Fracturing construction parameter dual-objective optimization method for offshore condensate gas well vertical well separate layer fracturing

Abstract

The invention belongs to a condensate gas well fracturing construction technology, belongs to the field of ocean engineering equipment manufacturing, and particularly discloses a fracturing construction parameter double-objective optimization method for offshore condensate gas well vertical well separate layer fracturing, which comprises the following steps of: extracting a typical development unit based on reservoir development characteristics, high-stress interlayer distribution condition and limit fracture height; engineering parameter center combination design for crack propagation simulation; crack propagation simulation; based on a crack propagation simulation result, crack propagation-productivity integrated numerical simulation is carried out; establishing a crack propagation-productivity prediction integrated proxy model; calculating the offshore fracturing cost; and double-target fracturing parameter intelligent optimization is carried out by taking yield maximization and cost minimization as targets. According to the method, on the basis of the fracture propagation-productivity prediction integrated agent model and the NSGA-II algorithm, the two-year accumulated oil and gas equivalent maximization and the fracturing cost minimization serve as targets, fracturing parameter double-target optimization is carried out, and the fracturing cost can be minimized while the yield maximization can be achieved through the method.

Description

Technical Field

[0001] The present invention relates to a condensate gas well fracturing construction technology, belongs to the field of marine engineering equipment manufacturing, and specifically relates to a dual-objective optimization method for fracturing construction parameters for vertical well stratified fracturing of offshore condensate gas wells. Background Art

[0002] Offshore condensate reservoirs exhibit poor physical properties, strong vertical heterogeneity, and low natural productivity, necessitating fracturing to generate high-yield oil and gas flows. Currently, predicting the post-fracturing productivity of condensate reservoirs remains challenging, and optimization of fracturing parameters is also difficult due to the constraints of offshore platform construction. Therefore, a method is needed to accurately predict the post-fracturing productivity of condensate reservoirs, balance post-fracturing production with fracturing costs, and ultimately achieve global parameter optimization, laying the foundation for optimal fracturing strategies.

[0003] Currently, conventional simulations of the post-fracturing productivity of condensate gas wells generally artificially assume fracture parameters and then conduct numerical simulations. However, there is limited research on the coupled simulation of fracture expansion and production dynamics, which makes it difficult to establish a mapping relationship between fracturing parameters and post-fracturing effects. Furthermore, for the dynamic simulation of multiple fracture expansion in vertical wells and condensate gas well production, the coupled modeling and solution are difficult, computationally time-consuming, and computationally inefficient. Furthermore, in terms of fracturing parameter optimization, traditional optimization methods are typically single-objective oriented and utilize the control variable method for optimization. This approach makes it difficult to consider the mutual influence between parameters and cannot balance post-fracturing production and fracturing costs. Therefore, it is necessary to combine machine learning models and intelligent optimization algorithms to establish a dual-objective optimization method for fracturing construction parameters for stratified fracturing of offshore condensate gas wells. This method aims to maximize production while minimizing fracturing costs, thereby providing guidance for fracturing construction in offshore condensate gas wells. Summary of the Invention

[0004] To address the problems existing in the prior art, the present invention provides a dual-objective optimization method for fracturing construction parameters for stratified fracturing of vertical offshore condensate gas wells. Based on an integrated agent model for fracture extension and productivity prediction and the NSGA-II algorithm, dual-objective optimization of fracturing parameters is carried out with the goals of maximizing the two-year cumulative oil and gas equivalent production and minimizing the fracturing cost, thereby addressing the problems mentioned in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a dual-objective optimization method for fracturing operation parameters for vertical stratified fracturing of offshore condensate gas wells, comprising the following steps:

[0006] S1. Extract typical development units based on reservoir development characteristics, distribution of high-stress interlayers, and critical fracture height;

[0007] S2, Central combination design of engineering parameters for crack propagation simulation;

[0008] S3, crack propagation simulation;

[0009] S4. Based on the fracture propagation simulation results, conduct fracture propagation-productivity integrated numerical simulation;

[0010] S5. Establishment of an integrated agent model for crack propagation and production capacity prediction;

[0011] S6. Offshore fracturing cost calculation;

[0012] S7. Intelligent optimization of dual-objective fracturing parameters with the goals of maximizing production and minimizing costs.

