Shale condensate gas reservoir circulation gas injection process optimization method and equipment and storage medium

Abstract

The invention relates to a shale condensate gas reservoir circulation gas injection process optimization method and device and a storage medium. The method comprises the steps that geological characteristic parameters and fracturing construction microseismic monitoring data corresponding to a target shale condensate gas reservoir are obtained; establishing a geological numerical model and a crack model based on the data, establishing a component mathematical model, performing state calculation based on a state equation, performing condensation flash calculation, performing full implicit numerical solution, performing relative permeability and capillary pressure model, and optimizing a circular gas injection process. According to the method, microseism inversion, phase equilibrium calculation and numerical simulation are integrated, the minimum total volume of stratum gas condensate and the maximum cumulative gas production rate serve as optimization targets, the circulation gas injection rate is quantitatively determined, and the shale gas condensate reservoir circulation gas injection process is quantitatively optimized; the problems that in the prior art, due to the fact that optimal gas injection technological parameters need to be found through a large number of field tests, mining time is long, efficiency is low, and cost is high are solved.

Description

Technical Field

[0001] This disclosure relates to the field of oil and gas exploration and development technology, and in particular to an optimized method, equipment and storage medium for circulating gas injection process in shale condensate gas reservoirs. Background Technology

[0002] Low gas thermal evolution and interconnected artificial and natural fracture networks are key characteristics of shale condensate reservoirs. When formation pressure is below dew point pressure, condensate precipitation reduces fluid flow in the near-wellbore area and impairs fracture network connectivity, thereby reducing production and ultimate recovery.

[0003] The circulating gas injection strategy can maintain formation pressure and mitigate the damage caused by condensation, making it applicable to the development and exploitation of shale condensate gas reservoirs. Traditional development methods often require extensive field trials to find optimal gas injection parameters, which is not only time-consuming and costly but also detrimental to improving oil and gas extraction efficiency. Summary of the Invention

[0004] This disclosure provides a method, equipment, and storage medium for optimizing the circulating gas injection process in shale condensate gas reservoirs, in order to solve the problems of long extraction time, high cost, and low efficiency caused by having to conduct numerous field tests to find the optimal gas injection process parameters.

[0005] Firstly, this disclosure provides an optimization method for the circulating gas injection process in shale condensate gas reservoirs, including:

[0006] Obtain geological characteristic parameters and microseismic monitoring data of the target shale condensate gas reservoir during fracturing operations;

[0007] Based on the geological characteristic parameters and microseismic monitoring data during fracturing operations, a stratigraphic model reflecting the fracture network during the fracturing process of the shale condensate gas reservoir was established.

[0008] Based on the principles of mass conservation and momentum conservation, a component mathematical model is established according to the fluid composition and mole fraction in the target shale condensate gas reservoir. A thermodynamic equilibrium model is established for the dynamic changes of the fluid composition under different pressures and temperatures. The thermodynamic equilibrium model is then coupled with the component mathematical model.

[0009] Establish a relative permeability model and a capillary pressure model for the fluid components;

[0010] The relative permeability model, the capillary pressure model, and the coupled thermodynamic equilibrium model and component mathematical model are integrated into the formation model to obtain a production model that simulates the oil and gas production of the target shale condensate gas reservoir under different gas injection processes.

[0011] Input the parameters of different cyclic gas injection processes into the production model, and solve the production model under each cyclic gas injection process to obtain the simulated oil production volume and simulated gas production under that cyclic gas injection process.

[0012] By comparing the simulated oil production volume and simulated gas production rate under each cycle of gas injection process, and taking the minimization of the simulated oil production volume and the maximization of the simulated gas production rate as optimization objectives, the parameters of the preferred cycle of gas injection process are determined.

[0013] In one embodiment, the step of establishing a formation model reflecting the fracture network during the fracturing process of the shale condensate gas reservoir includes:

[0014] Based on the geological feature parameters, the model grid size of the stratigraphic model is determined, and a geological numerical model is established based on the grid size;

[0015] Based on the geological numerical model, the fracture network is characterized using the microseismic monitoring data from the fracturing operation, and a microseismic inversion network model of the main fracture-secondary fracture is established based on the fracture network.

[0016] Data on the distribution of bedding fractures were obtained using shale core samples from the shale condensate gas reservoir.

[0017] The bedding fracture distribution data is introduced into the main fracture-secondary fracture microseismic inversion network model to obtain the stratigraphic model reflecting the three-level fracture network of the main fracture-secondary fracture-bedding fracture.

[0018] In one embodiment, the step of establishing a component mathematical model based on the fluid composition and mole fraction in the target shale condensate gas reservoir includes:

[0019] Based on the principle of mass conservation, the following mass conservation equation is established for the fluid components:

[0020]

[0021] Where t is time; φ is porosity; n p n is the number of phases; c For group numbers; x cj ρ is the mole fraction of fluid component c in phase j; ρ is the phase density; S is the degree of saturation. For the source and sink terms of the j-th phase;

[0022] Based on the principle of conservation of momentum, the momentum equation for phase j is obtained as follows:

[0023]

[0024] Where v is velocity; k is permeability; μ is viscosity; and P is pressure.

[0025] Substituting the momentum equation of phase j into the mass conservation equation, and discretizing the mass conservation equation, and expressing it in residual form, we obtain the following calculation formula:

[0026]

[0027] Wherein, the superscripts n+1 and n represent the current and previous time steps, respectively; V′ represents the block volume; and the superscript l represents the interface of the grid block. Let be the flow rate of the j-th phase.

[0028] In one embodiment, the thermodynamic equilibrium model is calculated as follows:

[0029] z c =Vx cg +Lx cl c = 1, 2, ..., n c ;

[0030]

[0031] V+L=1;

[0032]

[0033] Among them, Z c V is the mole fraction of component c in the total mixture; V is the mole fraction of the gas phase; L is the mole fraction of the liquid phase; x c This represents the mole fraction of fluid component c in the gas (liquid) phase; the subscripts l and g represent the liquid phase and the gas phase, respectively. Let f be the fugacity of component c in the liquid phase. Let f be the fugacity of component c in the gas phase.

