A method for optimizing injection-production capacity of a horizontal well of a gas reservoir type gas storage

Abstract

The application discloses a kind of gas reservoir type gas storage horizontal well injection-production capacity optimization method, comprising: S1: according to gas reservoir type gas storage basic requirement and the sensitivity analysis of production capacity influencing factor, determine key index;S2: according to gas reservoir engineering and fluid mechanics related theory, establish the gas phase mathematical model and water phase mathematical model of fractured gas reservoir type underground gas storage;S3: according to gas phase mathematical model and water phase mathematical model, build gas storage horizontal well injection-production optimization model;S4: solve gas storage horizontal well injection-production optimization model, and calculate key index according to the solution result;S5: according to the key index obtained, give optimization scheme in combination with specific gas storage and horizontal well injection-production parameter.The application establishes quantifiable mathematical optimization model based on improved differential evolution algorithm, can scientifically guide the optimization scheme of gas reservoir type gas storage, and improved differential evolution algorithm can guarantee the model solving speed on the basis of, give consideration to the global optimality of optimization result.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas field development, specifically relating to a method for optimizing the injection and production capacity of horizontal wells in gas reservoirs based on an improved differential evolution algorithm. Background Technology

[0002] Gas-storage type gas storage is the main type of natural gas storage facility. It is mainly used to ensure the security of natural gas supply and meet seasonal peak demand, and is an important component of the natural gas storage and transportation system.

[0003] Due to my country's complex geological conditions, gas reservoirs often utilize horizontal wells to enhance the injection and production capacity of individual wells and the well-controlled storage capacity. With the rapid development of gas reservoir construction in China, the focus has shifted from relatively homogeneous gas reservoirs to more complex, highly heterogeneous fractured gas reservoirs. Therefore, accurately predicting the injection and production capacity of different horizontal wells for various geological conditions, especially for highly heterogeneous fractured gas reservoirs, and guiding the well network and well type design of fractured gas reservoirs, is a significant challenge during the design phase. Existing optimization methods for the injection and production capacity of gas reservoirs are mainly designed for relatively homogeneous porous or fracture-porous gas reservoirs. These methods are somewhat inadequate for fractured gas reservoirs with well-developed fractures and high heterogeneity. In particular, key parameters such as the absolute permeability of fractures, the relative permeability of gas and water phases in fractures, and the amount of gas diffusion caused by the concentration difference between fractures and matrix have not been identified and characterized, which affects the optimization of injection and production capacity of horizontal wells in fractured gas reservoirs. Therefore, it is necessary to establish a new method that considers multiple factors to optimize the production of horizontal wells in fractured gas reservoirs.

[0004] Publication CN113128882A discloses a method for evaluating the injection and production capacity of horizontal wells in gas reservoirs based on triangular plots. This method includes: conducting sensitivity analysis of factors influencing production capacity based on production capacity calculation formulas to determine key production capacity indicators; determining a coordinate system and drawing a triangular plot based on these key indicators, followed by dimensionality reduction processing of the triangular plot; setting multiple different key indicator data points and calculating multiple production capacity data points based on these data points and the production capacity calculation formulas; interpolating these multiple production capacity data points and projecting them onto the triangular plot coordinate system to draw a color temperature map; and drawing contour lines on the color temperature map based on the calculation results to refine the plot. This method can quickly and accurately predict the injection and production capacity of different horizontal wells under different geological conditions, guiding the design of well networks and well types in gas reservoirs. However, in practice, this technology mainly focuses on the rapid evaluation of injection and production capacity based on the plot, that is, evaluating the theoretically achievable maximum capacity (maximum volume) of injection or production wells. In actual production, injection and production will not be carried out using the theoretical maximum capacity calculated by this method. On the one hand, the safety of wellbore erosion by gas flow needs to be considered, and on the other hand, the injection and production allocation should be chosen to achieve the maximum economic benefits. Therefore, how to optimize the injection and production capacity in a reasonable way to achieve the optimal solution for safety and economy is still an urgent problem to be solved. Summary of the Invention