[0013] Preferably, in step S1, it specifically includes:

[0014] S11. Determine the vertical distribution characteristics of the gas layer based on the well logging interpretation results;

[0015] S12. Input the well logging curve into the Gohfer software to calculate the minimum horizontal principal stress along the wellbore direction. Identify the interlayer using the longitudinal stress difference greater than 7 MPa and thickness greater than 10 m as the standard. Preliminary division of the vertical development unit is based on the distribution characteristics of the interlayer.

[0016] S13. For the divided development units, establish a geological model for fracture propagation simulation. Combined with previous offshore platform fracturing experience and platform construction constraints, clarify the value ranges of the three fracturing operation parameters: displacement, sand injection intensity, and fluid intensity.

[0017] S14. Using the upper limit of the fracturing construction parameters, conduct a simulation of crack expansion under extreme construction conditions to obtain the longitudinal expansion of the cracks under extreme construction conditions; if the hydraulic fracture does not penetrate the interlayer, it indicates that the divided development unit is valid; if the hydraulic fracture longitudinally penetrates the interlayer, the development unit is re-divided according to the fracture height under extreme construction conditions.

[0018] Preferably, in step S13, the geological model for fracture propagation simulation has model attributes including porosity, permeability, water saturation, minimum horizontal principal stress, maximum horizontal principal stress, vertical stress, Young's modulus and Poisson's ratio;

[0019] The specific ranges of the three fracturing construction parameters, displacement, sand addition intensity and fluid intensity, are as follows: displacement: 3-6m 3 / min, sand adding intensity 0.4-0.8m 3 / m, liquid strength 4-8m 3 / m.

[0020] Preferably, in step S2, the minimum value of each key fracturing parameter is taken as the low level, the maximum value of each key fracturing parameter is taken as the high level, and the average value of the maximum and minimum values of each key fracturing parameter is taken as the medium level; a central composite design of 3 factors and 3 levels is further carried out to obtain 17 simulation schemes.

[0021] Preferably, in step S3, different fracturing construction parameter combinations obtained according to the 3-factor 3-level center combination design are used as input parameters for the divided development units, and the GOHFER fracturing software is used to carry out crack propagation simulation.

[0022] Preferably, in step S4, for the divided development units, a conceptual geological model is established based on CMG software, and based on the indoor PVT experimental test results of flash experiments, constant mass expansion experiments (CCE), and isochoric depletion experiments (CVD) carried out on the condensate gas reservoir fluid samples in the study area, PVT fitting is carried out using the Winprop module of CMG software to establish a full-component fluid model; the fracture morphology and conductivity parameters simulated by the GOHFER software are output as a fracture parameter file with a suffix of .cmgfrac, and the fracture parameter file is imported into the conceptual geological model in the CMG software; a fracture extension-production capacity integration numerical simulation is carried out in a depletion development manner to obtain the cumulative oil production and cumulative gas production for two years, and the two-year cumulative oil and gas production are further used to calculate the two-year cumulative oil and gas equivalent.

[0023] Preferably, in step S5, the following is specifically included:

[0024] S51. Construct a data set by taking the three key fracturing parameters, displacement, sand addition intensity, and fluid intensity, as independent variables and the two-year cumulative oil and gas equivalent production as the response variable;

[0025] S52, divide the data set into training set and test set in a ratio of 8:2;

[0026] S53. Based on the support vector machine model (SVM), the training set was used to construct a mapping relationship between displacement, sand addition intensity, and fluid intensity and the two-year cumulative oil and gas equivalent production, and an integrated agent model for fracture extension and production capacity prediction was established.

