Optimization method and equipment for injection and production scheme of gas storage with multiple constraints based on PSO

Abstract

The present invention discloses a method and computer equipment for optimizing injection and production schemes of gas storage reservoirs with multiple conditional constraints based on PSO, and relates to the field of energy storage technology. The method and computer equipment for optimizing injection and production schemes of gas storage reservoirs with multiple conditional constraints based on PSO mainly include: constructing a three-dimensional geological model and a fluid model of the gas storage reservoir according to the core data, well logging data, seismic data and static fluid data of the gas storage reservoir, fitting and inverting using numerical simulation methods and historical production data, selecting injection and production wells of the gas storage reservoir as injection and production optimization objects, constructing an optimization model, an operation objective function of the gas storage reservoir and multiple constraint conditions, and obtaining an optimal injection and production allocation plan using a particle swarm algorithm. Implementing the method and computer equipment for optimizing injection and production schemes of gas storage reservoirs with multiple conditional constraints based on PSO provided by the present invention can optimize the injection and production volume of a single well, realize the design of a gas storage allocation plan, and ensure the long-term stable and safe operation of the gas storage reservoir.

Description

Technical Field

[0001] The present invention relates to the field of energy storage technology, and more specifically to a PSO-based multi-condition constraint gas storage injection and production plan optimization method and computer equipment. Background Art

[0002] As a clean, efficient and green energy source, natural gas plays an important role in heating, power generation and other aspects. However, due to the obvious seasonality and daily fluctuations in its consumption, the natural gas supply system must be able to adapt to these rapid short-cycle changes. Gas storage effectively balances the difference between supply and demand by storing natural gas when demand is low and releasing it when demand peaks. Optimizing the injection and production operation of gas storage can not only improve gas storage efficiency and reduce operating costs, but also ensure the long-term stability and safety of gas storage, which is of great significance to maintaining the stability of the natural gas market and ensuring energy security. With the development of technology, a variety of technologies, including numerical simulation, intelligent control, big data analysis, etc., have been widely used in the operation optimization of gas storage to achieve more efficient, economical and environmentally friendly natural gas storage and supply.

[0003] Patent application CN112392473A discloses a method for evaluating the injection and production capacity of gas wells in low-permeability gas reservoirs. The method uses the gas material balance equation and the gas inflow and outflow equations to establish the relationship between the injected gas, produced gas volume and formation pressure, and then evaluates the injection and production capacity of the injection and production wells. The evaluation results effectively guide the operation and management of the gas storage, ensure the efficiency and safety of the gas storage operation, and use the previous cycle data to predict the next cycle indicators, thereby improving the timeliness and accuracy of the prediction.

[0004] Patent application CN111625922A discloses a large-scale reservoir injection and production optimization method based on a machine learning proxy model, which uses a machine learning-based proxy model to accelerate the computational efficiency of traditional evolutionary algorithms in numerical simulations. This method uses dimensionality reduction technology, especially Salmon mapping, to simplify the data preprocessing process and improve preprocessing efficiency. At the same time, a lightweight mathematical model based on a small number of samples is constructed to improve the calculation speed while ensuring the calculation accuracy. Afterwards, the high-dimensional interpolation technology in machine learning is used to quickly approximate complex reservoir systems and achieve optimized design. Furthermore, an evolutionary algorithm is used to dynamically update samples, fine-tune the model, and thus improve the accuracy of the proxy model, ultimately optimizing the production plan for oil and water wells.

[0005] At present, there are many in-depth studies in the fields of gas storage site selection and construction technology, reservoir geological evaluation, and safety risk management. In particular, many scholars have conducted relevant research on the optimization of gas storage operation. In 2002, Chen Jiaxing and others accurately described the changes in the pressure and water saturation of the gas storage during the injection process by constructing a dynamic mathematical model of injection and production. This method aims to minimize the power consumption of the gas storage compressor station, and uses the city's remaining natural gas demand and the injection pressure and water saturation of each well as constraints to calculate the optimal injection volume of each injection and production well in the gas storage. Based on this, the optimal operation plan of the gas storage in summer is formulated, providing theoretical guidance for the efficient injection and production of the gas storage. In 2010, based on the injection and production operation characteristics of salt cavern gas storage, Tan Yufei et al. considered the multi-cavity injection and production operation of gas storage as a connected system. Under the three constraints of meeting the peak-shaving demand of the city, preventing the injection and production wellbore of the solution cavity from being eroded by natural gas, and the minimum operating pressure of the solution cavity, the total pressure drop of the cavity group was minimized as the optimization goal. The model was solved based on the constrained complex optimization method to determine the optimal production volume of each single cavity in the storage. In 2017, Liu Jianing et al., based on the integrated simulation model of the above-ground and underground pressure system of the underground gas storage, comprehensively considered the influence of factors such as environment, geology and safety pressure, set the single well gas injection flow rate as a variable, and constructed a double-layer objective function based on the pipeline level. At the same time, the actual production requirements were set as constraints, and the NSGA_II algorithm was used to optimize the gas injection and production plan. In 2017, Wu Kexin and others constructed an optimization mathematical model under a stepped electricity price and applied the branch and bound method to solve it, verifying the feasibility of the model and algorithm. The proposed start-stop optimization scheme can effectively reduce electricity costs and maximize the economic benefits of the gas storage. In 2021, Zhou Jun and others established an optimization model with the goal of minimizing the variance of formation pressure between blocks. The model combines mathematical optimization technology, uses the number of wells opened in each block and the injection and production volume of a single well as decision variables, and considers constraints such as the total injection and production volume of the gas storage, the upper limit of the injection and production volume of a single well, and the maximum formation pressure of the block, and successfully obtains an annual working gas volume of 30×10 8 m 3 Optimal injection-production plan under certain conditions.