[0005] The purpose of this invention is to solve the above-mentioned problems existing in the prior art and to provide a method for optimizing the injection and production capacity of horizontal wells in gas reservoirs. This invention establishes a quantifiable mathematical optimization model based on an improved differential evolution algorithm, which can scientifically guide the optimization scheme of gas reservoirs. The improved differential evolution algorithm can ensure the model solution speed while taking into account the global optimality of the optimization results.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A method for optimizing the injection and production capacity of horizontal wells in a gas reservoir type gas storage facility, characterized by the following steps:

[0008] Step S1: Based on the basic requirements for the construction of gas reservoir-type gas storage facilities and the sensitivity analysis of factors affecting production capacity, key indicators are determined;

[0009] Step S2: Based on the relevant theories of gas reservoir engineering and fluid mechanics, establish the gas phase mathematical model and water phase mathematical model of fractured gas reservoir type underground gas storage.

[0010] Step S3: Construct an optimization model for injection and production of horizontal wells in a gas storage facility based on the gas phase mathematical model and the aqueous phase mathematical model;

[0011] Step S4: Solve the injection and production optimization model of the horizontal well in the gas storage tank based on the differential evolution algorithm, and calculate the key indicators based on the solution results;

[0012] Step S5: Based on the obtained key indicators, and in combination with the specific gas storage and horizontal well injection and production parameters, provide an optimization plan.

[0013] In step S1, the key indicators determined include single-well gas injection capacity, single-well gas production capacity, formation pressure at the end of the gas injection period, formation pressure at the end of the gas production period, reservoir construction period, cumulative gas production at the end of the reservoir construction period, and improved production rate at the end of the reservoir construction period.

[0014] In step S1, the impact on production capacity includes the impact of formation coefficient, formation water, formation pressure, stress sensitivity, temperature, and other factors.

[0015] In step S2, the established mathematical models for the gas phase and aqueous phase are as follows:

[0016] Gas phase:

[0017]

[0018] Aqueous phase:

[0019]

[0020] In equations (1) and (2), Absolute permeability vector of the fracture, μm 2 ;K rgf K rwf Φ represents the relative permeability of the gas phase and water phase in the fracture, respectively; gf Φ wf S represents the potential of the gas and water phase fluids in the fracture, respectively; gf S wf Gas and water saturation in the crack; φ f Rock porosity; B g B w Volume index of gas and water; μ g μ w Viscosity of gas and water; q g q w The flow rate of air and water; Γ gmf ,Γ wmf These represent the flow rates of gas and water caused by the pressure difference between the crack and the rock block, respectively; Γ gdmf This represents the amount of gas diffusion caused by the concentration difference between the crack and the rock block.

[0021] In step S3, an injection-production optimization model for horizontal wells in a gas storage facility is constructed with the objective function of maximizing the net present value (NPV) within n years after the gas storage facility is put into operation.

[0022] In step S3, the constructed optimization model for injection and production in the horizontal well of the gas storage facility is as follows:

[0023]

[0024]

[0025]

[0026]

[0027]

[0028]

[0029]

[0030]

[0031]

[0032]

[0033]

[0034]

[0035] q sc ≥q p

[0036] q sc ≥q el

[0037] q sc <Q c

[0038] q sc <q e

[0039] q sc <q AOF

[0040] In equation (3), X i Calculate the gas injection volume (in ten thousand cubic meters) for the i-th year under the constraints: k is the adiabatic index (dimensionless); n is the number of production years; P is the gas price (yuan / cubic meter); r w Working gas ratio, dimensionless; C c The total cost is expressed in ten thousand yuan; r0 is the bank interest rate, dimensionless; I(type) represents the investment in different well types, in ten thousand yuan; p i p represents the original formation pressure and the formation pressure at a certain development time, in MPa; p R and p a For formation pressure and abandoned formation pressure, MPa; Cumulative effective compressibility, MPa 1 ;zi And z represents the original formation pressure p i Gas deviation coefficient under a certain development time and gas deviation coefficient under a formation pressure p; p wf These are the average formation pressure and the bottom hole flowing pressure, respectively; p1, p2, p3, p4, and p5 are the tubing bottom pressure, downhole choke valve inlet pressure, downhole choke valve outlet pressure, downhole safety valve inlet pressure, and downhole safety valve outlet pressure, respectively; q p The production plan specifies the gas production per well in ten thousand cubic meters; q el For the economically limited output, 10,000 cubic meters; Q c q represents the rock velocity sensitivity, in m / s; e q represents the pipe wall erosion velocity, in m / s. AOF For unobstructed flow, 10,000 cubic meters / day; a1,…,a 10 The fitting parameters are d1…d5, which are the wellbore diameter, tubing diameter, downhole choke valve diameter, downhole safety valve diameter, and wellhead nozzle diameter, respectively, in meters.