[0027] S54. Use the test set to verify the established integrated agent model for fracture propagation and production capacity prediction until the average relative error of the model training set is less than 5% and the average relative error of the test set is less than 15%, and then stop training the model.

[0028] Preferably, in step S6, a calculation method for the fracturing cost is established based on the towing fee, vehicle crew fee and material fee of the offshore fracturing cost. The calculation expression of the fracturing cost is as follows:

[0029] Exp=Exp拖船 +Exp 车组 +Exp 陶粒 +Exp 石英砂 +Exp 瓜胶

[0030] Where, Exp 拖船 ,Exp 车组 ,Exp 陶粒 ,Exp 石英砂 and Exp 瓜胶 They represent tugboat costs, vehicle set costs, expanded clay costs, quartz sand costs and guar gum costs respectively.

[0031] Preferably, in step S7, the three fracturing parameters of displacement, sand addition intensity and fluid intensity are taken as variables to be optimized, and the upper and lower limits of the value range of these parameters are used as constraints. Based on the integrated agent model of crack extension and production capacity prediction and the fracturing cost calculation formula, combined with the NSGA-II algorithm, dual-objective fracturing parameter intelligent optimization with the goals of maximizing production and minimizing cost is carried out to obtain the optimization results of the three parameters of displacement, sand addition intensity and fluid intensity.

[0032] The beneficial effects of the present invention are as follows: based on the integrated agent model of fracture extension and productivity prediction and the NSGA-II algorithm, the present invention carries out dual-objective optimization of fracturing parameters with the goals of maximizing the two-year cumulative oil and gas equivalent production and minimizing the fracturing cost. The method of the present invention can maximize production while minimizing the fracturing cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a schematic diagram of the technical route of the dual-objective optimization method for fracturing operation parameters for vertical stratified fracturing of offshore condensate gas wells;

[0034] Figure 2 Some fracture propagation simulation results of a typical development unit with discontinuous thin interbeds: (a) Case 2; (b) Case 5; (c) Case 7; (d) Case 10; (e) Case 13; (f) Case 15;

[0035] Figure 3 Schematic diagram of the model, (a) geological model; (b) hydraulic fracture model;

[0036] Figure 4 This is a schematic diagram of the cumulative oil and gas equivalent production over two years;

[0037] Figure 5 This is the cross plot of the simulated value and the model calculated value of the two-year cumulative oil and gas equivalent production;

[0038] Figure 6 This is a diagram of the Pareto frontier solution set;

[0039] Figure 7 Schematic diagram of Pareto frontier solution set, ideal solution and optimal solution. DETAILED DESCRIPTION

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0041] Example 1

[0042] A dual-objective optimization method for fracturing operation parameters for vertical stratified fracturing of offshore condensate gas wells, such as Figure 1 As shown, the following steps are included:

[0043] (1) Comprehensively consider the reservoir development characteristics, distribution of high-stress interlayers and critical fracture height, and extract typical development units;

[0044] First, the vertical distribution characteristics of the gas strata were determined based on well logging interpretation. Furthermore, the well logging curves were input into Gohfer software to calculate the minimum horizontal principal stress along the wellbore. Interlayers were identified based on a longitudinal stress differential greater than 7 MPa and a thickness greater than 10 m. Based on the interlayer distribution characteristics, vertical development units were preliminarily divided.

[0045] A geological model for fracture propagation simulation was established for each development unit. Model attributes included porosity, permeability, water saturation, minimum and maximum horizontal principal stresses, vertical stress, Young's modulus, and Poisson's ratio. Furthermore, based on previous experience with fracturing operations on offshore platforms and the constraints imposed by platform construction conditions, the ranges for three key fracturing parameters—displacement rate, sand injection intensity, and fluid injection intensity—were determined. The corresponding ranges are shown in Table 1.

