A method for predicting multi-cycle injection and production reservoir capacity of water-intrusion gas reservoir type gas storage

Abstract

The present application relates to the technical field of gas reservoir development and efficient operation of gas storage, and particularly relates to a multi-cycle injection-production storage capacity dynamic prediction method for water-invasion gas reservoir type gas storage. The method comprises: obtaining injection-production experimental data of each cycle based on multi-cycle injection-production experiments; determining the phase permeability hysteresis coefficient, stress sensitivity coefficient, water invasion coefficient and water recession coefficient corresponding to each cycle based on the injection-production experimental data of each cycle; constructing a water invasion-seepage-pressure dynamic coupling model based on the phase permeability hysteresis coefficient, stress sensitivity coefficient, water invasion coefficient and water recession coefficient corresponding to each cycle; and predicting the storage capacity of the water-invasion gas reservoir type gas storage based on the water invasion-seepage-pressure dynamic coupling model. The present application solves the problems of storage capacity prediction deviation, insufficient experimental reproducibility and missing coupling model caused by dynamic interference of water invasion, stress sensitivity effect and hysteresis effect under water invasion conditions.

Description

Technical Field

[0001] This invention relates to the field of gas reservoir development and efficient operation of gas storage facilities, and in particular to a method for dynamic prediction of multi-cycle injection and production capacity of water-inundated gas reservoirs. Background Technology

[0002] Existing technologies have three significant drawbacks in addressing multi-cycle injection-production dynamic simulation of water-inundated gas reservoirs: 1) Capacity prediction bias: Traditional static / dynamic methods neglect the nonlinear response of water intrusion, irreversible shift of the gas-water phase permeability curve, and reservoir elastoplastic deformation during multi-cycle injection-production, leading to significant errors in effective capacity prediction. 2) Insufficient experimental reproducibility: Limited pressure alternation rate and number of cycles (usually <5 cycles) prevent simulation of stress-sensitive effects and non-Darcy flow under high-speed injection-production conditions, resulting in distorted seepage parameters. 3) Lack of coupled model: The absence of a dynamic coupling mechanism between water intrusion, seepage, and pressure results in insufficient basis for optimizing reservoir utilization efficiency.

[0003] Chinese patent CN115619578A discloses a method, apparatus, equipment, and storage medium for calculating the capacity of aquifer gas storage facilities. This method improves the accuracy of calculating the capacity of aquifer-type gas storage facilities when the aquifer water body is open. Chinese patent CN121413263A discloses a dynamic correction method for the capacity of condensate gas reservoir storage facilities. This patent, through basic data collection and model construction, experimental measurement and multi-pathway calculation of pore volume changes, model correction, parameter verification, experimental simulation, and final prediction and optimization, can accurately calculate and correct capacity-related parameters, effectively quantify the influence of various factors on pore volume, and achieve accurate prediction of capacity changes under future multi-cycle injection and production of gas storage facilities.

[0004] Therefore, there is an urgent need to propose a new dynamic coupling method for water intrusion-seepage-pressure to solve the problems of reservoir capacity prediction deviation caused by dynamic interference from water intrusion, stress sensitivity effect and hysteresis effect under water intrusion conditions, insufficient experimental reproducibility and lack of coupling model. Summary of the Invention