[0034] In one embodiment, the step of coupling the thermodynamic equilibrium model with the component mathematical model includes:

[0035] An initial value for the equilibrium constant is obtained. Based on this initial value, the mole fraction V of the gas phase, the mole fraction L of the liquid phase, and the mole fraction x of fluid component c in liquid phase l are calculated using the condensation flash evaporation calculation method in the thermodynamic equilibrium model. cl The mole fraction x of fluid component c in gas phase g cg ;

[0036] x cl and x cg Substitute the preset compressibility factor Z of the mixture m In the cubic equation of state, the compressibility factor Z is calculated. m The value of , where the compression factor Z mAmong the values, the largest positive root is the gas phase compressibility factor Z of the gas phase mixture. g The smallest positive root is the liquid compressibility factor Z of the liquid mixture. l ;

[0037] x cl x cg Gas phase compressibility factor Z g and liquid phase compressibility factor Z l Substitute these values ​​into the preset gas phase density calculation formula and the preset liquid phase density calculation formula to calculate the corresponding gas phase density and liquid phase density.

[0038] The mole fraction V of the gas phase, the mole fraction L of the liquid phase, and the mole fraction x of fluid component c in the liquid phase l are given. cl The mole fraction x of fluid component c in gas phase g cg The gas phase density and liquid phase density are substituted into the mass conservation equation expressed in the residual scheme to couple the thermodynamic equilibrium model with the component mathematical model.

[0039] In one embodiment, the calculation yields the compression factor Z. m After the step of determining the value, it also includes:

[0040] According to the compressibility factor Z of the mixture m From the cubic equation of state, we obtain expressions for the fugacity of component c in the liquid phase and in the gas phase.

[0041] x cl x cg Gas phase compressibility factor Z g and liquid phase compressibility factor Z l Substituting these values ​​into the expressions for the fugacity of component c in the liquid phase and the fugacity of component c in the gas phase, the fugacity of component c in the liquid phase is calculated. and the fugacity of component c in the gas phase

[0042] When the difference between the fugacity of component c in the liquid phase and the fugacity of component c in the gas phase does not meet the preset error condition, the gas-liquid equilibrium constant is updated, and x is recalculated using the condensation flash evaporation calculation method under the updated gas-liquid equilibrium constant. cl x cg and using this x cl x cg The new gas-phase compressibility factor Z was calculated. g and liquid phase compressibility factor Z l According to the new x cl x cg Gas phase compressibility factor Z gand liquid phase compressibility factor Z l Calculation yields new And new Verify the new one again and new The difference, until the new and new If the preset error conditions are met, the calculation will stop.

[0043] In one embodiment, the step of solving the production model to obtain the simulated oil production volume and simulated gas production under the cyclic gas injection process includes:

[0044] The mass conservation equation represented by the residual scheme in the production model is solved using a fully implicit scheme based on the Newton-Raphson method, and the simulated oil production volume and simulated gas production under the cyclic gas injection process are obtained.

[0045] In a second aspect, this disclosure provides a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described above.

[0046] Thirdly, this disclosure provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method described above.

[0047] Fourthly, this disclosure provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the methods described in the foregoing aspects.

[0048] This disclosure provides an optimized method, equipment, and storage medium for circulating gas injection technology in shale condensate gas reservoirs. By integrating geological characteristic parameters, microseismic monitoring data from fracturing operations, and various mathematical models such as component mathematical models, thermodynamic balance models, relative permeability models, and capillary pressure models, a production model is constructed that can accurately simulate the dynamic changes in shale condensate gas reservoirs under different gas injection processes. Simulation and prediction based on this production model can significantly reduce the number and scale of field tests, improving the verification efficiency of the entire gas injection process. Simultaneously, it can reduce unnecessary energy consumption, equipment wear and maintenance costs, and significantly lower overall production costs, thereby improving economic efficiency. Attached Figure Description

[0049] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings:

[0050] Figure 1 A schematic flowchart illustrating an optimized method for circulating gas injection in a shale condensate gas reservoir, provided in an embodiment of this disclosure;

[0051] Figure 2 A schematic diagram of a microseismic inversion fracture network model provided in an embodiment of this disclosure;

[0052] Figure 3 A schematic diagram of a reservoir fluid PT phase diagram provided for an embodiment of this disclosure;

[0053] Figure 4 A schematic diagram of a matrix gas-water relative permeability curve provided in an embodiment of this disclosure;

[0054] Figure 5 A schematic diagram of a capillary pressure curve in a matrix provided in an embodiment of this disclosure;

[0055] Figure 6 A schematic diagram illustrating the change in the total volume of formation condensate oil in a circulating gas injection process provided in an embodiment of this disclosure;

[0056] Figure 7 A schematic diagram illustrating the cumulative oil production variation of a circulating gas injection process provided in this embodiment of the present disclosure;

[0057] Figure 8 A schematic diagram illustrating the change in cumulative gas production in a circulating gas injection process provided in this embodiment of the present disclosure;

[0058] Figure 9 This is an internal structural block diagram of a shale condensate gas reservoir circulating gas injection process optimization device provided in an embodiment of this disclosure. In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0059] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort should fall within the protection scope of this disclosure.

[0060] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0061] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0062] Example 1

[0063] Figure 1 This is a schematic flowchart illustrating an optimized circulating gas injection process for shale condensate gas reservoirs, provided as an embodiment of this disclosure. Figure 1 As shown, an optimization method for circulating gas injection technology in shale condensate gas reservoirs includes:

[0064] Step 110: Obtain the geological characteristic parameters and microseismic monitoring data of the target shale condensate gas reservoir.

[0065] Step 120: Based on the geological characteristic parameters and microseismic monitoring data during the fracturing operation, establish a stratigraphic model that reflects the fracture network during the fracturing process of the shale condensate gas reservoir.

[0066] Step 130: Under the principles of mass conservation and momentum conservation, based on the fluid composition and mole fraction in the target shale condensate gas reservoir, establish a component mathematical model, establish a thermodynamic equilibrium model for the dynamic changes of the fluid composition under different pressures and temperatures, and couple the thermodynamic equilibrium model with the component mathematical model.

[0067] Step 140: Establish the relative permeability model and capillary pressure model of the fluid components;

[0068] Step 150: Integrate the relative permeability model, the capillary pressure model, and the coupled thermodynamic equilibrium model and component mathematical model into the formation model to obtain a production model that simulates the oil and gas production of the target shale condensate gas reservoir under different gas injection processes.

[0069] Step 160: Input the parameters of different circulating gas injection processes into the production model. Under each circulating gas injection process, solve the production model to obtain the simulated oil production volume and simulated gas production under that circulating gas injection process.

[0070] Step 170: Compare the simulated oil production volume and simulated gas production under each cycle gas injection process, and determine the parameters of the preferred cycle gas injection process with the optimization objectives of minimizing the simulated oil production volume and maximizing the simulated gas production.

[0071] In this embodiment, geological characteristic parameters include, but are not limited to, reservoir size, reservoir pressure, reservoir temperature, matrix permeability, matrix porosity, rock compressibility, fracture conductivity, and reservoir fluid composition and mole fraction data. These data are obtained through well logging, core analysis, seismic exploration, and other methods.