[0046] Table 1 Single variable parameter range

[0047]

[0048] For each development unit, using the upper limit of the hydraulic fracturing parameters, we simulated fracture propagation under extreme conditions to study the longitudinal expansion of fractures under these conditions. If the hydraulic fractures did not penetrate the interlayer, the development unit was considered valid. If the hydraulic fractures did penetrate the interlayer, the development unit was re-divided based on the fracture height under extreme conditions.

[0049] (2) Central combination design of engineering parameters for crack propagation simulation;

[0050] The minimum value of each key fracturing parameter is taken as the low level, the maximum value of each key fracturing parameter is taken as the high level, and the average of the maximum and minimum values of each key fracturing parameter is taken as the medium level, as shown in Table 2.

[0051] Table 2 3-factor 3-level table

[0052]

[0053]

[0054] A central composite design with three factors and three levels was further carried out, and 17 simulation schemes were obtained, as shown in Table 3.

[0055] Table 3 17 simulation scenarios determined by 3 factors and 3 levels

[0056]

[0057] (3) Crack propagation simulation based on Gohfer software;

[0058] According to the different fracturing construction parameter combinations obtained by the 3-factor 3-level center combination design, different fracturing construction parameter combinations are used as input parameters for the divided development units, and the GOHFER fracturing software is used to carry out crack propagation simulation.

[0059] (4) Based on the actual crack propagation simulation results, conduct numerical simulation of crack propagation-productivity integration;

[0060] For the demarcated development units, a conceptual geological model was established using CMG software. Based on the results of laboratory PVT experiments, including flash evaporation experiments, constant-content expansion experiments (CCE), and constant-volume depletion experiments (CVD), conducted on fluid samples from the condensate reservoirs in the study area, PVT fitting was performed using the Winprop module of CMG software to establish a full-component fluid model. The fracture morphology and conductivity parameters simulated using the GOHFER software were exported as a fracture parameter file with the suffix .cmgfrac. This fracture parameter file was then imported into the conceptual geological model within CMG software. Numerical simulations of fracture expansion and productivity integration were conducted using a depletion-based development approach to obtain the cumulative oil and gas production over two years of production. These cumulative oil and gas production over two years were then used to calculate the cumulative oil and gas equivalent production over two years.

[0061] Standard oil and gas equivalent is the oil and gas production converted based on the calorific value of crude oil and natural gas, generally taken as 1255m 3 Natural gas = 1 ton of crude oil. Therefore, the cumulative oil and gas equivalent production over two years can be calculated using formula (1).

[0062]

[0063] Where Q is the cumulative oil and gas equivalent produced in two years, t; Qo is the cumulative oil produced in two years, t; Qg is the cumulative gas produced in two years, m 3 .

[0064] (5) Establishment of an integrated agent model for crack propagation and production capacity prediction;

[0065] A data set was constructed using three key fracturing parameters: displacement, sand injection intensity, and fluid application intensity, as independent variables, and two-year cumulative oil and gas equivalent production as the response variable. The data set was divided into training and test sets in an 8:2 ratio. Using the training set, a mapping relationship between displacement, sand injection intensity, and fluid application intensity and two-year cumulative oil and gas equivalent production was constructed using a support vector machine (SVM) model. This integrated proxy model for fracture propagation and productivity prediction was then established. This model was further validated using the test set. If the average relative error of the model training set exceeded 5% and the average relative error of the test set exceeded 15%, the model was retrained until the model error met the aforementioned criteria.

[0066] (6) offshore fracturing cost calculation;

[0067] Considering that the initial costs of offshore fracturing operations are complex and already incurred, cost optimization is not possible. Therefore, the fracturing cost is calculated directly. A fracturing cost calculation method was established, taking into account the towboat, vehicle, and material costs of offshore fracturing. The fracturing cost calculation is expressed as Equation (2).