[0006] In general, injection and production optimization in the field of oil and gas field development is mostly aimed at oil field development, and the optimization algorithms are mostly particle swarm, multi-fidelity differential evolution and other algorithms combined with numerical simulation or machine learning application agent models for optimization. The optimization of multiple rounds of injection and production in gas storage is still limited to small-scale gas storage, and most of them focus on the economic efficiency of gas storage operation, and rarely consider the safety and stability of operation. The optimization research results obtained are difficult to effectively guide the safe and efficient production operation of actual large-scale gas storage. Summary of the invention

[0007] The purpose of the present invention is to provide a multi-condition constraint gas storage injection and production plan optimization method and computer equipment based on PSO, which can optimize the injection and production volume of a single well, realize the design of gas storage production plan, and ensure the long-term stable and safe operation of gas storage, especially large-scale depleted gas reservoir type gas storage.

[0008] The invention provides a method for optimizing injection and production scheme of a gas storage reservoir with multiple condition constraints based on PSO, comprising the following steps: S1: constructing a three-dimensional geological model and a fluid model of the gas storage reservoir according to core data, well logging data, seismic data and static fluid data of the gas storage reservoir; S2: obtaining the three-dimensional geological model and the fluid model of the gas storage reservoir after inversion by using a numerical simulation method and historical production data according to the three-dimensional geological model and the fluid model of the gas storage reservoir; S3: selecting injection and production wells of the gas storage reservoir as injection and production optimization objects according to the well network layout of the gas storage reservoir, and constructing a basic mathematical model for injection and production optimization calculation of the gas storage reservoir and a gas storage operation target function according to the inverted three-dimensional geological model and the fluid model of the gas storage reservoir and actual operation targets; S4: obtaining multiple constraint conditions according to the three-dimensional geological model of the gas storage reservoir, the historical production data and the operation characteristics of the gas storage reservoir; S5: obtaining an optimal injection and production allocation scheme according to the basic mathematical model for injection and production optimization calculation of the gas storage reservoir, the gas storage operation target function and the multiple constraint conditions by using a particle swarm algorithm.

[0009] Furthermore, step S1 specifically includes: S11: obtaining geological information of the gas storage reservoir based on the core data, logging data and seismic data of the gas storage reservoir; S12: constructing a three-dimensional geological model of the gas storage reservoir using the geological information of the gas storage reservoir; S13: constructing a fluid model of the gas storage reservoir based on the three-dimensional geological model of the gas storage reservoir and using static fluid data.

[0010] Furthermore, step S2 specifically includes: S21: according to the three-dimensional geological model and the fluid model of the gas storage reservoir, a numerical simulation method is used to perform fluid flow simulation to obtain key control factors that control fluid flow; S22: according to the key control factors that control fluid flow, the three-dimensional geological model and the fluid model of the gas storage reservoir are fitted and adjusted using historical production data to obtain fitting results; S23: according to the fitting results, a historical fitting method is used to obtain reservoir parameter distribution, and parameter inversion is performed according to the reservoir parameter distribution to obtain the inverted three-dimensional geological model and fluid model of the gas storage reservoir.

[0011] Furthermore, step S3 specifically includes: S31: obtaining injection and production wells of the gas storage according to the well network layout of the gas storage; S32: constructing a basic mathematical model for injection and production optimization calculation of the gas storage according to the obtained injection and production wells of the gas storage; S33: constructing a gas storage operation objective function according to the basic mathematical model for injection and production optimization calculation of the gas storage and the actual operation objectives of the gas storage.

[0012] Further, step S33 specifically includes: constructing a gas storage operation objective function according to the basic mathematical model of gas storage injection and production optimization calculation and the actual operation objective of the gas storage, such as the formula:

[0013] ,

[0014] in, is the formation pressure variance between blocks, , is the total number of gas injection blocks in the gas storage, is the total number of time periods, is the number of injection and production blocks, , is the injection-production time period, , For the After the cycle gas injection The formation pressure of the block, For the The average formation pressure of all blocks after periodic gas injection, is the average formation pressure at the end of gas production, is the average formation pressure at the end of gas injection, is the bottom hole pressure of a single well at the end of the injection and production period, The number of injection and production wells in the gas storage facility.

[0015] Furthermore, step S4 specifically includes: S41: calculating the maximum injection and production volume of each injection and production well according to the three-dimensional geological model of the gas storage reservoir and historical production data, and obtaining numerical simulation results; S42: constructing a linear relationship model between the weighted average pressure of the target reservoir of the gas storage reservoir and the target reservoir inventory gas volume according to the numerical simulation results; S43: obtaining multiple constraints according to the linear relationship model between the weighted average pressure of the target reservoir of the gas storage reservoir and the target reservoir inventory gas volume and the operating characteristics of the gas storage reservoir.

[0016] Further, step S43 specifically includes: obtaining multiple constraints according to the nonlinear relationship model between the weighted average pressure of the target reservoir of the gas storage and the gas volume in the target reservoir and the operation characteristics of the gas storage, such as the formula:

[0017] ,

[0018] ,

[0019] ,

[0020] ,

[0021] ,

[0022] ,

[0023] ,

[0024] in, For gas storage The total amount of gas injected during the cycle, For the The number of production days of the cycle, For the Cycle No. The total daily gas injection volume of the block, For the j The maximum daily gas injection volume of a single well in the block, is the bottom hole pressure, is the wellhead pressure of the tubing, is the oil pipe resistance coefficient, is the average temperature of the dynamic air column in the wellbore, is the average deviation coefficient of the dynamic gas column in the wellbore, is the inner diameter of the oil pipe, is the skin factor, is the relative density of natural gas, is the depth of the middle gas layer, is the formation pressure, is the gas injection volume of a single well, is the laminar flow coefficient, is the turbulence coefficient, For the Cycle No. The formation pressure of the block, is the maximum allowable formation working pressure of the gas storage reservoir, is the minimum allowable formation working pressure of the gas storage reservoir, For the Cycle No. The gas inventory of the block, For the The relationship coefficient between the formation pressure of the block and the amount of gas stored, is a constant term.

[0025] Furthermore, step S5 specifically includes: S51: based on the particle swarm algorithm, encoding the basic mathematical model of the gas storage reservoir injection and production optimization calculation to obtain a particle swarm; S52: randomly generating an initial population according to the particle swarm, the gas storage reservoir operation objective function and multiple constraints, and constructing a fitness function; S53: iteratively updating the speed and position of the particles according to the initial population and the fitness function to obtain the optimal particles; S54: performing inverse encoding according to the optimal particles to obtain the optimal injection and production allocation plan.