[0005] To address the aforementioned problems, this invention provides a multi-cycle injection-production dynamic prediction method for water-inundated gas reservoirs. This method aims to resolve issues in existing technologies such as prediction deviations due to dynamic interference from water intrusion, stress sensitivity effects, and hysteresis effects under water intrusion conditions, as well as insufficient experimental reproducibility and the lack of coupled models. The invention provides the following technical solution: In a first aspect of the present invention, a method for dynamic prediction of multi-cycle injection-production capacity of a water-inundated gas reservoir is provided, the method comprising: Based on multi-cycle injection and extraction experiments, data from each cycle of injection and extraction experiments were obtained. Based on the injection and production experimental data of each cycle, the relative permeability hysteresis coefficient, stress sensitivity coefficient, water intrusion coefficient, and water retreat coefficient corresponding to each cycle were determined. Based on the phase permeation hysteresis coefficient, stress sensitivity coefficient, water intrusion coefficient, and water retreat coefficient corresponding to each cycle, a dynamic coupling model of water intrusion-seepage-pressure is constructed. Based on the aforementioned dynamic coupling model of water intrusion-seepage-pressure, the storage capacity of water-intrusion gas reservoirs is predicted. Based on the relative permeability hysteresis coefficient, stress sensitivity coefficient, water intrusion coefficient, and water regression coefficient corresponding to each cycle, a dynamic coupling model of water intrusion-seepage-pressure is constructed, including: Based on the relative permeation lag coefficient, stress sensitivity coefficient, water intrusion coefficient, and water retreat coefficient corresponding to all historical cycles at the i-th cycle, construct the relative permeation lag coefficient set, stress sensitivity coefficient set, water intrusion coefficient set, and water retreat coefficient set corresponding to the i-th cycle. Based on the least squares method, the set of relative permeation hysteresis coefficients, stress sensitivity coefficients, water intrusion coefficients and water retreat coefficients corresponding to the i-th period are fitted and calculated respectively to obtain the optimal relative permeation hysteresis coefficient, optimal stress sensitivity coefficient, optimal water intrusion coefficient and optimal water retreat coefficient corresponding to the i-th period. Based on the optimal phase permeation hysteresis coefficient, optimal stress sensitivity coefficient, optimal water intrusion coefficient, optimal water retreat coefficient, and the first injection-production data corresponding to the i-th period, a dynamic coupling model of water intrusion-seepage-pressure is constructed.

[0006] Furthermore, the injection-production experimental data for each cycle include initial formation pressure, formation pressure at the end of gas injection, formation pressure at the end of gas production, gas injection rate, gas production rate, water production rate, water intrusion rate, water retreat rate, total core pore volume, and the first injection-production data, wherein, The first injection and production data includes residual gas saturation, bound water saturation, oil saturation, pressure variation, net water intrusion, and upper pressure gas volume coefficient.

[0007] Furthermore, the expression for the dynamic coupling model of water intrusion-seepage-pressure is as follows:

[0008] in,

[0009]

[0010]

[0011] In the formula, Indicates the first i Periodic storage capacity; This represents the total pore volume of the core. Indicates the first iPeriodic residual gas saturation; Indicates the first i Periodically bound water saturation; Indicates the first i Oil saturation during the cycle; Indicates the first i The optimal stress sensitivity coefficient corresponding to the period; Indicates the first i The optimal phase permeation hysteresis coefficient corresponding to the period; Indicates the first i Cycle water intrusion volume; Indicates the upper limit pressure gas volume coefficient; Indicates the first i Periodic water intrusion time; Indicates the first i Periodic water receding time; Indicates the first i Formation pressure at the end of cyclic gas injection; Indicates the first i Formation pressure at the end of cyclic gas production; Indicates the first i The magnitude of pressure changes during the periodic flooding phase; Indicates the first i The magnitude of pressure changes during the periodic receding water level; Indicates the first i The optimal water intrusion coefficient corresponding to the period; Indicates the first i The optimal water receding coefficient corresponding to the cycle; Indicates the first i Periodic water intrusion volume; Indicates the first i Periodic water receding volume; Indicates the first i Cycle water production; m This indicates the current injection / production cycle.

[0012] Furthermore, the relative permeability hysteresis coefficient is determined, including: Based on multi-cycle injection and production experiments, the injection volume, production volume, water production, residual gas saturation, formation pressure at the end of injection, and formation pressure at the end of production were obtained for each cycle. A hysteresis model was constructed based on the gas injection volume, gas production volume, water production volume, residual gas saturation, formation pressure at the end of gas injection, and formation pressure at the end of gas production for each cycle. Based on the hysteresis effect model, the phase permeation hysteresis coefficient corresponding to each cycle is obtained.