[0068] Exp=Exp 拖船 +Exp 车组 +Exp 陶粒 +Exp 石英砂 +Exp 瓜胶 (2)

[0069] Where, Exp 拖船 ,Exp 车组 ,Exp 陶粒 ,Exp 石英砂 and Exp 瓜胶 They represent tugboat costs, vehicle set costs, expanded clay costs, quartz sand costs and guar gum costs respectively.

[0070] (7) Intelligent optimization of fracturing parameters with dual objectives of maximizing production and minimizing costs;

[0071] The three key fracturing parameters, namely displacement, sand addition intensity and fluid intensity, are taken as variables to be optimized, and the upper and lower limits of the value range of these parameters are used as constraints, as shown in Table 4.

[0072] Table 4 Single variable parameter range

[0073]

[0074] Based on an integrated agent model for fracture propagation and productivity prediction and a fracturing cost calculation formula, combined with the NSGA-II algorithm, a dual-objective intelligent optimization of fracturing parameters is conducted, aiming to maximize production and minimize costs. This dual-objective optimization method yields an ideal solution for maximizing production and minimizing costs, along with its closest optimal solution. This optimal solution includes optimized displacement, sand injection intensity, and fluid intensity. The optimization results can be used to guide the selection of parameters for actual fracturing.

[0075] Example 2

[0076] A specific implementation case of the present invention is given below.

[0077] A bay basin contains a gas condensate reservoir with an extremely high condensate content, in which Well W1 is a development well. The 4526.6-4572.6 m interval in this well is designated as a typical development unit of discontinuous thin interbeds.

[0078] Based on the scale limitations of offshore fracturing operations, the distribution ranges of displacement, sand addition intensity, and fluid intensity were clarified, and then the three levels of high, medium, and low were determined for the three parameters, as shown in Table 5.

[0079] Table 5 3-factor 3-level table

[0080]

[0081] A central composite design with three factors and three levels was further developed, as shown in Table 6 .

[0082] Table 6 Central composite design simulation scheme

[0083]

[0084] Based on the fracturing operation parameter combination designed by the combination of 3 factors and 3 horizontal centers, the fracture propagation simulation was carried out for the typical development unit of discontinuous thin interbeds. Some simulation results are shown in the figure below. Figure 2 shown.

[0085] Furthermore, a geological model was established for a typical development unit, and hydraulic fracture modeling was carried out based on the fracture propagation simulation results, as shown in the schematic diagram. Figure 3 The numerical simulation of fracture expansion and production capacity integration was carried out in the form of depletion development. The two-year cumulative oil and gas equivalent production of each scheme is shown as follows: Figure 4 shown.

[0086] The three key fracturing parameters, displacement, sand addition intensity, and fluid intensity, were used as independent variables, and the two-year cumulative oil and gas equivalent production was used as the response variable. The training set and test set were divided into a ratio of 8:2. Based on the SVM algorithm, an integrated agent model for fracture extension and production capacity prediction was established using the training set, and the model was validated using the test set. The results are as follows: Figure 5 The average relative errors of the training set and test set are 1.57% and 12.7% respectively, so the model can accurately calculate the two-year cumulative oil and gas equivalent production.

[0087] Finally, using displacement, sand injection intensity, and fluid intensity as the variables to be optimized, a dual-objective intelligent optimization of fracturing parameters was conducted with the goals of maximizing production and minimizing costs, based on an integrated proxy model for fracture propagation and productivity prediction and a fracturing cost calculation formula, combined with the NSGA-II algorithm. The constraints for displacement, sand injection intensity, and fluid intensity are shown in Table 7.