[0026] Furthermore, the multi-condition constrained gas storage injection and production plan optimization method based on PSO also includes: optimizing and adjusting the injection and production volume of each single well according to the optimal injection and production allocation plan to obtain the final optimal injection and production allocation plan.

[0027] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the above-mentioned PSO-based multi-condition constraint gas storage reservoir injection and production plan optimization method are implemented.

[0028] The implementation of the multi-condition constraint gas storage injection and production plan optimization method and computer equipment based on PSO provided by the present invention has the following beneficial effects:

[0029] The present invention establishes a three-dimensional geological model of a target gas storage reservoir through the interpretation results of basic geological and geophysical data such as cores, well logging, and seismic data; based on the three-dimensional geological model, numerical simulation technology is used to simulate and analyze the flow of multiphase fluid (gas+oil+water) inside the gas storage reservoir to control the key geological and engineering parameters of fluid movement in the gas storage reservoir; combining the gas reservoir production data before the reconstruction of the gas storage reservoir and the historical data of the operation of the gas storage reservoir after the reconstruction, the numerical simulation technology is used to perform historical fitting and then invert the reservoir parameters; determine the objective function of the injection and production optimization model of the gas storage reservoir, establish a linear relationship between the formation pressure and the stored gas volume, clarify the multi-constraint conditions of the injection and production optimization model of the gas storage reservoir, and construct a gas storage injection and production optimization proxy model; finally, the particle swarm optimization (PSO) algorithm is used to optimize the injection and production volume of a single well under the conditions of minimizing the formation pressure difference of each fault block of the gas storage reservoir and ensuring the safe and stable operation of the gas storage reservoir and the maximum working gas volume of the gas storage reservoir, so as to realize the design of the gas storage reservoir production allocation plan;

[0030] The optimization algorithm framework provided by the present invention, which combines the proxy model and the particle swarm optimization algorithm, avoids the complicated operations and high time cost of the conventional optimization injection-production method, has fast optimization speed, high efficiency and is easy to implement, and combines numerical simulation to realize a fast and reasonable gas storage periodic injection-production development plan, thereby improving the injection-production effect of the gas storage. It can be applied to large-scale gas storages, suitable for injection-production optimization of large-scale gas storages with a large number of wells, and takes the influence of fault closure into consideration, and optimizes from the perspective of safe and stable operation of the gas storage, which can ensure the long-term stable and safe operation of the gas storage, especially the large-scale depleted gas reservoir type gas storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0032] Figure 1 It is a flow chart of a method for optimizing injection and production scheme of a gas storage reservoir with multiple condition constraints based on PSO provided by the present invention;

[0033] Figure 2 It is a fitting diagram of the relationship between the inventory volume of each block of the gas storage and the formation pressure of the block provided by the present invention;

[0034] Figure 3 It is a comparison diagram of the pressure around the wells of each block before and after the optimization of gas injection at the end of the gas storage provided by the present invention;

[0035] Figure 4 It is a comparison diagram of formation pressure in each block before and after optimization of gas injection in the gas storage provided by the present invention;

[0036] Figure 5 It is a comparison diagram of the pressure around the wells of each block before and after the optimization of gas production at the gas storage provided by the present invention;

[0037] Figure 6 It is a comparison diagram of formation pressure in each block before and after the optimization of gas production in the gas storage provided by the present invention;

[0038] Figure 7 It is a structural block diagram of the computer device provided by the present invention. DETAILED DESCRIPTION

[0039] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, specific embodiments of the present invention are now described in detail with reference to the accompanying drawings.

[0040] Figure 1 A schematic diagram of a multi-condition constraint gas storage injection and production scheme optimization method based on PSO in this embodiment is shown. In this embodiment, the multi-condition constraint gas storage injection and production scheme optimization method based on PSO includes the following steps:

[0041] S1: Construct a three-dimensional geological model and fluid model of the gas storage reservoir based on the core data, well logging data, seismic data and static fluid data of the gas storage reservoir;

[0042] It should be noted that in step S1, based on the interpretation results of core, well logging, seismic and other data, a three-dimensional geological and fluid model of the gas storage reservoir is established, including models of faults, structures, porosity, permeability, initial gas saturation, etc.;

[0043] Specifically, step S1 includes:

[0044] S11: obtaining geological information of the gas storage reservoir based on core data, well logging data and seismic data of the gas storage reservoir;

[0045] In an exemplary embodiment, in step S11, core, well logging and seismic data of the gas storage are collected and sorted; the core data provides physical properties of the reservoir, such as porosity and permeability; the well logging data obtains the distribution of physical parameters such as porosity and saturation; the seismic data is used to identify the structural characteristics, fault distribution and main stratum information of the gas storage; these data are cleaned and analyzed, noise and outliers are removed, the accuracy and completeness of the data are ensured, and the geological information such as structural characteristics, fault distribution, and stratum of the gas storage is obtained;

[0046] S12: Using the geological information of the gas storage reservoir, construct a three-dimensional geological model of the gas storage reservoir;

[0047] In an exemplary embodiment, in step S12, a three-dimensional geological model of the gas storage reservoir is established using geological data such as cores, well logging and seismic data of the gas storage reservoir; the model includes spatial distribution of key physical parameters such as porosity, permeability, initial gas saturation, etc. of the reservoir; the spatial distribution of these parameters can reflect important information such as gas storage capacity and fluid flow characteristics of the reservoir;

[0048] S13: constructing a gas storage reservoir fluid model based on the three-dimensional geological model of the gas storage reservoir and using static fluid data;

[0049] In an exemplary embodiment, in step S13, based on basic geological data, that is, on the basis of the three-dimensional geological model, the fluid model of the gas storage is further established in combination with static fluid data; the static fluid data includes initial gas distribution, pressure, gas saturation, etc.; these data help the fluid model simulate the flow characteristics of gas in the gas storage and predict the dynamic changes that may occur during the gas injection and production process; by integrating geological and fluid models, the dynamic behavior of the gas storage can be more fully understood, and the injection and production strategy and the long-term operation effect of the gas storage can be optimized;