[0013] Furthermore, the expression for the phase permeation hysteresis coefficient corresponding to each cycle is as follows:

[0014] In the formula,

[0015] In the formula, Indicates the first i The phase permeation hysteresis coefficient corresponding to the period; Indicates the first i Periodic residual gas saturation; Indicates the first i -1 cycle residual gas saturation; This represents the weight of the pressure change in the i-th period in the cumulative pressure change over historical periods. Indicates the first i Formation pressure at the end of cyclic gas injection; Indicates the first i Formation pressure at the end of cyclic gas production; m This indicates the current injection / production cycle.

[0016] Furthermore, the stress sensitivity coefficients corresponding to each cycle are determined, including: Based on multi-cycle injection and production experimental data, core permeability measurement data, formation pressure data at the end of gas injection, and formation pressure data at the end of gas production were extracted for each cycle. Based on the measured core permeability data for each cycle, a fitted exponential decay model was constructed. Based on the fitted exponential decay model, the stress sensitivity coefficient corresponding to each period is determined.

[0017] Furthermore, the expression for the stress sensitivity coefficient corresponding to each period is as follows:

[0018] In the formula, Indicates the first i Cycle penetration rate; Indicates the initial penetration rate; Indicates the first i Periodic stress sensitivity coefficient; Indicates the first i The phase permeation hysteresis coefficient corresponding to the period; Indicates the first i The magnitude of pressure changes during the periodic receding water level; m This indicates the current injection / production cycle.

[0019] Furthermore, the intrusion and receding coefficients for each cycle are determined, including: Based on multi-cycle injection and production experimental data, the water intrusion, water receding, formation pressure at the end of gas injection, and formation pressure at the end of gas production were extracted for each cycle. Based on the water intrusion, water retreat, formation pressure at the end of gas injection, and formation pressure at the end of gas production for each cycle, dynamic response models for water intrusion and water retreat for each cycle are constructed. Based on the dynamic response model of water intrusion for each cycle, the water intrusion coefficient for each cycle is determined. Based on the dynamic response model of water recession corresponding to each cycle, the water recession coefficient for each cycle is determined. Furthermore, the expression for the water intrusion coefficient is:

[0020] The expression for the water recession coefficient is:

[0021] In the formula,

[0022]

[0023] In the formula, Indicates the first i Periodic water intrusion coefficient; Indicates the first i Periodic water intrusion volume; Indicates the first i Periodic water receding coefficient; Indicates the first i Periodic water receding volume; Indicates the first i Formation pressure at the end of cyclic gas injection; Indicates the first i Formation pressure at the end of cyclic gas production; Indicates the first i- Formation pressure at the end of a single gas production cycle; Indicates the first i Periodic water intrusion time; Indicates the first i Periodic water receding time; Indicates the first i The magnitude of pressure changes during the periodic flooding phase; Indicates the first i The magnitude of pressure changes during the cyclical water recession phase.

[0024] In a second aspect of the invention, a device for dynamic prediction of multi-cycle injection-production capacity of a water-inundated gas reservoir is provided, the device comprising: The acquisition unit is used to acquire data from each cycle of injection and extraction experiments based on multi-cycle injection and extraction experiments. The determination unit is used to determine the relative permeability hysteresis coefficient, stress sensitivity coefficient, water intrusion coefficient, and water retreat coefficient for each cycle based on the injection and production experimental data of each cycle. The building blocks are used to construct a dynamic coupling model of water intrusion-seepage-pressure based on the relative permeability hysteresis coefficient, stress sensitivity coefficient, water intrusion coefficient, and water regression coefficient corresponding to each cycle, including: Based on the relative permeation lag coefficient, stress sensitivity coefficient, water intrusion coefficient, and water retreat coefficient corresponding to all historical cycles at the i-th cycle, construct the relative permeation lag coefficient set, stress sensitivity coefficient set, water intrusion coefficient set, and water retreat coefficient set corresponding to the i-th cycle. Based on the least squares method, the set of relative permeation hysteresis coefficients, stress sensitivity coefficients, water intrusion coefficients and water retreat coefficients corresponding to the i-th period are fitted and calculated respectively to obtain the optimal relative permeation hysteresis coefficient, optimal stress sensitivity coefficient, optimal water intrusion coefficient and optimal water retreat coefficient corresponding to the i-th period. Based on the optimal phase permeation hysteresis coefficient, optimal stress sensitivity coefficient, optimal water intrusion coefficient, optimal water retreat coefficient, and the first injection-production data corresponding to the i-th period, a dynamic coupling model of water intrusion-seepage-pressure is constructed.