[0041] In step S4, the method for solving the injection-production optimization model of horizontal wells in gas storage based on the differential evolution algorithm includes the following steps:

[0042] Step 1. Given the population size NP and the number of iterations t = 100-1000, generate the initial population GEN and NPV based on the gas storage horizontal well injection-production optimization model. best =inf;

[0043] Step 2. Select any individual X from the initial population GEN. r1,t An improved mutation operation is performed to obtain the mutated individual V. i,t for:

[0044] V i,t =W i,t X r1,t +K i,t (X best -X i,t )+F i,t (X pbest -X i,t (4)

[0045] In equation (4), X i,t X is the i-th individual (i = 1, 2, ..., NP) in the t-th generation initialization population GEN. best X is the current optimal individual. pbest This represents the historically best individual; the mutation probability F adopts an improved adaptive form:

[0046]

[0047] Step 3. For each individual X i,t and its corresponding variant individual V i,t Generate the corresponding test individual U i,t , Its j-th component is uniformly crossed according to the following rules:

[0048]

[0049] Among them, rand i,j [0,1) are random numbers that follow a uniform distribution on [0,1). CR uses an improved crossover factor:

[0050]

[0051]

[0052]

[0053] In equations (5)-(7), w0 = 0.2, w min =0.15, w max =0.95, F min =0.02, F max =0.95, CR min =0.3, CR max =0.95, t is the number of iterations, f best,t f is the objective function value corresponding to the best individual in the present era. worst,t This represents the objective function value corresponding to the worst individual in the current generation.

[0054] Step 4. Compare the newly generated individual objective function value f(U) i,t ) and the current individual objective function value f(X) i,t If the objective function value of the newly generated individual is less than that of the current individual, then the newly generated individual is assigned to the current individual, and the update is performed according to the following rules:

[0055]

[0056] Then proceed to Step 5; otherwise, proceed directly to Step 5.

[0057] Step 5. If the maximum number of iterations is reached or the required accuracy is achieved, exit the loop and output the optimal solution X. best Otherwise, the current best individual X will be... best Assign the value to X, t = t + 1, and go to Step 2;

[0058] Step 6. Based on the optimal solution X best Calculate each key indicator.

[0059] By adopting the above technical solution, the beneficial technical effects of the present invention are:

[0060] 1. Based on specific steps, this invention can accurately predict and analyze the factors affecting the injection and production capacity of horizontal wells under different geological conditions. At the same time, it establishes a quantifiable mathematical optimization model to scientifically guide the design scheme of gas reservoir-type gas storage facilities. The improved differential evolution algorithm can ensure the model solution speed while taking into account the global optimality of the optimization results.

[0061] 2. The gas storage horizontal well injection-production optimization model established in this invention takes achieving the optimal net present value (NPV) as the objective function. An improved differential evolution algorithm is used to solve the model, yielding the optimal decision variables and their corresponding NPVs. Based on these optimal decision variables, key indicators can then be further calculated. The key indicators obtained using this method have advantages such as fast calculation speed and high accuracy, which helps improve the accuracy of the optimization.