[0072] Microseismic monitoring data from fracturing operations provides crucial information such as the spatial distribution of the fracture network, fracture size, and fracture conductivity. Utilizing geological characteristic parameters and microseismic monitoring data, combined with geological modeling techniques, a three-dimensional formation model is constructed. This model should be able to reflect the complex fracture network formed during fracturing. The spatial distribution of the fracture network determines the flow path and diffusion range of gas in the reservoir, thus more accurately reflecting gas flow in the simulation. The fracture size directly affects the gas storage and flow capacity. Different fracture sizes have different effects on parameters such as gas permeability and diffusion rate; therefore, fracture size needs to be considered in the simulation. Fracture conductivity is a key parameter for measuring the ability of fractures to conduct fluids. Understanding the conductivity of fractures allows for more accurate prediction of gas migration patterns within the fracture network, thereby improving the accuracy of the simulation.

[0073] In this embodiment, detailed information on the fluid components in the target shale condensate gas reservoir is obtained, including the type, initial mole fraction, density, viscosity, and other physical properties of each component. In a shale condensate gas reservoir, the reservoir can be considered a relatively closed system. Therefore, when establishing the mathematical model of the components, it is ensured that the total mass of each component remains constant during the simulation. Based on the principle of mass conservation, mathematical expressions for the changes of fluid components in the reservoir over time and space can be established. Based on the principle of mass conservation, combined with the physical properties of the fluid components and reservoir environmental parameters, a mathematical model describing the dynamic changes of fluid components in the reservoir can be constructed to describe processes such as diffusion, convection, and phase transition of the fluid components in the reservoir.

[0074] In shale condensate gas reservoirs, the phase state and properties of fluid components change with temperature and pressure. Therefore, a thermodynamic equilibrium model is needed to describe the phase equilibrium and property changes of fluid components under different temperatures and pressures. The dynamic changes of fluid components and the thermodynamic equilibrium state are interrelated. On the one hand, the dynamic changes of fluid components affect their thermodynamic equilibrium state; on the other hand, changes in the thermodynamic equilibrium state also affect the dynamic changes of fluid components. Therefore, to more accurately simulate the behavior of fluid components in the reservoir, it is necessary to couple the component mathematical model and the thermodynamic equilibrium model.

[0075] By coupling the component mathematical model and the thermodynamic equilibrium model, detailed information on the temporal and spatial variations of fluid components in the reservoir can be obtained, including changes in parameters such as the mole fraction, phase distribution, and density of each component. This helps to more accurately simulate reservoir productivity and optimize the parameters of the circulating gas injection process.

[0076] Relative permeability models can describe the relative flow capacity of different fluid components (such as condensate oil, gas, and water) in a reservoir under the same pressure gradient. Capillary pressure models can predict the saturation distribution of fluids in the reservoir under different pressure conditions. Establishing relative permeability and capillary pressure models can more accurately simulate the migration patterns of multiphase fluids in complex geological structures, and helps to more precisely predict the distribution and changes of various fluid components in the reservoir during circulating gas injection.

[0077] By combining relative permeability models, capillary pressure models, component mathematical models, and thermodynamic equilibrium models, a more complete reservoir flow simulation system (or production model) can be constructed, greatly improving the accuracy and reliability of the simulation and providing a more accurate scientific basis for production decisions. Using the constructed production model, different circulating gas injection process schemes are simulated and optimized. By comparing the oil and gas production and economic benefits under different circulating gas injection process schemes, with the optimization objectives of minimizing simulated oil production volume and maximizing simulated gas production, the optimal circulating gas injection process scheme is selected to improve recovery rate and economic benefits.

[0078] In one embodiment, the step of establishing a stratigraphic model reflecting the fracture network during the fracturing process of the shale condensate gas reservoir includes: determining the model grid size of the stratigraphic model based on the geological characteristic parameters, and establishing a geological numerical model based on the grid size; characterizing the fracturing fracture network based on the microseismic monitoring data of the fracturing operation based on the geological numerical model, and establishing a primary fracture-secondary fracture microseismic inversion network model based on the fracturing fracture network; obtaining bedding fracture distribution data using shale core zone pressure permeation experiments of the shale condensate gas reservoir; and introducing the bedding fracture distribution data into the primary fracture-secondary fracture microseismic inversion network model to obtain the stratigraphic model reflecting the three-order fracture network of primary fracture-secondary fracture-bedding fracture.

[0079] In this embodiment, the geological characteristic parameters of shale condensate gas reservoirs are first analyzed, including but not limited to reservoir size, lithological distribution, fault strike, and tectonic stress field. By analyzing these parameters, the mesh size of the bottom-level model is determined, laying the foundation for the level of detail in the geological structure description. When determining the mesh size, methods such as equal-spacing or variable-spacing meshing can be used. The choice of mesh size should balance computational accuracy and computational cost, capturing the details of the fracture network while avoiding excessive computation. The specific meshing method depends on the actual computational needs and is not limited here.

[0080] Using geological modeling software, a preliminary geological numerical model was established based on the determined grid size and geological characteristic parameters. This model should reflect the geometric morphology, lithological distribution, and basic geological structural features of the strata. Based on microseismic monitoring data, signal processing, waveform matching, and source location techniques were employed to identify parameters such as the location, orientation, and length of fractures formed during the fracturing process. Through statistical analysis, a preliminary morphology of the fracturing fracture network was constructed. Building upon the geological numerical model, the characterization results of the fracturing fracture network were incorporated into the model to establish a primary fracture-secondary fracture microseismic inversion network model. This model not only includes geological structural information but also accurately reflects the fracture network formed during the fracturing process, including the orientation and length of the primary fractures and the distribution of secondary fractures.

[0081] In one embodiment, a pressure-driven percolation test of shale cores is used to simulate the fluid flow process in shale under reservoir conditions. The opening and expansion of bedding fractures (i.e., tiny cracks between shale bedding planes) are observed and recorded to obtain bedding fracture distribution data. This data includes parameters such as the location, density, and aperture of the bedding fractures, and is a key input for constructing a three-order fracture network model.

[0082] By incorporating bedding fracture distribution data into the primary fracture-secondary fracture microseismic inversion network model, and comprehensively considering primary fractures, secondary fractures, and bedding fractures, a stratigraphic model reflecting a three-tiered fracture network—from primary fractures to secondary fractures to bedding fractures—is constructed through model fusion and parameter adjustment. This model can comprehensively describe the complexity and diversity of the fracture network during fracturing, providing strong support for subsequent oil and gas production prediction and process optimization.