[0025] The prediction unit is used to predict the storage capacity of water-inundated gas reservoirs based on the dynamic coupling model of water intrusion-seepage-pressure.

[0026] In a third aspect of the invention, an electronic device is provided, the electronic device comprising at least one processor and at least one memory, the memory being data-connected to the processor, wherein... The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method described above.

[0027] In a fourth aspect of the invention, a computer-storeable medium is provided, wherein computer instructions are stored thereon, and when executed by a processor, the computer instructions specifically perform the steps in the above-described method.

[0028] In a fifth aspect of the invention, a computer program product is provided, comprising computer instructions that, when executed by a processor, specifically perform the steps in the method described above.

[0029] The technical effects and advantages of this invention are as follows: This invention achieves the following breakthrough effects in the development of gas reservoirs in water-intrusion reservoirs through a long core multi-period dynamic simulation system and a triple coupling correction mechanism of water intrusion-seepage-stress: 1) Revolutionary Improvement in Storage Capacity Prediction Accuracy: Based on real-time corrected phase permeability hysteresis coefficient and stress sensitivity coefficient, a dynamic coupling model of water intrusion-seepage-pressure is constructed by combining water intrusion coefficient and water retreat coefficient, and this model is used as a nonlinear storage capacity prediction model. The storage capacity prediction error of the traditional static model is reduced from >15% to <5%. This breaks through the limitation of relying on ideal mass balance equations and significantly improves the reliability of gas storage peak-shaving capacity planning.

[0030] 2) Optimization of gas storage facility operation efficiency: Real-time monitoring via online gas chromatograph By dynamically adjusting the gas injection pressure and equilibrium time, the content of the gas can be controlled, effectively suppressing phase changes, controlling the inrush of water at the edge, and reducing resource losses caused by flooding.

[0031] 3) High-fidelity reproduction of the full-cycle seepage mechanism: The first-ever ≥10 pressure alternating cycle experiments accurately capture the cumulative effect of stress sensitivity and phase permeation hysteresis, with a permeability decay quantification accuracy >95%. It restores the additional pressure drop characteristics of non-Darcy flow, reducing the prediction error of gas-water crossflow from 25% to <8%.

[0032] 4) Adaptability to complex reservoirs and engineering risk control: For reservoirs with both fractured and porous media, targeted regulation is achieved through heterogeneous core combinations and CO2 synergistic displacement, improving the utilization efficiency of low-permeability zones by 18%. The response time for risk warnings during multi-cycle operation is shortened.

[0033] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description and the drawings. Attached Figure Description

[0034] Figure 1 This is a flowchart of the method for dynamic prediction of multi-cycle injection and production capacity of water-inundated gas reservoirs provided in the embodiments of this application; Figure 2 This is a diagram of the multi-cycle injection and production experimental device for a water-inundated gas reservoir provided in the embodiments of this application; Figure 3 This is a structural block diagram of an electronic device according to an embodiment of this application; Figure 4 The results of dynamic modification of relative permeability for different injection and production cycles provided in the embodiments of this application; Figure 5 The permeability damage rate and irreversible damage rate variation patterns are provided for the embodiments of this application.