[0062] 3. The horizontal well injection-production capacity optimization model established in this invention, considering the safety factor of wellbore erosion by injection and production gas flow, limits the maximum reasonable production allocation by calculating the erosion velocity of the pipe wall in the wellbore flow to prevent wellbore integrity and safety failure. The optimal injection-production allocation derived from this model can guide safe production in the field. Through practical application at the Xiangguosi gas storage facility, the accuracy of this invention can reach over 93%. Attached Figure Description

[0063] Figure 1 This is a flowchart of the present invention;

[0064] Figure 2 This is a schematic diagram of step S4. Detailed Implementation

[0065] Example 1

[0066] This embodiment provides a method for optimizing the injection and production capacity of horizontal wells in gas-bearing reservoirs. This method establishes a quantifiable optimization model based on an improved differential evolutionary algorithm, which can scientifically guide the optimization scheme for the actual injection and production capacity of gas-bearing reservoirs. The improved differential evolutionary algorithm can ensure the model's solution speed while also considering the global optimality of the optimization results. Figure 1 As shown, it includes the following steps:

[0067] Step S1: Based on the basic requirements for the construction of gas reservoir-type gas storage facilities and the sensitivity analysis of factors affecting production capacity, key indicators are determined.

[0068] It should be noted that the aforementioned impact on production capacity includes the influence of formation coefficient, formation water, formation pressure, stress sensitivity, temperature, and other factors, as detailed below:

[0069] ① Influence of stratigraphic coefficient

[0070] For a homogeneous infinitely large stratum, solve the seepage equation. The relationship between output and pressure, expressed in terms of the square of pressure, can be obtained under unstable conditions:

[0071]

[0072] In equation (1-1): p e Original static pressure of formation, MPa; p wf Bottom hole flowing pressure, MPa; q g Gas wellhead flow rate, 10 4 m 3 / d; K formation effective permeability, 10 -3 μm 2 h is the effective thickness of the strata, in meters. The average gas deviation coefficient and average temperature under formation conditions are dimensionless. Reference viscosity of the gas layer under average conditions, mPa·s; φ: gas layer porosity, dimensionless; t: time, h; S a Depending on the epidermal coefficient, S a =S+Dq g , dimensionless; S is the true epidermal coefficient, dimensionless; p sc ,T sc Pressure and temperature of a gas under standard conditions, p sc =0.1013 MPa, T sc =293.16K; C t Formation compressibility coefficient, MPa 1 ;r w The equivalent radius of the well, in meters; D is the non-Darcy coefficient.

[0073] Put S a =S+Dq g Substituting into equation (1-1) and rearranging according to the binomial production capacity equation, equation (1-1) can be simplified.

[0074]

[0075] The expressions for A and B are obtained as follows:

[0076]

[0077]

[0078] ② The influence of formation water

[0079] During actual gas reservoir production, the presence of formation water reduces gas phase permeability, leading to decreased well productivity. Furthermore, the flow of water during well production consumes more formation energy, further reducing productivity. Reservoir properties are significantly affected by rising water saturation. Therefore, in actual production, to prevent excessively rapid decline in well productivity due to water ingress, it is crucial to rationally control the production pressure differential.

[0080] ③ The influence of formation pressure

[0081] A key concern in gas well production is the impact of formation pressure changes on well productivity. Rock properties such as permeability, porosity, and compressibility decrease with decreasing reservoir pressure and significant elastoplastic deformation of the rock. Therefore, for actual gas well production, it is crucial to utilize formation energy effectively and control the production pressure differential to prevent a rapid decline in formation pressure that could lead to a sharp drop in well productivity.

[0082] ④ Effect of stress sensitivity

[0083] During gas field development, reservoir stress sensitivity damage induced by the decrease in reservoir pressure is unavoidable. The permeability, porosity, and pore compressibility of reservoir rocks decrease with increasing effective pressure, with a sharp decrease in the early stages, a slower decrease in the middle stages, and a slower decrease in the later stages. This indicates that during depletion-type gas reservoir development, as gas is continuously extracted, formation pressure decreases, effective pressure increases, and reservoir permeability, porosity, and pore compressibility decrease, leading to reduced production capacity, changes in gas-water distribution, and a decrease in elastic energy. Furthermore, artificially fractured rock samples are much more sensitive to net confining pressure than unfractured samples, especially in the initial stages of an increase in net confining pressure.