[0088] Table 7 Upper and lower limits of fracturing parameters to be optimized

[0089]

[0090] For a typical development unit with discontinuous thin interbeds, the Pareto optimization method was used to solve the dual-objective function model based on the two-year cumulative oil and gas equivalent production and fracturing cost. A total of 1,000 Pareto solutions were obtained. The solution set is as follows: Figure 6 As shown in the figure, the blue dots are Pareto front solutions. The Pareto front solution set contains 70 feasible solutions, and an optimal solution is further obtained. The cumulative oil and gas production in two years corresponding to the optimal solution is 2.1×10 4 t, the fracturing cost is 1.3238 million yuan, such as Figure 7 The optimal solution corresponds to the fracturing engineering parameters: the fluid intensity is 4m 3 / m, the sand adding strength is 0.59m 3 / m, displacement is 4.5m 3 / min.

[0091] The present invention is based on an integrated agent model for fracture expansion and productivity prediction and the NSGA-II algorithm. With the goals of maximizing the two-year cumulative oil and gas equivalent production and minimizing the fracturing cost, dual-objective optimization of fracturing parameters is carried out. The method of the present invention can maximize production while minimizing fracturing costs.

[0092] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0093] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "an", "the" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise.

[0094] It should be understood that the term "and / or" as used herein is merely a description of the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0095] The word "if," as used herein, may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0096] The references to "first" and "second" in the embodiments merely distinguish similar objects and do not represent a specific ordering of the objects. It is understood that the specific order or precedence of "first" and "second" can be interchanged where appropriate. It should be understood that the objects distinguished by "first" and "second" can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein.

[0097] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A dual-objective optimization method for fracturing operation parameters for vertical stratified fracturing of offshore condensate gas wells, characterized in that: The steps include: S1. Extract typical development units based on reservoir development characteristics, distribution of high-stress interlayers, and critical fracture height; S2, Central combination design of engineering parameters for crack propagation simulation; S3, crack propagation simulation; S4. Based on the fracture propagation simulation results, conduct fracture propagation-productivity integrated numerical simulation; S5. Establishment of an integrated agent model for crack propagation and production capacity prediction; S6. Offshore fracturing cost calculation; S7. Intelligent optimization of dual-objective fracturing parameters with the goals of maximizing production and minimizing costs.

2. The dual-objective optimization method for fracturing operation parameters for vertical stratified fracturing of offshore condensate gas wells according to claim 1, characterized in that: In step S1, it specifically includes: S11. Determine the vertical distribution characteristics of the gas layer based on the well logging interpretation results; S12. Input the well logging curve into the Gohfer software to calculate the minimum horizontal principal stress along the wellbore direction. Identify the interlayer using the longitudinal stress difference greater than 7 MPa and thickness greater than 10 m as the standard. Preliminary division of the vertical development unit is based on the distribution characteristics of the interlayer. S13. For the divided development units, establish a geological model for fracture propagation simulation. Combined with previous offshore platform fracturing experience and platform construction constraints, clarify the value ranges of the three fracturing operation parameters: displacement, sand injection intensity, and fluid intensity. S14. Using the upper limit of the fracturing construction parameters, conduct a simulation of crack expansion under extreme construction conditions to obtain the longitudinal expansion of the cracks under extreme construction conditions; if the hydraulic fracture does not penetrate the interlayer, it indicates that the divided development unit is valid; if the hydraulic fracture longitudinally penetrates the interlayer, the development unit is re-divided according to the fracture height under extreme construction conditions.

3. The dual-objective optimization method for fracturing operation parameters for vertical stratified fracturing of offshore condensate gas wells according to claim 2, characterized in that: In step S13, the geological model for fracture propagation simulation has model attributes including porosity, permeability, water saturation, minimum horizontal principal stress, maximum horizontal principal stress, vertical stress, Young's modulus, and Poisson's ratio; The specific ranges of the three fracturing construction parameters, displacement, sand addition intensity and fluid intensity, are as follows: displacement: 3-6m 3 / min, sand adding intensity 0.4-0.8m 3 / m, liquid strength 4-8m 3 / m.