[0050] S2: Based on the three-dimensional geological model and fluid model of the gas storage reservoir, the inverted three-dimensional geological model and fluid model of the gas storage reservoir are obtained by using numerical simulation methods and historical production data;

[0051] In an exemplary embodiment, in step S2, numerical simulation is performed based on historical production data to achieve parameter inversion based on historical matching results;

[0052] Specifically, step S2 includes:

[0053] S21: Based on the three-dimensional geological model and fluid model of the gas storage reservoir, a numerical simulation method is used to simulate the fluid flow and obtain the key control factors controlling the fluid flow;

[0054] In an exemplary embodiment, in step S21, after constructing a three-dimensional geological model and a fluid model of the gas storage reservoir, a fluid flow simulation is performed using numerical simulation; through this simulation, the flow path, diffusion mode, pressure change and other processes of the gas in the reservoir can be intuitively displayed; during the simulation process, the influence of various parameters on the fluid flow will be analyzed, such as the porosity, permeability, viscosity and phase permeability curve of the reservoir; through the simulation results, the key control factors controlling the fluid flow can be identified, such as the main physical parameters affecting the gas injection and production efficiency;

[0055] S22: fitting and adjusting the three-dimensional geological model of the gas storage reservoir and the fluid model of the gas storage reservoir according to the key control factors controlling the fluid flow and using the historical production data to obtain the fitting results;

[0056] In an exemplary embodiment, in step S22, the model is fitted and adjusted using historical production data by adjusting key parameters until the numerical simulation results are consistent with the historical production results; that is, in order to ensure the accuracy and predictive ability of the fluid model, the model is fitted and adjusted in combination with actual historical production data; first, based on the key control factors in the fluid flow simulation, the relevant model parameters in the model, such as porosity, permeability, phase permeability curve and compressibility, are gradually adjusted; after the adjustment, the accuracy of the model is verified by comparing the simulation results with the actual historical production data; if there is a difference between the two, the model parameters are further adjusted until the simulation results are consistent with the historical production data;

[0057] S23: according to the fitting results, a history fitting method is used to obtain reservoir parameter distribution, and parameter inversion is performed according to the reservoir parameter distribution to obtain an inverted three-dimensional geological model and a fluid model of the gas storage reservoir;

[0058] In an exemplary embodiment, in step S23, based on the above steps, a history matching method is used to obtain reservoir parameter distribution and implement parameter inversion; that is, after completing the history matching of the model, the reservoir parameters can be inverted using the fitting results; through the history matching process, the model can accurately characterize the spatial distribution of physical parameters in the reservoir, such as permeability, porosity, and gas saturation; the inversion process is to infer the actual underground reservoir characteristics by adjusting the model parameters to match the historical production data;

[0059] S3: According to the well network layout of the gas storage, the injection and production wells of the gas storage are selected as the injection and production optimization objects. According to the inverted three-dimensional geological model of the gas storage, the fluid model of the gas storage and the actual operation objectives, the basic mathematical model of the gas storage injection and production optimization calculation and the gas storage operation objective function are constructed;

[0060] In an exemplary embodiment, in step S3, a gas storage reservoir injection-production well is selected as an injection-production optimization object, and an objective function of a gas storage reservoir injection-production optimization model is determined;

[0061] Specifically, step S3 includes:

[0062] S31: obtaining injection and production wells of the gas storage according to the well network layout of the gas storage;

[0063] In an exemplary embodiment, in step S31, the well pattern layout of the gas storage is analyzed to evaluate the distribution and operation of each injection and production well and its influence on the overall fluid flow of the reservoir; by comparing the historical production data, those wells with stable injection and production performance are screened out, and it is ensured that these wells show good operation capabilities under different working conditions; that is, representative injection and production wells that meet the parameter requirements are screened out, and these wells are required to have historical production data and show stable injection and production capabilities under different operating conditions;

[0064] In an exemplary embodiment, 56 representative wells are selected and divided into three categories according to injection and production capacity, wherein the first category of wells has an average daily gas production of more than 8a or a gas production per unit pressure drop of more than 27b; the second category of wells has an average daily gas production of between 5a and 8a and a gas production per unit pressure drop of between 16b and 27b; the third category of wells has an average daily gas production of less than 5a and a gas production per unit pressure drop of less than 16b;

[0065] S32: constructing a basic mathematical model for optimizing injection and production calculation of the gas storage according to the obtained injection and production wells of the gas storage;

[0066] In an exemplary embodiment, in step S32, after the representative injection and production wells are determined, a basic mathematical model for injection and production optimization calculation of the gas storage reservoir is constructed based on the operation conditions of these wells, and the injection and production parameters, operation restrictions and mutual influence between wells of each injection and production well are determined; the key parameters of each well, such as injection and production gas volume, pressure conditions, injection and production rate, etc., need to be determined in the model, and the operation restrictions of each well, such as the lower limit of formation pressure, equipment capacity, etc., need to be clarified;

[0067] In an exemplary embodiment, the operation restrictions and key parameter settings of each well are formed by a plurality of constraints that restrict each other, thereby ensuring the reasonable injection and production operation of a single well;

[0068] S33: constructing a gas storage operation objective function according to a basic mathematical model for gas storage injection and production optimization calculation and the actual operation objectives of the gas storage;

[0069] In an exemplary embodiment, in step S33, the gas storage operation objective function is set according to the actual operation target of the gas storage, such as maximizing the injection and production volume, minimizing the operation cost, or the most stable and safe operation, etc.; when setting, it is necessary to comprehensively consider the production efficiency and the safety and stability of the gas storage, and avoid inappropriate injection and production rate causing instability of the target formation or damage to the equipment, such as avoiding too high injection and production rate causing too fast formation pressure drop or equipment overload damage;

[0070] In an exemplary embodiment, an injection-production optimization model is established with the objective function of minimizing the formation pressure variance between blocks after each injection-production cycle, and the overall formation pressure equilibrium change of a large-scale depleted gas reservoir gas storage is taken as the goal to ensure the long-term safe and stable operation of the gas storage, increase the working gas volume of the gas storage, and carry out optimization research under multiple constraints; the objective function expression is as follows:

[0071]

[0072] Where: is the formation pressure variance between blocks, , unit is MPa 2 ; is the total number of gas injection blocks in the gas storage facility; is the total number of time periods; is the number of injection and production blocks, ; is the injection-production time period, ; For the After the cycle gas injection The formation pressure of the block, in MPa; For the The average formation pressure of all blocks after periodic gas injection, in MPa; is the average formation pressure at the end of gas production, in MPa; is the average formation pressure at the end of gas injection, in MPa; is the bottom hole pressure of a single well at the end of the injection and production period, in MPa; The number of injection and production wells for the gas storage facility;

[0073] S4: Based on the three-dimensional geological model of the gas storage reservoir, historical production data and the operation characteristics of the gas storage reservoir, multiple constraints are obtained;

[0074] In an exemplary embodiment, in step S4, the maximum injection and production volume of the injection and production well is calculated by a numerical simulation method, and the nonlinear relationship between the formation pressure and the stored gas volume is fitted, so as to determine the constraint conditions of the injection and production optimization model of the gas storage reservoir;

[0075] Specifically, step S4 includes:

[0076] S41: Calculate the maximum gas injection and production volume of each injection and production well based on the three-dimensional geological model of the gas storage reservoir and historical production data, and obtain numerical simulation results;

[0077] In an exemplary embodiment, in step S41, the maximum injection and production volume of each injection and production well is calculated based on the constructed three-dimensional geological model of the gas storage reservoir and the historical production data of the injection and production wells; this step combines the physical properties of the reservoir (such as porosity, permeability) and actual production records, and uses numerical simulation tools to predict pressure changes and fluid flows under different injection and production conditions; the focus is on ensuring that the bottom hole pressure and formation pressure will not exceed the bearing limit of the reservoir during the injection and production process to avoid formation instability or rupture; at the same time, the maximum injection and production volume of the gas also needs to take into account the operating capacity of the equipment and the influence of well-to-well interference; the maximum injection and production volume of the 56 wells selected in this embodiment is divided into three categories according to the three categories of wells, the maximum injection and production volume of the first category well is greater than 7c, the maximum injection and production volume of the second category well is between 4c~7c, and the maximum injection and production volume of the third category well is between 3c~4c;

[0078] S42: constructing a linear relationship model between the weighted average pressure of the target reservoir of the gas storage and the gas volume in the target reservoir according to the numerical simulation results;

[0079] In an exemplary embodiment, in step S42, based on the numerical simulation results of the gas storage, a linear relationship model between the weighted average pressure of the target reservoir of the gas storage and the target reservoir inventory gas volume is established, and the inventory gas changes under different pressure conditions are analyzed, that is, through the dynamic simulation of the reservoir, a mathematical relationship model between the formation pressure and the gas volume in the gas storage is established; the model describes the correlation between the formation pressure and the gas storage inventory changes under different injection and production rates and injection and production cycles; based on the simulation results, the inventory gas volume changes under different pressure conditions are analyzed to determine the gas storage capacity at each stage; this relationship model is an important tool for optimizing injection and production operations, and can provide a prediction of the formation pressure change trend under different injection and production conditions to ensure the safety and effective operation of the gas storage;

[0080] In an exemplary embodiment, there are a large number of faults in the study area. Considering the closure and connectivity of the faults, the study area is divided into multiple blocks, and the relationship between the formation pressure and the inventory gas volume is fitted in each block; the relationship between the inventory volume and the block formation pressure is fitted based on production data (injection and production gas volume, wellhead oil pressure) combined with numerical simulation, and the fitting results are as follows: Figure 2 As shown, the fitting relationship of the four blocks is as follows:

[0081]

[0082]

[0083]

[0084]

[0085] S43: obtaining multiple constraint conditions according to a linear relationship model between a target reservoir weighted average pressure of the gas storage and the target reservoir storage gas volume and the operation characteristics of the gas storage;

[0086] Specifically, step S43 includes: obtaining multiple constraints according to the linear relationship model between the weighted average pressure of the target reservoir of the gas storage and the gas volume in the target reservoir and the operation characteristics of the gas storage, such as the formula:

[0087] ,

[0088] ,

[0089] ,

[0090] ,

[0091] ,

[0092] ,

[0093] ,

[0094] in, For gas storage The total amount of gas injected during the cycle, For the The number of production days of the cycle, For the Cycle No. The total daily gas injection volume of the block, For the j The maximum daily gas injection volume of a single well in the block, is the bottom hole pressure, is the wellhead pressure of the tubing, is the oil pipe resistance coefficient, is the average temperature of the dynamic air column in the wellbore, is the average deviation coefficient of the dynamic gas column in the wellbore, is the inner diameter of the oil pipe, is the skin factor, is the relative density of natural gas, is the depth of the middle gas layer, is the formation pressure, is the gas injection volume of a single well, is the laminar flow coefficient, is the turbulence coefficient, For the Cycle No. The formation pressure of the block, is the maximum allowable formation working pressure of the gas storage reservoir, is the minimum allowable formation working pressure of the gas storage reservoir, For the Cycle No. The gas inventory of the block, For the The relationship coefficient between the formation pressure of the block and the amount of gas stored, is a constant term;

[0095] In an exemplary embodiment, in step S43, multiple constraints are determined, including formation pressure limit, injection and production speed limit, well-to-well interference, gas reservoir capacity limit and related constraints for safe operation, that is, multiple constraints are set in combination with the operating characteristics of the gas storage to ensure the safety and stability of the injection and production optimization calculation; the constraints include the minimum and maximum allowable values ​​of formation pressure, the upper and lower limits of the injection and production speed of a single well, the total amount of injected and produced gas, the actual limit of the gas reservoir capacity, and the relevant equipment safety operation requirements; in particular, the limits on formation pressure and injection and production speed must be strictly observed to prevent reservoir rupture or equipment overload; in addition, factors such as formation heterogeneity and fault effects must also be considered to ensure that the injection and production process operates within a safe range;