[0084] ⑤ Temperature effect

[0085] Temperature is an important external factor affecting rock deformation. Experimental results show that as temperature increases, the elastic limit and tensile limit of both carbonate and silicate rocks decrease, the elastic modulus decreases, the yield point becomes more pronounced, and plasticity increases, that is, the rock transforms from brittle to plastic.

[0086] ⑥ The influence of other factors

[0087] Factors such as sulfur deposition, fracture development, boundary and formation heterogeneity can all affect gas well productivity; changes in any of these factors will cause variations in gas well productivity. Human factors, however, are much broader, ranging from extraction methods, well network, well spacing, drilling of adjustment (infill) wells, and shutdown / abandonment of old wells, to various human interventions (including fracturing, acidizing, perforation repair, and drainage). Changes in any of these human factors will affect gas field production. Furthermore, the factors influencing production differ at different stages of gas field development.

[0088] Based on the basic requirements for the construction of gas reservoirs and the sensitivity analysis of the factors affecting production capacity mentioned above, the key indicators that can be determined include single-well gas injection capacity, single-well gas production capacity, formation pressure at the end of the gas injection period, formation pressure at the end of the gas production period, construction period, cumulative gas production at the end of the construction period, and the degree of improved production at the end of the construction period.

[0089] Step S2: Based on relevant theories of gas reservoir engineering and fluid mechanics, establish gas-phase and water-phase mathematical models for fractured gas reservoir-type underground gas storage facilities. The established gas-phase and water-phase mathematical models are equations of state used to express the flow of gas and water underground.

[0090] Step S3: Construct an injection-production optimization model for horizontal wells in the gas storage facility based on the gas phase mathematical model and the aqueous phase mathematical model. This optimization model is established with the objective function of maximizing the net present value (NPV) within n years after the gas storage facility is put into operation.

[0091] Step S4: Solve the injection-production optimization model of the horizontal well in the gas storage facility using the differential evolution algorithm, and calculate the key indicators based on the solution results. The solution to the horizontal well injection-production optimization model yields intermediate decision variables, which are then used to calculate the key indicators.

[0092] Step S5: Based on the obtained key indicators, and in combination with the specific gas storage and horizontal well injection and production parameters, provide an optimization plan.

[0093] It should be noted that this embodiment does not impose specific limitations on the gas phase mathematical model, the water phase mathematical model, the gas storage horizontal well injection and production optimization model, or the solution process. As long as the above conditions are met and the intermediate decision variables can be calculated, and the key indicators can be calculated based on the intermediate decision variables, it is acceptable.

[0094] Example 2

[0095] This embodiment provides a method for optimizing the injection and production capacity of horizontal wells in gas reservoirs, such as... Figure 1 As shown, it includes the following steps:

[0096] Step S1: Based on the basic requirements for the construction of gas reservoir-type gas storage facilities and the sensitivity analysis of factors affecting production capacity, key indicators are determined. Specifically, the key indicators include single-well gas injection capacity, single-well gas production capacity, formation pressure at the end of the gas injection period, formation pressure at the end of the gas production period, construction period, cumulative gas production at the end of the construction period, and improved production rate at the end of the construction period.

[0097] Step S2: Based on relevant theories of gas reservoir engineering and fluid mechanics, establish gas-phase and water-phase mathematical models for fractured gas reservoir-type underground gas storage facilities. The established gas-phase and water-phase mathematical models are as follows:

[0098] Gas phase:

[0099]

[0100] Aqueous phase:

[0101]

[0102] In equations (1) and (2), Absolute permeability vector of the fracture, μm 2 ;K rgf K rwf Φ represents the relative permeability of the gas phase and water phase in the fracture, respectively; gf Φ wf S represents the potential of the gas and water phase fluids in the fracture, respectively; gf S wf Gas and water saturation in the crack; φ f Rock porosity; B g B w Volume index of gas and water; μ g μ w Viscosity of gas and water; q g q w The flow rate of air and water; Γ gmf ,Γ wmf These represent the flow rates of gas and water caused by the pressure difference between the crack and the rock block, respectively; Γ gdmf This represents the amount of gas diffusion caused by the concentration difference between the crack and the rock block.