4. The dual-objective optimization method for fracturing operation parameters for vertical stratified fracturing of offshore condensate gas wells according to claim 1, characterized in that: In step S2, the minimum value of each key fracturing parameter is taken as the low level, the maximum value of each key fracturing parameter is taken as the high level, and the average value of the maximum and minimum values of each key fracturing parameter is taken as the medium level; a central composite design of 3 factors and 3 levels is further carried out to obtain 17 simulation schemes.

5. The dual-objective optimization method for fracturing operation parameters for vertical stratified fracturing of offshore condensate gas wells according to claim 1, characterized in that: In step S3, different fracturing construction parameter combinations obtained from the 3-factor 3-level center combination design are used as input parameters for the divided development units, and the GOHFER fracturing software is used to carry out crack propagation simulation.

6. The dual-objective optimization method for fracturing operation parameters for vertical stratified fracturing of offshore condensate gas wells according to claim 1, characterized in that: In step S4, a conceptual geological model is established for the divided development units using CMG software. Based on the indoor PVT test results of flash evaporation experiments, constant-volume expansion experiments (CCE), and constant-volume depletion experiments (CVD) conducted on fluid samples of the condensate gas reservoir in the study area, PVT fitting is performed using the Winprop module of CMG software to establish a full-component fluid model. The fracture morphology and conductivity parameters simulated by the GOHFER software are output as a fracture parameter file with the suffix .cmgfrac, and this fracture parameter file is imported into the conceptual geological model in the CMG software. An integrated numerical simulation of fracture expansion and production capacity was carried out using a depletion-based development approach to obtain the cumulative oil and gas production over two years. The two-year cumulative oil and gas production were then used to calculate the two-year cumulative oil and gas equivalent.

7. The dual-objective optimization method for fracturing operation parameters for vertical stratified fracturing of offshore condensate gas wells according to claim 1, characterized in that: In step S5, the specific steps include: S51. Construct a data set by taking the three key fracturing parameters, displacement, sand addition intensity, and fluid intensity, as independent variables and the two-year cumulative oil and gas equivalent production as the response variable; S52, divide the data set into training set and test set in a ratio of 8:2; S53. Based on the support vector machine model (SVM), the training set was used to construct a mapping relationship between displacement, sand addition intensity, and fluid intensity and the two-year cumulative oil and gas equivalent production, and an integrated agent model for fracture extension and production capacity prediction was established. S54. Use the test set to verify the established integrated agent model for fracture propagation and production capacity prediction until the average relative error of the model training set is less than 5% and the average relative error of the test set is less than 15%, and then stop training the model.

8. The dual-objective optimization method for fracturing operation parameters for vertical stratified fracturing of offshore condensate gas wells according to claim 1, characterized in that: In step S6, a calculation method for the fracturing cost is established based on the towing fee, vehicle crew fee, and material fee of the offshore fracturing cost. The calculation expression for the fracturing cost is as follows: Exp=Exp 拖船 +Exp 车组 +Exp 陶粒 +Exp 石英砂 +Exp 瓜胶 Where, Exp 拖船 ,Exp 车组 ,Exp 陶粒 ,Exp 石英砂 and Exp 瓜胶 They represent tugboat costs, vehicle set costs, expanded clay costs, quartz sand costs and guar gum costs respectively.

9. The dual-objective optimization method for fracturing operation parameters for vertical stratified fracturing of offshore condensate gas wells according to claim 1, characterized in that: In step S7, the three fracturing parameters, namely displacement, sand addition intensity, and fluid use intensity, are selected as variables to be optimized. The upper and lower limits of the value ranges of these parameters are used as constraints. Based on the integrated agent model of crack extension and production capacity prediction and the fracturing cost calculation formula, combined with the NSGA-II algorithm, a dual-objective intelligent optimization of fracturing parameters with the goals of maximizing production and minimizing costs is carried out to obtain the optimization results of the three parameters, namely displacement, sand addition intensity, and fluid use intensity.

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