[0083] In one embodiment, the step of establishing a component mathematical model based on the fluid composition and mole fraction in the target shale condensate gas reservoir includes:

[0084] Based on the principle of mass conservation, the following mass conservation equation is established for the fluid components:

[0085]

[0086] Where t is time; φ is porosity; n p n is the number of phases; c For group numbers; x cj ρ is the mole fraction of fluid component c in phase j; ρ is the phase density; S is the degree of saturation. For the source and sink terms of the j-th phase;

[0087] Based on the principle of conservation of momentum, the momentum equation for phase j is obtained as follows:

[0088]

[0089] Where v is velocity; k is permeability; μ is viscosity; and P is pressure.

[0090] Substituting the momentum equation of phase j into the mass conservation equation, and discretizing the mass conservation equation, and expressing it in residual form, we obtain the following calculation formula:

[0091]

[0092] Wherein, the superscripts n+1 and n represent the current and previous time steps, respectively; V′ represents the block volume; and the superscript l represents the interface of the grid block. Let be the flow rate of the j-th phase.

[0093] In one embodiment, Defined as (k rj / μ j ) l The upstream weighting method is used to determine the flow degree λ. l Calculated by the following formula:

[0094]

[0095] In the formula, subscripts a and b represent grid blocks; A l Let l be the region; l is the distance between the center of the block and the centroid of the interface.

[0096] In one embodiment, the thermodynamic equilibrium model is calculated as follows:

[0097] z c =Vx cg +Lx cl c = 1, 2, ..., n c (5)

[0098]

[0099] V+L=1 (7)

[0100]

[0101] Among them, Z c V is the mole fraction of component c in the total mixture; V is the mole fraction of the gas phase; L is the mole fraction of the liquid phase; x c This represents the mole fraction of fluid component c in the gas (liquid) phase; the subscripts l and g represent the liquid phase and the gas phase, respectively. Let f be the fugacity of component c in the liquid phase. Let f be the fugacity of component c in the gas phase.

[0102] In one embodiment, the step of coupling the thermodynamic equilibrium model with the component mathematical model includes:

[0103] An initial value for the equilibrium constant is obtained. Based on this initial value, the mole fraction V of the gas phase, the mole fraction L of the liquid phase, and the mole fraction x of fluid component c in liquid phase l are calculated using the condensation flash evaporation calculation method in the thermodynamic equilibrium model. cl The mole fraction x of fluid component c in gas phase g cg ;

[0104] x cl and x cg Substitute the preset compressibility factor Z of the mixture m In the cubic equation of state, the compressibility factor Z is calculated. m The value of , where the compression factor Z m Among the values, the largest positive root is the gas phase compressibility factor Z of the gas phase mixture. g The smallest positive root is the liquid compressibility factor Z of the liquid mixture. l ;

[0105] x cl x cg Gas phase compressibility factor Z gand liquid phase compressibility factor Z l Substitute these values ​​into the preset gas phase density calculation formula and the preset liquid phase density calculation formula to calculate the corresponding gas phase density and liquid phase density.

[0106] The mole fraction V of the gas phase, the mole fraction L of the liquid phase, and the mole fraction x of fluid component c in the liquid phase l are given. cl The mole fraction x of fluid component c in gas phase g cg The gas phase density and liquid phase density are substituted into the mass conservation equation expressed in the residual scheme to couple the thermodynamic equilibrium model with the component mathematical model.

[0107] In this embodiment, the compressibility factor Z of the mixture is obtained by applying the equation of state of the mixture system. m The formula for calculating the cubic equation is:

[0108]

[0109]

[0110] Among them, a m The gravitational coefficient, R is the molar gas constant; T is the temperature; b m For van der Waals covolute, C c Let x be the mole fraction of component c in the gas phase (or liquid phase), which is x in the gas phase. cg In the liquid phase, this is x. cl ;a c a is the gravitational coefficient of component c; c δ is an adjustable temperature function; cj b is the binary interaction coefficient; c Let be the van der Waals covolute of component c.

[0111] The expression for fugacity is as follows:

[0112]

[0113] In the formula, Let C be the fugacity of component C, which is [value missing] in the liquid phase. In the gas phase

[0114] The stability of a single phase is determined based on the Gibbs energy minimization principle. If unstable, the mole fractions of the gas and liquid phases, as well as the component composition of each phase, are obtained through flash evaporation calculations. The successive substitution method is used for the flash evaporation calculations, and the gas-liquid equilibrium constant K is continuously updated during the solution process. c The process of approximating the exact solution step by step includes:

[0115] a. Obtain the initial value of the equilibrium constant using Wilson's formula:

[0116]

[0117] Among them, P cc ω is the critical pressure of component c; c is the Pitzer eccentricity factor for component c.

[0118] b. Through K c Values ​​of V and L

[0119] The liquid and gas phase material balance equations are shown below:

[0120]

[0121] Substituting equations (16) and (17) into equation (8) yields the Ratchford-Rice equation:

[0122]

[0123] Given K c The value is obtained by solving equation (18) using the bisection method or Newton's iteration method. First, the bisection method is used to ensure a relatively accurate initial value, and then the exact solution is obtained quickly through Newton's iteration to obtain V. Then, L is calculated by equation (7).

[0124] c. Given z c K c The values ​​of V and L are obtained by calculating x using equations (16) and (17). cl and x cg .

[0125] d. Given x cl and x cg The value is calculated using equations (9), (10), and (11) to determine the compression factor Z. m The largest positive root is the gas-phase compressibility factor Z of the gas-phase mixture. g The smallest positive root is the liquid compressibility factor Z of the liquid mixture. l value.

[0126] e. Given x cl x cg Z g Z l The gas phase density ρ is calculated using equations (19) and (20). g and liquid phase density ρ l :

[0127]

[0128] Among them, M c denoted as c, representing the relative molecular mass of component c.

[0129] Finally, the mole fraction V of the gas phase, the mole fraction L of the liquid phase, and the mole fraction x of the fluid component c in the liquid phase l are calculated. cl The mole fraction x of fluid component c in gas phase g cg Gas phase density ρ g and liquid phase density ρ l Substituting into the calculation formula (3) makes the thermodynamic equilibrium model coupled with the component mathematical model.

[0130] In one embodiment, the calculation yields the compression factor Z. m After the step of determining the value, it also includes:

[0131] According to the compressibility factor Z of the mixture m From the cubic equation of state, we obtain expressions for the fugacity of component c in the liquid phase and in the gas phase.