[0103] Step S3: Construct an injection-production optimization model for horizontal wells in a gas storage facility based on the gas phase mathematical model and the aqueous phase mathematical model. The constructed injection-production optimization model for horizontal wells in a gas storage facility is as follows:

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113]

[0114]

[0115]

[0116] q sc ≥q p

[0117] q sc ≥q el

[0118] q sc <Q c

[0119] q sc <q e

[0120] q sc <q AOF

[0121] In equation (3), X i Calculate the gas injection volume (in ten thousand cubic meters) for the i-th year under the constraints: k is the adiabatic index (dimensionless); n is the number of production years; P is the gas price (yuan / cubic meter); r w Working gas ratio, dimensionless; C c The total cost is expressed in ten thousand yuan; r0 is the bank interest rate, dimensionless; I(type) represents the investment in different well types, in ten thousand yuan; p i p represents the original formation pressure and the formation pressure at a certain development time, in MPa; p R and p a For formation pressure and abandoned formation pressure, MPa; Cumulative effective compressibility, MPa -1 ;z i And z represents the original formation pressure p i Gas deviation coefficient under a certain development time and gas deviation coefficient under a formation pressure p; p wfThese are the average formation pressure and the bottom hole flowing pressure, respectively; p1, p2, p3, p4, and p5 are the tubing bottom pressure, downhole choke valve inlet pressure, downhole choke valve outlet pressure, downhole safety valve inlet pressure, and downhole safety valve outlet pressure, respectively; q p The production plan specifies the gas production per well in ten thousand cubic meters; q el For the economically limited output, 10,000 cubic meters; Q c q represents the rock velocity sensitivity, in m / s; e q represents the pipe wall erosion velocity, in m / s. AOF For unobstructed flow, 10,000 cubic meters / day; a1,…,a 10 The fitting parameters are d1…d5, which are the wellbore diameter, tubing diameter, downhole choke valve diameter, downhole safety valve diameter, and wellhead nozzle diameter, respectively, in meters.

[0122] Step S4: Solve the injection and production optimization model of the horizontal well in the gas storage tank based on the differential evolution algorithm, and calculate the key indicators based on the solution results.

[0123] Step S5: Based on the obtained key indicators, and in combination with the specific gas storage and horizontal well injection and production parameters, provide an optimization plan.

[0124] Example 3

[0125] Based on Example 2, this example further specifies the solution process for the horizontal well injection-production optimization model of the gas storage facility, such as... Figure 2 As shown, the specific solution process is as follows:

[0126] The method for solving the injection-production optimization model of horizontal wells in gas storage based on differential evolution algorithm includes the following steps:

[0127] Step 1. Given the population size NP and the number of iterations t = 100-1000, generate the initial population GEN and NPV based on the gas storage horizontal well injection-production optimization model. best =inf;

[0128] Step 2. Select any individual X from the initial population GEN. r1,t An improved mutation operation is performed to obtain the mutated individual V. i,t for:

[0129] V i,t =W i,t X r1,t +K i,t (X best -X i,t )+F i,t (X pbest -X i,t (4)

[0130] In equation (4), X i,tX is the i-th individual (i = 1, 2, ..., NP) in the t-th generation initialization population GEN. best X is the current optimal individual. pbest This represents the historically best individual; the mutation probability F adopts an improved adaptive form:

[0131]

[0132] Step 3. For each individual X i,t and its corresponding variant individual V i,t Generate the corresponding test individual U i,t , Its j-th component is uniformly crossed according to the following rules:

[0133]

[0134] Among them, rand i,j [0,1) are random numbers that follow a uniform distribution on [0,1). CR uses an improved crossover factor:

[0135]

[0136]

[0137]

[0138] In equations (5)-(7), w0 = 0.2, w min =0.15, w max =0.95, F min =0.02, F max =0.95, CR min =0.3, CR max =0.95, t is the number of iterations, f best,t f is the objective function value corresponding to the best individual in the present era. worst,t This represents the objective function value corresponding to the worst individual in the current generation.