[0035] In the figure: 1-First injection pump, 2-Condensate gas sample intermediate container, 3-Injected dry gas intermediate container, 4-First back pressure valve, 5-Permeability measuring device, 6-First metering device, 7-Online chromatograph, 8-First valve, 9-First pressure sensor, 10-Core holder, 11-Second valve, 12-Containing pressure pump, 13-Second pressure sensor, 14-Second back pressure valve, 15-Second metering device, 16-Second injection pump, 17-Third valve, 18-Formation water sample intermediate container, 19-Electrode sensor. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] To address the shortcomings of existing technologies, this invention discloses a method for dynamic prediction of multi-cycle injection-production capacity of water-inundated gas reservoirs, such as... Figure 1 As shown, the method includes, Step 1: Based on multi-cycle injection and production experiments, obtain injection and production experiment data for each cycle; the injection and production experiment data for each cycle include initial formation pressure, formation pressure at the end of gas injection, formation pressure at the end of gas production, gas injection rate, gas production rate, water production rate, water intrusion rate, water retreat rate, bound water saturation, oil saturation, and residual gas saturation.

[0038] Step 2: Based on the injection and production experimental data of each cycle, determine the relative permeability hysteresis coefficient, stress sensitivity coefficient, water intrusion coefficient, and water retreat coefficient corresponding to each cycle; Step 3: Based on the relative permeability hysteresis coefficient, stress sensitivity coefficient, water intrusion coefficient, and water retreat coefficient corresponding to each cycle, the optimal correction coefficient is established by fitting the relationship with the cycle using the least squares method, and a dynamic coupling model of water intrusion-seepage-pressure is constructed. Step 4: Based on the aforementioned water intrusion-seepage-pressure dynamic coupling model, predict the storage capacity of the water-intrusion gas reservoir.

[0039] In a specific embodiment of the present invention, the specific steps for obtaining injection and acquisition data for each cycle based on multi-cycle injection and acquisition experiments in step 1 are as follows: Multi-cycle injection-production experiments, including gas injection, balancing, and gas production stages, were performed on long core samples. During each stage of the multi-cycle injection-production experiments, changes in physical quantities were monitored synchronously to generate raw monitoring data. The raw monitoring data were then filtered and converted to generate multi-cycle injection-production experimental data.

[0040] For example, such as Figure 2As shown, the apparatus used in the multi-cycle injection-production experiment simulates the gas reservoir stage. A saturated core is placed in the core holder 10, the second valve 11 is opened, the confining pressure pump 12 is started, and the pressure is applied to the overlying strata. The second valve 11 is closed, the first injection pump 1 is started, and the second backpressure valve 14 is controlled to the original formation pressure. The first valve 8 and the third valve 17 are opened, and the condensate gas sample intermediate container 2 is pushed to inject condensate gas into the core holder 10. The amount of water discharged is monitored by the second metering device 15, the initial water saturation is calculated, and the pressure is monitored by the first pressure sensor 9 and the second pressure sensor 13. Once the pressure stabilizes at the original formation pressure, the first valve 8 and the third valve 17 are closed.

[0041] Simulating gas reservoir development, the third valve 17 is opened, and the second injection pump 16 pushes the intermediate container 18 of the formation water sample to simulate water intrusion. At the same time, the injection volume of the second injection pump is monitored to obtain the water intrusion volume. The first valve 8 is opened to control the pressure drop rate of the first back pressure valve 4. The permeability is measured by the permeability measuring device 5. The water production, gas production, and oil production are monitored by the first metering device 6. The components of the produced oil and gas fluids are measured by the online chromatograph 7. The liquid saturation in the core affected by water intrusion is monitored by the electrode sensor 19. The pressure sensor 9 is monitored until the formation pressure drops to the depletion pressure, at which point the first valve 8 and the third valve 17 are closed.

[0042] During the gas injection phase, the first injection pump 1 is started, the second back pressure valve 14 is controlled to the formation pressure at the end of the injection phase, the first valve 8 and the third valve 17 are opened, the intermediate dry gas container 3 is pushed, and the injected gas is injected into the core holder 10. The gas injection volume is monitored by the first injection pump 1, and the water discharge volume is monitored by the second metering device 15 to obtain the water receding volume. When the pressure monitored by the first pressure sensor 9 reaches the formation pressure at the end of the gas injection phase, the first valve 8 and the third valve 17 are closed.