[0132] x cl x cg Gas phase compressibility factor Z g and liquid phase compressibility factor Z l Substituting these values ​​into the expressions for the fugacity of component c in the liquid phase and the fugacity of component c in the gas phase, the fugacity of component c in the liquid phase is calculated. and the fugacity of component c in the gas phase

[0133] When the difference between the fugacity of component c in the liquid phase and the fugacity of component c in the gas phase does not meet the preset error condition, the gas-liquid equilibrium constant is updated, and x is recalculated using the condensation flash evaporation calculation method under the updated gas-liquid equilibrium constant. cl x cg and using this x cl x cg The new gas-phase compressibility factor Z was calculated. g and liquid phase compressibility factor Z l According to the new x cl x cg Gas phase compressibility factor Z g and liquid phase compressibility factor Z l Calculation yields new and new Verify the new one again and new The difference, until the new and new If the preset error conditions are met, the calculation will stop.

[0134] In this embodiment, after step d above, the following steps d1 and d2 are added:

[0135] d1. Given x cl x cg Z g Z l The fugacity of component c in the liquid phase is calculated using equations (12) and (13). And in the gas phase, it is called fugacity.

[0136] d2, Test and If equation (6) is not satisfied, then it is further determined whether the preset error condition (e.g., error < 5%) is met. Then, the new equilibrium constant K is calculated according to equation (14). c Repeat steps b to d2 until the error condition is met, thereby improving the accuracy of the production model simulation.

[0137] In one embodiment, a fully implicit scheme based on the Newton-Raphson method is applied to solve the following nonlinear system consisting of equation (3):

[0138]

[0139] Where x represents a vector of unknowns, and the unknowns for each block include (P, z1, z2, ..., z...). nc ); r represents the residual vector; J represents the Jacobian matrix; subscript n+1 represents the current time step; superscript k+1 and k represent the current and previous nonlinear iterations, respectively.

[0140] Solving equation (21) yields the simulated oil production volume and gas production on each grid. By summing the calculation results from each grid, the simulated oil production volume and simulated gas production of the target shale condensate gas reservoir as a whole can be obtained.

[0141] In one embodiment, the formula for calculating relative permeability versus capillary pressure is as follows:

[0142] The relative permeability of the matrix gas phase is calculated by the following formula:

[0143]

[0144] The relative permeability of the aqueous phase in the matrix is ​​calculated by the following formula:

[0145]

[0146] The relative permeability of the matrix condensate oil is calculated by the following formula:

[0147]

[0148] Among them, K rg K represents the relative permeability of the matrix gas phase. rw K represents the relative permeability of the aqueous phase in the matrix. ro K represents the relative permeability of the oil phase in the matrix. rgm K represents the endpoint value for relative permeability in the gas phase. rgw The endpoint value for relative permeability of the aqueous phase; S gd S is the dimensionless gas saturation. wd S is the dimensionless water saturation; A, A', B, B', C, and C' are fitting parameters based on the relative permeability test results of shale cores; S o S represents oil phase saturation. oc S represents the critical fluid saturation of condensate oil. wc To bind water saturation.

[0149] The relative permeabilities of gas, water, and condensate oil in the fractures are directly proportional to their respective saturations. The capillary pressure curves in the matrix are obtained from mercury intrusion porosimetry of shale cores. The capillary pressure in the fractures is calculated using the following formula:

[0150]

[0151] Among them, P cf σ is the capillary force of the crack; θ is the surface tension; W is the contact angle; f The average width of the crack.

[0152] Example 2

[0153] Based on the above embodiments, this embodiment provides an application example. Taking the X10 shale condensate gas well in the Sichuan Basin as an example, the circulating gas injection process is optimized.

[0154] Step 1: Obtain the calculation parameters.

[0155] Based on the component model, the gas injection strategy was studied. First, basic calculation parameters were obtained. The basic calculation parameters for well X10 include reservoir size of 2500m×2500m×40m, reservoir pressure of 53.9MPa, reservoir temperature of 78℃, and matrix permeability of 0.01×10-3μm. 2 Matrix porosity 0.05, rock compressibility coefficient 0.1 MPa -1 The fracture conductivity is 3.3 × 10⁻³ μm. 2 •m, microseismic monitoring data during fracturing operations, reservoir fluid composition and mole fraction (as shown in Table 1 below).

[0156] Table 1 - Reservoir fluid composition and mole fraction

[0157] Components Well flow group mol% <![CDATA[N2]]> 0.4310 <![CDATA[CO2]]> 0.2528 <![CDATA[C1]]> 80.0957 <![CDATA[C2]]> 8.3060 <![CDATA[C3]]> 2.2944 <![CDATA[iC4]]> 0.3807 <![CDATA[nC4]]> 0.4357 <![CDATA[iC5]]> 0.1638 <![CDATA[nC5]]> 0.1083 <![CDATA[C6]]> 0.1939 <![CDATA[C7]]> 0.4950 <![CDATA[C8]]> 1.2107 <![CDATA[C9]]> 1.2219 <![CDATA[C 10 ]]> 1.0504 <![CDATA[C 11+ ]]> 3.3596

[0158] Step 2: Establish geological numerical models and fracture models.

[0159] Based on the reservoir dimensions of 2500m × 2500m × 40m, a geological numerical model was established with a model grid size of 4m × 4m × 2m. Using microseismic monitoring data from hydraulic fracturing operations, a computer vision algorithm was employed to finely characterize the complex fracture network of hydraulic fracturing, establishing a microseismic inversion network model of primary fractures and secondary fractures. Using shale core samples from the X10 well's shale condensate gas reservoir, the density of bedding fractures was determined to be 3 fractures / 10cm. The distribution of bedding fractures was incorporated into the primary fracture-secondary fracture network model, ultimately establishing a three-level fracture network model of primary fractures, secondary fractures, and bedding fractures. Figure 2 .

[0160] Step 3: Establish a mathematical model for the components.

[0161] a. Mass conservation equation, hydrocarbons in the X10 well reservoir fluid have n c =15 components, and the mass conservation equation for each component is shown below:

[0162]

[0163] Where t is time; φ is porosity; n p n is the number of phases; c For group numbers; x cj ρ is the mole fraction of fluid component c in phase j; ρ is the phase density; S is the degree of saturation. For the source and sink terms of the j-th phase;

[0164] Based on the principle of conservation of momentum, the momentum equation for phase j is obtained as follows:

[0165]

[0166] Where v is velocity; k is permeability; μ is viscosity; and P is pressure.

[0167] Substituting the momentum equation of phase j into the mass conservation equation, and discretizing the mass conservation equation, and expressing it in residual form, we obtain the following calculation formula:

[0168]

[0169] Wherein, the superscripts n+1 and n represent the current and previous time steps, respectively; V′ represents the block volume; and the superscript l represents the interface of the grid block. Let be the flow rate of the j-th phase. Defined as (k rj / μ j )l The upstream weighting method is used to determine the flow degree λ. l Calculated by the following formula:

[0170]

[0171] In the formula, subscripts a and b represent grid blocks; A l Let l be the region; l is the distance between the center of the block and the centroid of the interface.