[0139] Step 4. Compare the newly generated individual objective function value f(U) i,t ) and the current individual objective function value f(X) i,t If the objective function value of the newly generated individual is less than that of the current individual, then the newly generated individual is assigned to the current individual, and the update is performed according to the following rules:

[0140]

[0141] Then proceed to Step 5; otherwise, proceed directly to Step 5.

[0142] Step 5. If the maximum number of iterations is reached or the required accuracy is achieved, exit the loop and output the optimal solution X. best Otherwise, the current best individual X will be... best Assign the value to X, t = t + 1, and go to Step 2;

[0143] Step 6. Based on the optimal solution X best Calculate each key indicator.

[0144] It should be further explained in this embodiment that the method for calculating each key indicator is a conventional existing technique, mainly involving: obtaining the optimal solution X. best The decision variables are the corresponding injection pressure and flow rate, production pressure and flow rate, and the time when pressure equilibrium is reached and gas production is no longer possible (this is the reservoir construction period). These are converted to single-well injection capacity, single-well production capacity, formation pressure at the end of the injection period, and formation pressure at the end of the production period. Further, based on the maximum injection volume (injection flow rate * injection time) and maximum production volume (production flow rate * production time) during the reservoir construction period, the maximum and effective storage capacity of the gas storage facility can be obtained. Adding the injection and production volumes during the period yields the cumulative injection volume and cumulative production volume at the end of the reservoir construction period. The ratio of the cumulative production volume at the end of the reservoir construction period to the cumulative injection volume represents the improved production level at the end of the construction period. This improved production level, compared to the unoptimized result, represents the final improved production level.

[0145] Example 4

[0146] This embodiment verifies the method described in this invention, as follows:

[0147] The original data for a certain gas reservoir type gas storage facility is as follows: the gas reservoir burial depth is H. f =2575m, original formation pressure p i =45.11MPa, pressure coefficient is 1.08, reservoir temperature is T=131℃, and natural gas relative density is r g =0.6914, original gas deviation coefficient Z i =1.825, gas volume coefficient B under original conditions gi =0.002413, initial water saturation of the formation S wi =0.107.

[0148] With fixed working gas ratios of 40% and 60% and reservoir construction periods of 4 and 7 years, and using injection pressure, flow rate, and extraction pressure and flow rate as decision variables, the optimization models in steps S2 and S3 are substituted into the optimization algorithm in step S4 to obtain four optimal solutions X. best The key indicators are obtained by referring to step S5, and the final solution is shown in the table below.

[0149]

[0150] In the above examples, the single-well gas production capacity of the four schemes before optimization was 873,000 cubic meters / day, 1.49 million cubic meters / day, 1.954 million cubic meters / day, and 2.23 million cubic meters / day, respectively. After optimization, the gas production capacity increased to 2.356 million cubic meters / day, 3.537 million cubic meters / day, 2.356 million cubic meters / day, and 3.537 million cubic meters / day, respectively, showing a significant optimization effect.

[0151] The above description is merely a specific embodiment of the present invention. Any feature disclosed in this specification may be replaced by other equivalent or similar features unless otherwise specified. All features or steps in the disclosed methods or processes may be combined in any way, except for mutually exclusive features and / or steps.

Claims

1. A method for optimizing injection-production capacity of a horizontal well of a gas storage of gas reservoir type, characterized in that The method comprises the following steps: Step S1: determining key indexes according to basic requirements for building gas reservoir type gas storage and sensitivity analysis of factors affecting deliverability; Step S2: establishing a gas phase mathematical model and a water phase mathematical model of the fractured gas reservoir type underground gas storage according to relevant theories of gas reservoir engineering and fluid mechanics; Step S3: constructing a gas injection and production optimization model of the gas storage horizontal well according to the gas phase mathematical model and the water phase mathematical model; Step S4: solving the gas injection and production optimization model of the gas storage horizontal well based on a differential evolution algorithm, and calculating the key indexes according to a solution result; Step S5: giving an optimization scheme according to the obtained key indexes and combining specific gas storage and gas injection and production parameters of the horizontal well; In Step S1, the key indexes include single-well gas injection capacity, single-well gas production capacity, formation pressure at the end of the gas injection period, formation pressure at the end of the gas production period, building period, cumulative gas production at the end of the building period, and enhanced recovery degree at the end of the building period. In Step S3, the gas injection and production optimization model of the gas storage horizontal well is constructed with the optimal net present value (NPV) in n years after the gas storage is put into production as an objective function.