[0096] In an exemplary embodiment, considering the stability of the fault layers in each block, constraints are imposed from four aspects: gas injection and production volume, maximum single well gas injection and production volume, maximum and minimum formation pressure of the block, and relationship between formation pressure of the block and gas volume in stock; during the gas injection production process of the gas storage, the sum of the gas injection volume of each block must meet the overall gas injection volume requirement of the gas storage. The expression of this constraint is as follows:

[0097]

[0098] Where: For gas storage The total amount of gas injected during the cycle, in units of 10 4 m 3 / d; For the The production days of the cycle, in days (d); For the Cycle No. The total daily gas injection volume of the block, in 10 4 m 3 / d;

[0099] The gas injection volume of a single well in each block is subject to the maximum gas injection capacity of the single well. The expression of this constraint is shown in formula (7). The maximum gas injection capacity of a single well is determined by the vertical pipe flow equation of the wellbore and the stable seepage equation of the formation. The node analysis method divides the gas storage injection system into the inflow part from the wellhead to the bottom of the well and the outflow part from the bottom of the well to the formation. The vertical pipe flow inflow curve and the formation seepage outflow curve are solved according to formulas (8) and (10) respectively. The intersection of the inflow and outflow dynamic curves is the maximum gas injection volume of the single well.

[0100]

[0101]

[0102]

[0103]

[0104] Where: For the j The maximum daily gas injection volume of a single well in the block, in 10 4 m 3 / d; is the bottom hole pressure, in MPa; is the wellhead pressure of the oil pipe, in MPa; is the oil pipe resistance coefficient; is the average temperature of the dynamic air column in the wellbore, in K; is the average deviation coefficient of the dynamic gas column in the wellbore; is the inner diameter of the oil pipe, in cm; is the skin factor, is the relative density of natural gas; is the depth of the middle of the gas layer, in m; is the formation pressure, in MPa; is the gas injection volume of a single well, in units of 10 4 m 3 / d; is the laminar flow coefficient, unit is MPa / (10 4 m 3 / d); is the turbulence coefficient, in MPa 2 / (10 4 m 3 / d) 2 ;

[0105] In order to ensure the safe and stable operation of the gas storage, during the gas injection process, the formation pressure of the block cannot be greater than the maximum formation working pressure allowed by the design, and during the gas production process, the formation pressure of the block should not be less than the minimum formation working pressure allowed by the design; the expression of this constraint is:

[0106]

[0107] Where: For the Cycle No. The formation pressure of the block, in MPa; is the maximum allowable formation working pressure of the gas storage, in MPa; is the minimum allowable formation working pressure of the gas storage, in MPa;

[0108] As the gas injection work proceeds, the formation pressure of the gas storage block will continue to rise. In order to facilitate the solution of the optimization model, the production data fitting method is used to calculate the change of the formation pressure of the gas storage block with the storage gas volume. The expression of this constraint is:

[0109]

[0110] Where: For the Cycle No. The gas inventory of the block, in units of 10 8 m 3 ; For the The relationship coefficient between the formation pressure of the block and the amount of gas stored; is a constant term;

[0111] S5: Based on the basic mathematical model of gas storage injection and production optimization calculation, gas storage operation objective function and multiple constraints, the optimal injection and production allocation plan is obtained by using the particle swarm algorithm;

[0112] In an exemplary embodiment, in step S5, based on a multi-objective particle swarm algorithm, the gas storage single-cycle injection and production optimization problem is encoded, an initial population is randomly generated, particle fitness is calculated, particle velocity and position are updated, and an optimized production allocation result is obtained to optimize the injection and production volume of a single well;

[0113] Specifically, step S5 includes:

[0114] S51: Based on the particle swarm algorithm, the basic mathematical model of gas storage injection and production optimization calculation is encoded to obtain a particle swarm;

[0115] In an exemplary embodiment, in step S51, based on the particle swarm algorithm, the gas storage reservoir injection and production optimization problem is encoded according to the requirements of the particle swarm algorithm, and each particle is defined to represent a different combination of injection and production schemes, including parameters such as injection volume, production volume, injection rate, and production rate. That is, before performing particle swarm optimization, the injection and production optimization problem of the gas storage reservoir is first encoded; each particle represents a possible combination of injection and production schemes, specifically including key parameters such as injection and production volume, injection and production rate, and well selection; by combining these parameters, a variety of different injection and production strategies can be generated to provide diversity for subsequent optimization; this embodiment selects the injection and production rates and block injection and production volumes of 56 wells as particles, dynamically selects the opening and closing states of the wells, and dynamically forms different parameter combinations according to the constraints of step S3;

[0116] S52: According to the particle swarm, the gas storage operation objective function and multiple constraints, the initial population is randomly generated and the fitness function is constructed;

[0117] In an exemplary embodiment, in step S52, based on the gas storage optimization objective function, the fitness function of the particle swarm algorithm is determined, the fitness is evaluated based on the objective function and the constraint conditions, the initial population is randomly generated, and the fitness of each particle is calculated, that is, after the encoding is completed, the fitness function of the particle swarm algorithm is defined; after the initial population is randomly generated, the fitness of each particle is calculated; the fitness evaluation is based on the set objective function, such as maximizing the injection and production volume, and considering the satisfaction of the constraint conditions, such as formation pressure limit, injection and production rate limit, etc.; by evaluating each particle scheme, its advantages and disadvantages under the current parameter settings are determined; particles with high fitness indicate that their combination scheme can more effectively achieve the preset goals, while particles with low fitness need to be improved in subsequent iterations;

[0118] In an exemplary embodiment, a single objective function is used for optimization, and minimizing the formation pressure variance between blocks is set as the objective function for optimization. The fitness of the objective function is calculated according to the particles set in step S51, and it is iterated step by step until the objective function value reaches convergence;

[0119] S53: According to the initial population and fitness function, iteratively update the speed and position of the particles to obtain the optimal particles;

[0120] Specifically, step S53 includes: iteratively updating the speed and position of the particles according to the initial population and the fitness function to obtain the optimal particle, as shown in the formula:

[0121] ,

[0122] ,

[0123] in, For the Iteration Particle The flight velocity vector Dimensional component; For the Iteration Particle The position vector Dimensional component; , is the acceleration constant, used to adjust the maximum learning step size; , are two random functions with a value range of [0, 1], used to increase randomness; is the inertia weight, which is a non-negative number and is used to adjust the search range of the solution space; For particles i Individual The best position of the dimensional component, For the population The best position of the dimension component.

