintain the remaining production.

5. The method according to claim 1, characterized in that, Based on the aforementioned fundamental data, a dynamic model of multiphase flow in the shale gas wellbore is established, and its accuracy is verified. This specifically includes: Based on the fluid composition data, Multiflash software is used to generate a fluid package for import into OLGA software; Based on the pressure drop test data and production test data, obtain the wellbore inflow dynamic data, and determine the inflow characteristics of the shale gas well by plotting the inflow dynamic curves of bottom hole flowing pressure and production. Based on the wellbore parameter data, production data, fluid composition data, and inflow characteristics of shale gas wells, a multiphase flow dynamic model of shale gas wells is established in OLGA software. The multiphase flow dynamic model was tested and verified using the aforementioned basic data and field test data.

6. The method according to claim 1, characterized in that, The multiphase flow dynamic model is tested and verified using the aforementioned basic data and field test data, specifically including: Based on the test results of the multiphase flow dynamic model, the relative error between the daily gas production and the actual production data is calculated. The reliability of the simulation results of the OLGA model is judged based on the relative error.

7. The method according to claim 1, characterized in that, Based on the simulation results of the multiphase flow dynamic model and the optimization strategy, a multi-objective optimization model is established to optimize the tubing depth scheme, obtain the Pareto optimal solution, and determine the optimal tubing depth scheme, specifically including: The optimization objectives are determined based on the simulation results of the multiphase flow dynamic model, which are to maximize output, minimize energy loss, and achieve optimal flow state. A multi-objective optimization model is established based on the simulation results of the multiphase flow dynamic model and the optimization strategy, and the objective function is defined. The tubing depth setting scheme is optimized by using the multi-objective optimization model to obtain a set of Pareto optimal solutions. By combining historical production data and expert experience, we analyzed various tubing depth options at the Pareto frontier and used machine learning algorithms to identify key factors affecting production and energy loss. Based on production priorities, we determined the optimal tubing depth option.

8. A shale gas well tubing depth optimization system, characterized in that, The system includes: The data collection module is used to collect basic data from shale gas wells; The module for confirming tubing depth schemes is used to evaluate the pressure drop loss, fluid carrying capacity, and daily gas production at different tubing depth locations using a steady-state model, and to determine the tubing depth scheme. The production stage segmentation module is used to segment shale gas wells into production stages and formulate optimization strategies. The multiphase flow dynamic model establishment and verification module is used to establish a multiphase flow dynamic model of the shale gas wellbore based on the aforementioned basic data, and to verify its accuracy. The multiphase flow dynamic simulation module is used to simulate the working conditions of different tubing depths using the multiphase flow dynamic model, adjust the tubing depth position, and record the simulation results. The module for determining the optimal tubing depth scheme is used to establish a multi-objective optimization model based on the simulation results of the multiphase flow dynamic model and the optimization strategy, optimize the tubing depth scheme, obtain the Pareto optimal solution, and determine the optimal tubing depth scheme.

9. A computer-readable storage medium, characterized in that, The system stores a program or instructions that, when executed on a computer, cause the computer to perform the shale gas well tubing depth optimization method as described in any one of claims 1-7.

10. A device, characterized in that, Includes a processor, which is coupled to a memory; The processor is used to read and execute the computer program stored in the memory to implement the shale gas well tubing depth optimization method as described in any one of claims 1-7.

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