[0172] b. Thermodynamic equilibrium

[0173] The gas condensation and revaporation process is described using a thermodynamic equilibrium model. The following equations are solved by applying two-phase flash evaporation calculations to obtain the phase mole fraction and the mole fraction of each phase component.

[0174] z c =Vx cg +Lx cl c = 1, 2, ..., n c (5)

[0175]

[0176] V+L=1 (7)

[0177]

[0178] Among them, Z c V is the mole fraction of component c in the total mixture; V is the mole fraction of the gas phase; L is the mole fraction of the liquid phase; x c This represents the mole fraction of fluid component c in the gas (liquid) phase; the subscripts l and g represent the liquid phase and the gas phase, respectively. Let f be the fugacity of component c in the liquid phase. Let f be the fugacity of component c in the gas phase.

[0179] Step 4: State calculation based on state equations.

[0180] Applying the equation of state for the mixture system (e.g., the PR equation of state), we obtain the cubic equation for the compressibility factor Zm of the mixture:

[0181]

[0182] Among them, a m The gravitational coefficient, R is the molar gas constant; T is the temperature; b m For van der Waals covolute, C c Let x be the mole fraction of component c in the gas phase (or liquid phase), which is x in the gas phase. cg In the liquid phase, this is x.cl ;a c a is the gravitational coefficient of component c; c δ is an adjustable temperature function; cj b is the binary interaction coefficient; c Let be the van der Waals covolute of component c.

[0183] The expression for fugacity is as follows:

[0184]

[0185] a cj =(a c α c ) 0.5 (a j α j ) 0.5 (1-δ cj (13)

[0186] In the formula, Let C be the fugacity of component C, which is [value missing] in the liquid phase. In the gas phase

[0187] Step 5: Condensation flash evaporation calculation.

[0188] The stability of a single phase is determined based on the Gibbs energy minimization principle. If unstable, the mole fractions of the gas and liquid phases, as well as the component composition of each phase, are obtained through flash evaporation calculations. The successive substitution method is used for the flash evaporation calculations, and the gas-liquid equilibrium constant K is continuously updated during the solution process. c The process of approximating the exact solution step by step includes:

[0189] a. Obtain the initial value of the equilibrium constant using Wilson's formula:

[0190]

[0191] Among them, P cc ω is the critical pressure of component c; c is the Pitzer eccentricity factor for component c.

[0192] b. Through K c Values ​​of V and L

[0193] The liquid and gas phase material balance equations are shown below:

[0194]

[0195] Substituting equations (16) and (17) into equation (8) yields the Ratchford-Rice equation:

[0196]

[0197] Given K c The value is obtained by solving equation (18) using the bisection method or Newton's iteration method. First, the bisection method is used to ensure a relatively accurate initial value, and then the exact solution is obtained quickly through Newton's iteration to obtain V. Then, L is calculated by equation (7).

[0198] c. Given z c K c The values ​​of V and L are obtained by calculating x using equations (16) and (17). cl and x cg .

[0199] d. Given x cl and x cg The value is calculated using equations (9), (10), and (11) to determine the compression factor Z. m The largest positive root is the gas-phase compressibility factor Z of the gas-phase mixture. g The smallest positive root is the liquid compressibility factor Z of the liquid mixture. l value.

[0200] e. Given x cl x cg Z g Z l The fugacity of component c in the liquid phase is calculated using equations (12) and (13). And in the gas phase, it is called fugacity.

[0201] f. Inspection and If equation (6) is not satisfied, then it is further determined whether the preset error condition (e.g., error < 5%) is met. Then, the new equilibrium constant K is calculated according to equation (14). c Repeat steps b to f until the error condition is met, thereby improving the accuracy of the production model simulation.

[0202] g, Given x cl x cg Z g Z l The gas phase density ρ is calculated using equations (19) and (20). g and liquid phase density ρ l :

[0203]

[0204]

[0205] Among them, M cdenoted as c, representing the relative molecular mass of component c.

[0206] Finally, the mole fraction V of the gas phase, the mole fraction L of the liquid phase, and the mole fraction x of the fluid component c in the liquid phase l are calculated. cl The mole fraction x of fluid component c in gas phase g cg Gas phase density ρ g and liquid phase density ρ l Substituting into the calculation formula (3) makes the thermodynamic equilibrium model coupled with the component mathematical model.

[0207] Step Six: Fully Implicit Numerical Solution.

[0208] The following nonlinear system composed of equation (3) is solved by a fully implicit scheme based on the Newton-Raphson method:

[0209]

[0210] Where x represents a vector of unknowns, and the unknowns for each block include (P, z1, z2, ..., z...). nc ); r represents the residual vector; J represents the Jacobian matrix; subscript n+1 represents the current time step; superscript k+1 and k represent the current and previous nonlinear iterations, respectively.

[0211] By solving the thermodynamic equilibrium model and performing numerical differentiation in step five, the required fluid properties and their derivatives in r and J are obtained. In this way, the mass conservation equation and the thermodynamic equilibrium model are fully coupled, the nonlinear system is solved on a fully implicit parallel framework, and the PT phase diagram of the X10 well reservoir fluid is calculated, as follows: Figure 3 As shown.

[0212] Step 7: Relative permeability and capillary pressure model.

[0213] The relative permeability of the matrix gas phase is calculated by the following formula:

[0214]

[0215] The relative permeability of the aqueous phase in the matrix is ​​calculated by the following formula:

[0216]

[0217] The relative permeability of the matrix condensate oil is calculated by the following formula:

[0218]

[0219] Among them, K rg K represents the relative permeability of the matrix gas phase. rw K represents the relative permeability of the aqueous phase in the matrix. roK represents the relative permeability of the oil phase in the matrix. rgm K represents the endpoint value for relative permeability in the gas phase. rgw The endpoint value for relative permeability of the aqueous phase; S gd S is the dimensionless gas saturation. wd S is the dimensionless water saturation; A, A', B, B', C, and C' are fitting parameters based on the relative permeability test results of shale cores; S o S represents oil phase saturation. oc S represents the critical fluid saturation of condensate oil. wc To bind water saturation.

[0220] like Figure 4 As shown, the relative permeability curve of matrix gas and water in well X10 is calculated using the above formula, and the relative permeability of matrix condensate oil is calculated according to formula (21).