[0124] In an exemplary embodiment, in step S53, the speed and position of the particle are updated through multiple iterations until the fitness of the particle group reaches a predetermined threshold or reaches a maximum number of iterations, and finally the optimal particle is obtained, that is, after the fitness calculation is completed, the multiple iterations are entered; according to the current speed and position of the particle, its speed and position are updated to explore a better injection and production scheme; in each iteration, the speed of the particle is adjusted to take into account its historical optimal position and the optimal position of all particles to ensure that it can move effectively in the solution space; the iteration continues until the predetermined fitness threshold or the maximum number of iterations is reached; finally, the particle with the highest fitness is screened out; the particle speed and particle position update formula in this embodiment are as follows:

[0125]

[0126]

[0127] Where: For the Iteration Particle The flight velocity vector Dimensional component; For the Iteration Particle The position vector Dimensional component; , is the acceleration constant, which adjusts the maximum learning step size; , are two random functions with a value range of [0, 1] to increase randomness; is the inertia weight, a non-negative number, which adjusts the search range of the solution space; For particles i Individual The best position of the dimensional component, For the population The best position of the dimension component.

[0128] S54: According to the optimal particles, reverse coding is performed to obtain the optimal injection and production allocation plan;

[0129] In one embodiment, the multi-condition constraint gas storage injection and production plan optimization method based on PSO further includes: optimizing and adjusting the injection and production volume of each single well according to the optimal injection and production allocation plan to obtain a final optimal injection and production allocation plan;

[0130] In an exemplary embodiment, in step S54, the optimal particle combination is reverse-coded to obtain the final optimal injection-production allocation plan, and finally the injection-production gas volume of each single well is optimized and adjusted;

[0131] Specifically, after obtaining the optimal particle, it is de-encoded back to the actual injection and production plan; the de-coding process is to convert the particle parameter value into actual physical quantities such as injection volume, production volume, injection and production rate, so as to obtain the optimal injection and production allocation plan for the gas storage reservoir; this plan can maximize the injection and production efficiency of the gas storage reservoir under the premise of meeting the constraints; finally, further fine-tuning is made for the specific injection and production volume of each single well in the gas storage reservoir to ensure the operability of the overall plan and the optimization of the actual execution effect; each iteration updates the particle speed and position to increase the function fitness value until the fitness value threshold or the number of iterations is met to select the particle group with the highest fitness value. On this basis, according to the actual production situation, the production allocation of high-capacity wells and low-capacity wells is appropriately adjusted in each block, and the overall injection and production volume of the block remains unchanged, meeting the production requirements from the actual situation, and forming the final optimization plan; the comparison effect between the injection and production optimization plan and the actual injection and production plan is as follows Figure 3-Figure 6 As shown in the figure, the formation pressure at the end of injection and production of the optimized scheme is lower, the formation pressure at the end of production is higher, the pressure change between blocks is smaller, the optimized predicted bottom hole pressure (PBHP) of the single well at the end of injection is lower than the bottom hole pressure (TBHP) of the actual injection and production scheme, and the optimized predicted bottom hole pressure of the single well at the end of production is higher than the bottom hole pressure of the actual injection and production scheme, which further increases the working gas volume range of gas injection and production.

[0132] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the above-mentioned PSO-based multi-condition constraint gas storage injection and production plan optimization method are implemented.

[0133] like Figure 7As shown, the computer device may include: at least one processor 121, such as a CPU (Central Processing Unit), at least one communication interface 123, a memory 124, and at least one communication bus 122. Among them, the communication bus 122 is used to realize the connection and communication between these components. Among them, the communication interface 123 may include a display screen (Display), a keyboard (Keyboard), and the optional communication interface 123 may also include a standard wired interface and a wireless interface. The memory 124 may be a high-speed RAM memory (Random Access Memory, volatile random access memory), or a non-volatile memory (non-volatile memory), such as at least one disk memory. The memory 124 may also be at least one storage device located away from the aforementioned processor 121. Among them, the memory 124 stores an application program, and the processor 121 calls the program code stored in the memory 124 to perform any of the above method steps. Among them, the communication bus 122 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The communication bus 122 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7Only one line is used to represent it, but it does not mean that there is only one bus or one type of bus. Among them, the memory 124 may include a volatile memory (English: volatile memory), such as a random access memory (English: random-access memory, abbreviated: RAM); the memory may also include a non-volatile memory (English: non-volatile memory), such as a flash memory (English: flash memory), a hard disk drive (English: hard disk drive, abbreviated: HDD) or a solid-state drive (English: solid-state drive, abbreviated: SSD); the memory 124 may also include a combination of the above-mentioned types of memory. Among them, the processor 121 may be a central processing unit (English: central processing unit, abbreviated: CPU), a network processor (English: network processor, abbreviated: NP) or a combination of CPU and NP. Among them, the processor 121 may further include a hardware chip. The above-mentioned hardware chip may be an application-specific integrated circuit (English: application-specific integrated circuit, abbreviated: ASIC), a programmable logic device (English: programmable logic device, abbreviated: PLD) or a combination thereof. The PLD can be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof. Optionally, the memory 124 is also used to store program instructions. The processor 121 can call the program instructions to implement the multi-condition constraint gas storage injection and production plan optimization method based on PSO as in this embodiment.