2. The method according to claim 1, wherein: In Step S1, the deliverability influence includes influences of formation coefficients, formation water, formation pressure, stress sensitivity, and temperature.

3. The method according to claim 1 or 2, characterized in that: In Step S2, the established gas phase mathematical model and water phase mathematical model are as follows: Gas phase: Water phase: in formulas (1), (2), Absolute permeability vector of the fracture, μm 2 ; , represent the relative permeability of gas phase and water phase in the fracture, respectively; , represent the potential of gas phase and water phase fluid in the fracture, respectively; , Saturation of gas phase and water phase in the fracture; Porosity of the rock; , Volume factor of gas and water; , Viscosity of gas and water; , Flow rate of gas and water; Γ gmf , Γ wmf represent the flow rate of gas and water between the fracture and the rock mass caused by pressure difference; Γ gdmf is the gas diffusion amount between the fracture and the rock mass caused by concentration difference.

4. The method according to claim 3, wherein: In Step S3, the constructed gas injection and production optimization model of the gas storage horizontal well is as follows: （3） In formula (3), is the constraint condition calculation of the i year gas injection, ten thousand square meters; is the adiabatic index, dimensionless; n is the production years; is the gas price, yuan / square meters; is the working gas ratio, dimensionless; is the full cost, unit ten thousand yuan; is the bank interest rate, dimensionless; is the investment of different well types, ten thousand yuan; and are the original formation pressure and the formation pressure at a certain development time, MPa; and are the formation pressure and the abandoned formation pressure, MPa; is the cumulative effective compression coefficient, MPa -1 ; and are the gas deviation factor at the original formation pressure and the gas deviation factor at the formation pressure at a certain development time ; are the average formation pressure, the bottom-hole flowing pressure, are the tubing bottom pressure, the inlet pressure of the downhole choke valve, the outlet pressure of the downhole choke valve, the inlet pressure of the downhole safety valve, and the outlet pressure of the downhole safety valve, respectively; is the open flow capacity, ten thousand square meters / day; is the fitting parameter; are the wellbore diameter, the tubing diameter, the downhole choke valve diameter, the downhole safety valve diameter, and the wellhead gas nozzle diameter, respectively, m.

5. The method according to claim 4, wherein: In Step S4, the method for solving the gas injection and production optimization model of the gas storage horizontal well based on the differential evolution algorithm comprises the following steps: Step 1. Given the population size NP, set the number of iterations t = 100-1000, and generate the initial population GEN according to the gas storage horizontal well injection-production optimization model, ; Step 2. Selecting an individual from the initialized population GEN , performing a modified mutation operation to obtain a mutated individual is: （4） In formula (4), is the ith individual (i = 1, 2,..., NP) in the tth initialization population GEN, is the current optimal individual, is the historical optimal individual; mutation probability F An improved adaptive form is adopted: （5）； Step 3. For each individual and its corresponding mutated individual generate a corresponding trial individual , whose jthcomponent is uniformly crossed over according to the rule: （6） wherein, [0,1) is a random number on [0,1) subject to a uniform distribution, An improved crossover factor is adopted: （7） In formulas (5)-(7) , , , , , , , t is the iteration step number, is the objective function value corresponding to the best individual of the current generation, is the objective function value corresponding to the worst individual of the current generation; Step 4. Compare the newly generated individual objective function value to the size of the current individual objective function value If the newly generated individual objective function value is less than the current individual objective function value, then the newly generated individual is assigned to the current individual, updating is achieved as follows: （8） Then go to Step 5, otherwise go to Step 5 directly; Step 5. If the maximum iteration number is reached or the result accuracy meets the requirement, exit the loop and output the optimal solution ; otherwise, assign the current optimal individual to , , and go to Step 2. Step 6. Based on the optimal solution The key indicators are calculated.

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