[0221] The relative permeability of gas, water, and condensate oil in the fractures of well X10 is proportional to the saturation of gas, water, and condensate oil, respectively, with a proportionality coefficient of 1.

[0222] The capillary pressure curve in the matrix was obtained from mercury intrusion porosimetry of shale cores. The capillary pressure in the fractures was calculated using the following formula:

[0223]

[0224] Among them, P cf σ is the capillary force of the crack; θ is the surface tension; W is the contact angle; f The average width of the crack.

[0225] like Figure 5 As shown, the capillary pressure curve in the matrix of well X10 was calculated using the above formula. The capillary pressure in the fracture was 90 Pa, which can be ignored.

[0226] Step 8: Optimize the circulating gas injection process.

[0227] In the numerical model and fracture model established in step two, the pressure (reservoir pressure) and initial saturation value (initial value of reservoir fluid composition) are assigned. The distribution of gas, water, and condensate phases is obtained by numerical solution using the fully implicit method in step six. The relative permeability and capillary pressure in step seven are introduced to simulate the total volume of formation condensate, cumulative oil production, and cumulative gas production under different circulating gas injection process conditions. The circulating gas injection process that can achieve the minimum total volume of formation condensate and the maximum cumulative gas production is selected as the optimized circulating gas injection process.

[0228] Two circulating gas injection processes were simulated in well X10: (1) Circulating gas injection process one: exhaustion production was carried out for 180 days with a bottom hole pressure of 30MPa, and then the produced gas was injected in a circulating manner (10 huff and puff cycles). In each cycle, the injection rate was 20,000 m³ / h.3 / d gas injection for 30 days, then shut in the well for 30 days, and finally produce gas for 60 days at a bottom pressure of 30MPa. (2) Circulating gas injection process two: exhaustion production for 180 days at a bottom pressure of 30MPa, then start circulating gas injection (10 huff and puff cycles). The first huff and puff cycle is: at an injection rate of 20000m 3 The well was injected for 30 days, then shut in for 30 days, and finally produced at a bottomhole pressure of 30 MPa for 60 days. Afterward, the injection rate increased by 2000 m³ / d in each cycle. 3 / d.

[0229] like Figure 6 As shown, this represents the total volume of formation condensate oil obtained through simulation. Figure 7 As shown, the cumulative oil production obtained from the simulation is as follows: Figure 8 As shown, the cumulative gas production was obtained through simulation. After comparison, it can be seen that the total volume of formation condensate oil in the second circulating gas injection process is smaller than that in the first circulating gas injection process, while the cumulative gas production is greater. Therefore, the second circulating gas injection process is selected as the optimized circulating gas injection process for well X10.

[0230] In summary, the optimization method for circulating gas injection technology in shale condensate gas reservoirs provided in this embodiment specifically includes: obtaining calculation parameters, establishing geological numerical models and fracture models, establishing component mathematical models, state calculations based on equations of state, condensate flash evaporation calculations, fully implicit numerical solutions, relative permeability and capillary pressure models, and optimization of the circulating gas injection process. This invention integrates microseismic inversion, phase equilibrium calculations, and numerical simulations, with the optimization objectives of minimizing the total volume of formation condensate oil and maximizing cumulative gas production. It quantitatively determines the circulating gas injection rate and quantitatively optimizes the circulating gas injection technology for shale condensate gas reservoirs, effectively guiding the selection of circulating gas injection technology and optimization of process parameters in the field, thereby achieving long-term stable production of shale condensate gas reservoirs.

[0231] Example 3

[0232] Based on the above embodiments, such as Figure 9 As shown, this embodiment provides an optimization device for the circulating gas injection process of shale condensate gas reservoirs, including:

[0233] Data acquisition module 910 is used to acquire geological characteristic parameters and microseismic monitoring data of the target shale condensate gas reservoir and fracturing operation.

[0234] The stratigraphic model building module 920 is used to build a stratigraphic model that reflects the fracture network during the fracturing process of the shale condensate gas reservoir based on the geological characteristic parameters and microseismic monitoring data of the fracturing operation.

[0235] The model coupling module 930 is used to establish a component mathematical model based on the fluid composition and mole fraction in the target shale condensate gas reservoir under the principles of mass conservation and momentum conservation, establish a thermodynamic equilibrium model for the dynamic change process of the fluid composition under different pressures and temperatures, and couple the thermodynamic equilibrium model with the component mathematical model.

[0236] The permeability and capillary pressure establishment module 940 is used to establish the relative permeability model and capillary pressure model of the fluid components.

[0237] The model integration module 950 is used to integrate the relative permeability model, the capillary pressure model, and the coupled thermodynamic equilibrium model and component mathematical model into the formation model to obtain a production model that simulates the oil and gas production of the target shale condensate gas reservoir under different gas injection processes.

[0238] The production calculation module 960 is used to input parameters of different circulating gas injection processes into the production model. Under each circulating gas injection process, the production model is solved to obtain the simulated oil production volume and simulated gas production under that circulating gas injection process.

[0239] The process determination module 970 is used to compare the simulated oil production volume and simulated gas production under each cycle gas injection process, and determine the parameters of the preferred cycle gas injection process with the optimization objectives of minimizing the simulated oil production volume and maximizing the simulated gas production.

[0240] In one embodiment, the shale condensate gas reservoir circulating gas injection process optimization device can realize the function of any of the shale condensate gas reservoir circulating gas injection process optimization methods in the above embodiments.

[0241] Specific limitations regarding the optimization device for the circulating gas injection process in shale condensate gas reservoirs can be found in the limitations of the optimization method for the circulating gas injection process in shale condensate gas reservoirs mentioned above, and will not be repeated here. Each unit in the aforementioned optimization device for the circulating gas injection process in shale condensate gas reservoirs can be implemented entirely or partially through software, hardware, or a combination thereof. These units can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each unit.

[0242] Example 4

[0243] Based on the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the above embodiments.

[0244] In some embodiments of this example, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the steps of the method described in the above embodiments.

[0245] In some embodiments of this example, a computer program product is provided, including a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the method described in the above embodiments.

[0246] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component, for executing the methods described in the above embodiments.

[0247] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof. Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (such as hard disks, floppy disks, solid-state drives, removable disks, and Blu-ray discs).

[0248] Computer-readable storage media may also store at least one computer-executable program / instruction, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0249] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).

[0250] The processor can communicate with external devices via the I / O bus through wired or wireless networks.

[0251] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0252] In the embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0253] It should be noted that, in this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0254] While the embodiments disclosed herein are as described above, the foregoing content is merely for the purpose of facilitating understanding of this disclosure and is not intended to limit this disclosure. Any person skilled in the art to which this disclosure pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope of this disclosure; however, the scope of patent protection of this disclosure shall still be determined by the scope defined in the appended claims.