[0134] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A multi-condition constrained gas storage injection and production plan optimization method based on PSO, characterized in that: The following steps are involved: S1: Construct a three-dimensional geological model and fluid model of the gas storage reservoir based on the core data, well logging data, seismic data and static fluid data of the gas storage reservoir; S2: according to the three-dimensional geological model and fluid model of the gas storage reservoir, using numerical simulation methods and historical production data, obtaining the inverted three-dimensional geological model and fluid model of the gas storage reservoir; S3: According to the well network layout of the gas storage, the injection and production wells of the gas storage are selected as injection and production optimization objects, and according to the inverted three-dimensional geological model of the gas storage, the gas storage fluid model and the actual operation target, the basic mathematical model of the gas storage injection and production optimization calculation and the gas storage operation target function are constructed; S4: obtaining multiple constraint conditions according to the three-dimensional geological model of the gas storage reservoir, the historical production data and the operation characteristics of the gas storage reservoir; S5: according to the basic mathematical model for optimizing the injection and production calculation of the gas storage, the gas storage operation objective function and the multiple constraints, using a particle swarm algorithm to obtain an optimal injection and production allocation plan; Step S2 specifically includes: S21: According to the three-dimensional geological model of the gas storage reservoir and the fluid model of the gas storage reservoir, a fluid flow simulation is performed using a numerical simulation method to obtain key control factors for controlling the fluid flow; S22: fitting and adjusting the three-dimensional geological model of the gas storage reservoir and the fluid model of the gas storage reservoir according to the key control factors for controlling the fluid flow and using historical production data to obtain fitting results; S23: according to the fitting result, using the history fitting method to obtain reservoir parameter distribution, performing parameter inversion according to the reservoir parameter distribution, and obtaining a three-dimensional geological model and a fluid model of the gas storage after inversion; Step S4 specifically includes: S41: Calculating the maximum gas injection and production volume of each injection and production well according to the three-dimensional geological model of the gas storage reservoir and the historical production data to obtain numerical simulation results; S42: constructing a linear relationship model between the weighted average pressure of the target reservoir of the gas storage and the gas volume in the target reservoir according to the numerical simulation results; S43: obtaining multiple constraint conditions according to the linear relationship model between the weighted average pressure of the target reservoir of the gas storage and the storage gas volume of the target reservoir and the operation characteristics of the gas storage.

2. The multi-condition constrained gas storage injection and production plan optimization method based on PSO according to claim 1 is characterized in that: Step S1 specifically includes: S11: obtaining geological information of the gas storage reservoir based on core data, well logging data and seismic data of the gas storage reservoir; S12: constructing a three-dimensional geological model of the gas storage reservoir using the geological information of the gas storage reservoir; S13: constructing a gas storage reservoir fluid model based on the three-dimensional geological model of the gas storage reservoir and using static fluid data.

3. The method for optimizing injection and production plans of gas storage reservoirs with multiple constraints based on PSO according to claim 1 is characterized in that: Step S3 specifically includes: S31: obtaining injection and production wells of the gas storage according to the well network layout of the gas storage; S32: constructing a basic mathematical model for optimizing injection and production calculation of the gas storage according to the obtained gas storage injection and production wells; S33: Constructing a gas storage operation objective function according to the basic mathematical model of the gas storage injection and production optimization calculation and the actual operation objectives of the gas storage.

4. The method for optimizing injection and production plans of gas storage reservoirs with multiple constraints based on PSO according to claim 3 is characterized in that: Step S33 specifically includes: constructing a gas storage operation objective function according to the basic mathematical model of the gas storage injection and production optimization calculation and the actual operation objective of the gas storage, such as the formula: ， in, is the formation pressure variance between blocks, , is the total number of gas injection blocks in the gas storage, is the total number of time periods, is the number of injection and production blocks, , is the injection-production time period, , For the After the cycle gas injection The formation pressure of the block, For the The average formation pressure of all blocks after periodic gas injection, is the average formation pressure at the end of gas production, is the average formation pressure at the end of gas injection, is the bottom hole pressure of a single well at the end of the injection and production period, The number of injection and production wells in the gas storage facility.

5. The method for optimizing injection and production plans of gas storage with multiple constraints based on PSO according to claim 1 is characterized in that: Step S43 specifically includes: obtaining multiple constraints according to the linear relationship model between the weighted average pressure of the target reservoir of the gas storage and the gas volume in the target reservoir and the operation characteristics of the gas storage, such as the formula: ， ， ， ， ， ， ， in, For gas storage The total amount of gas injected during the cycle, For the The number of production days of the cycle, For the Cycle No. The total daily gas injection volume of the block, For the j The maximum daily gas injection volume of a single well in the block, is the bottom hole pressure, is the wellhead pressure of the tubing, is the oil pipe resistance coefficient, is the average temperature of the dynamic air column in the wellbore, is the average deviation coefficient of the dynamic gas column in the wellbore, is the inner diameter of the oil pipe, is the skin factor, is the relative density of natural gas, is the depth of the middle gas layer, is the formation pressure, is the gas injection volume of a single well, is the laminar flow coefficient, is the turbulence coefficient, For the Cycle No. The formation pressure of the block, is the maximum allowable formation working pressure of the gas storage reservoir, is the minimum allowable formation working pressure of the gas storage reservoir, For the Cycle No. The gas inventory of the block, For the The relationship coefficient between the formation pressure of the block and the amount of gas stored, is a constant term.

6. The multi-condition constrained gas storage injection and production plan optimization method based on PSO according to claim 1 is characterized in that: Step S5 specifically includes: S51: Based on the particle swarm algorithm, the basic mathematical model of the gas storage reservoir injection and production optimization calculation is encoded to obtain a particle swarm; S52: randomly generating an initial population according to the particle swarm, the gas storage operation objective function and the multiple constraints, and constructing a fitness function; S53: iteratively updating the speed and position of particles according to the initial population and the fitness function to obtain the optimal particle; S54: Decoding is performed according to the optimal particles to obtain an optimal injection-production allocation plan.

7. The method for optimizing injection and production plans of gas storage based on PSO with multiple constraints according to claim 1 is characterized in that: The PSO-based multi-condition constraint gas storage injection and production plan optimization method also includes: optimizing and adjusting the injection and production volume of each single well according to the optimal injection and production allocation plan to obtain a final optimal injection and production allocation plan.

8. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for optimizing the injection and production plan of a gas storage reservoir with multiple condition constraints based on PSO are implemented as described in any one of claims 1 to 7.

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