Claims

1. An optimized method for circulating gas injection technology in shale condensate gas reservoirs, characterized in that, include: Obtain geological characteristic parameters and microseismic monitoring data of the target shale condensate gas reservoir during fracturing operations; Based on the geological characteristic parameters and microseismic monitoring data during fracturing operations, a stratigraphic model reflecting the fracture network during the fracturing process of the shale condensate gas reservoir was established. Based on the principles of mass conservation and momentum conservation, a component mathematical model is established according to the fluid composition and mole fraction in the target shale condensate gas reservoir. A thermodynamic equilibrium model is established for the dynamic changes of the fluid composition under different pressures and temperatures. The thermodynamic equilibrium model is then coupled with the component mathematical model. Establish a relative permeability model and a capillary pressure model for the fluid components; The relative permeability model, the capillary pressure model, and the coupled thermodynamic equilibrium model and component mathematical model are integrated into the formation model to obtain a production model that simulates the oil and gas production of the target shale condensate gas reservoir under different gas injection processes. Input the parameters of different cyclic gas injection processes into the production model, and solve the production model under each cyclic gas injection process to obtain the simulated oil production volume and simulated gas production under that cyclic gas injection process. By comparing the simulated oil production volume and simulated gas production rate under each cycle of gas injection process, and taking the minimization of the simulated oil production volume and the maximization of the simulated gas production rate as optimization objectives, the parameters of the preferred cycle of gas injection process are determined.

2. The method according to claim 1, characterized in that, The steps for establishing a formation model that reflects the fracture network during the fracturing process of the shale condensate gas reservoir include: Based on the geological feature parameters, the model grid size of the stratigraphic model is determined, and a geological numerical model is established based on the grid size; Based on the geological numerical model, the fracture network is characterized using the microseismic monitoring data from the fracturing operation, and a microseismic inversion network model of the main fracture-secondary fracture is established based on the fracture network. Data on the distribution of bedding fractures were obtained using shale core samples from the shale condensate gas reservoir. The bedding fracture distribution data are introduced into the main fracture-secondary fracture microseismic inversion network model to obtain the stratigraphic model reflecting the three-level fracture network of the main fracture-secondary fracture-bedding fracture.

3. The method according to claim 1, characterized in that, The step of establishing a component mathematical model based on the fluid composition and mole fraction in the target shale condensate gas reservoir includes: Based on the principle of mass conservation, the following mass conservation equation is established for the fluid components: Where t is time; φ is porosity; n p n is the number of phases; c For group numbers; x cj ρ is the mole fraction of fluid component c in phase j; ρ is the phase density; S is the degree of saturation. For the source and sink terms of the j-th phase; Based on the principle of conservation of momentum, the momentum equation for phase j is obtained as follows: Where v is velocity; k is permeability; μ is viscosity; and P is pressure. Substituting the momentum equation of phase j into the mass conservation equation, and discretizing the mass conservation equation, and expressing it in residual form, we obtain the following calculation formula: Wherein, the superscripts n+1 and n represent the current and previous time steps, respectively; V′ represents the block volume; and the superscript l represents the interface of the grid block. Let be the flow rate of the j-th phase.

4. The method according to claim 3, characterized in that, The calculation formula for the thermodynamic equilibrium model is as follows: z c =Vx cg +Lx cl ,c=1,2,…,n c ; V+L=1; Among them, Z c V is the mole fraction of component c in the total mixture; V is the mole fraction of the gas phase; L is the mole fraction of the liquid phase; x c This represents the mole fraction of fluid component c in the gas (liquid) phase; the subscripts l and g represent the liquid phase and the gas phase, respectively. Let f be the fugacity of component c in the liquid phase. Let f be the fugacity of component c in the gas phase.

5. The method according to claim 4, characterized in that, The step of coupling the thermodynamic equilibrium model with the component mathematical model includes: An initial value for the equilibrium constant is obtained. Based on this initial value, the mole fraction V of the gas phase, the mole fraction L of the liquid phase, and the mole fraction x of fluid component c in liquid phase l are calculated using the condensation flash evaporation calculation method in the thermodynamic equilibrium model. cl The mole fraction x of fluid component c in gas phase g cg ; x cl and x cg Substitute the preset compressibility factor Z of the mixture m In the cubic equation of state, the compressibility factor Z is calculated. m The value of , where the compression factor Z m Among the values, the largest positive root is the gas phase compressibility factor Z of the gas phase mixture. g The smallest positive root is the liquid compressibility factor Z of the liquid mixture. l ; x cl x cg Gas phase compressibility factor Z g and liquid phase compressibility factor Z l Substitute these values ​​into the preset gas phase density calculation formula and the preset liquid phase density calculation formula to calculate the corresponding gas phase density and liquid phase density. The mole fraction V of the gas phase, the mole fraction L of the liquid phase, and the mole fraction x of fluid component c in the liquid phase l are given. cl The mole fraction x of fluid component c in gas phase g cg The gas phase density and liquid phase density are substituted into the mass conservation equation expressed in the residual scheme to couple the thermodynamic equilibrium model with the component mathematical model.

6. The method according to claim 5, characterized in that, The calculation yields the compression factor Z. m After the step of determining the value, it also includes: According to the compressibility factor Z of the mixture m From the cubic equation of state, we obtain expressions for the fugacity of component c in the liquid phase and in the gas phase. x cl x cg Gas phase compressibility factor Z g and liquid phase compressibility factor Z l Substituting these values ​​into the expressions for the fugacity of component c in the liquid phase and the fugacity of component c in the gas phase, the fugacity of component c in the liquid phase is calculated. and the fugacity of component c in the gas phase When the difference between the fugacity of component c in the liquid phase and the fugacity of component c in the gas phase does not meet the preset error condition, the gas-liquid equilibrium constant is updated, and x is recalculated using the condensation flash evaporation calculation method under the updated gas-liquid equilibrium constant. cl x cg and using this x cl x cg The new gas-phase compressibility factor Z was calculated. g and liquid phase compressibility factor Z l According to the new x cl x cg Gas phase compressibility factor Z g and liquid phase compressibility factor Z l Calculation yields new And new Verify the new one again And new The difference, until the new And new If the preset error conditions are met, the calculation will stop.

7. The method according to any one of claims 1-6, characterized in that, The steps of solving the production model to obtain the simulated oil production volume and simulated gas production under the cyclic gas injection process include: The mass conservation equation represented by the residual scheme in the production model is solved using a fully implicit scheme based on the Newton-Raphson method, and the simulated oil production volume and simulated gas production under the cyclic gas injection process are obtained.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 7.

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