[0043] During the gas production phase, the third valve 17 is opened, and the second injection pump 16 pushes the intermediate container 18 of the formation water sample to simulate water intrusion. At the same time, the injection volume of the second injection pump is monitored to obtain the water intrusion volume. The first valve 8 is opened to control the pressure drop rate of the first back pressure valve 4. The permeability is measured by the permeability measuring device 5. The water production, gas production, and oil production are monitored by the first metering device 6. The components of the produced oil and gas fluids are measured by the online chromatograph 7. The liquid saturation in the core affected by water intrusion is monitored by the electrode sensor 19, and then the residual gas saturation of the core affected by water intrusion is calculated. The first pressure sensor 9 is monitored until the formation pressure drops to the end-of-production pressure, at which point the first valve 8 and the third valve 17 are closed.

[0044] Experimental process of gas injection and gas extraction phases to simulate the gas storage tank injection and extraction cycle.

[0045] During the simulated gas storage injection-production cycle, the injection volume was recorded using a constant-speed, constant-pressure pump; the production volume and water production were obtained using a gas-liquid acquisition device; the formation pressure at the end of injection and production was recorded using pressure sensors; and the resistivity changes at different locations during the displacement process were measured using electrode sensors in contact with the core facies within the holder. i Periodic influence of water intrusion on the fluid saturation within the core The first one is obtained by calculation using the formula. i Periodic residual gas saturation Residual gas saturation The calculation expression is:

[0046] In the formula, Indicates the first i The saturation of the liquid inside the core is affected by water intrusion during the cycle. Indicates the first i Periodic residual gas saturation.

[0047] In a specific embodiment of the present invention, for step 2: based on the injection-production experimental data of each cycle, determine the relative permeability hysteresis coefficient, stress sensitivity coefficient, water intrusion coefficient, and water retreat coefficient corresponding to each cycle, wherein, Relative permeation hysteresis coefficient The residual gas saturation was obtained through multi-cycle calculations. Since the pore throat occupancy effect essentially corresponds to an increase in the volume fraction of immovable fluid, exhibiting the same physical behavior as an increase in bound water saturation, the increase in residual gas saturation can be used as an equivalent characterization of changes in bound water saturation. Therefore, While characterizing the phase permeability lag effect, it can also reflect the dynamic increasing trend of bound water saturation under multi-cycle injection and production conditions, and further affect the calculation of effective gas phase saturation. The specific steps for calculating the phase permeability lag coefficient include: Step 201: Based on the multi-cycle injection and production experiment, obtain the injection volume, production volume, water production, residual gas saturation, formation pressure at the end of injection, and formation pressure at the end of production for each cycle. Step 202: Construct a hysteresis model based on the gas injection volume, gas production volume, water production volume, residual gas saturation, formation pressure at the end of gas injection, and formation pressure at the end of gas production for each cycle. Step 203: Based on the hysteresis effect model, obtain the relative permeability hysteresis coefficient corresponding to each cycle. Dynamically update the residual gas evolution trend for subsequent injection-production cycles by combining the residual gas saturation and pressure change data of each cycle, and calculate the corresponding residual gas hysteresis coefficient for each cycle. value; The expression for the phase permeation hysteresis coefficient corresponding to each cycle is:

[0048] In the formula,

[0049] In the formula, Indicates the first i The phase permeation hysteresis coefficient corresponding to the period; Indicates the first i Periodic residual gas saturation; Indicates the first i -1 cycle residual gas saturation; This represents the weight of the pressure change in the i-th period in the cumulative pressure change over historical periods. Indicates the first i Formation pressure at the end of cyclic gas injection; Indicates the first i Formation pressure at the end of cyclic gas production m This indicates the current injection / production cycle.

[0050] For example, This represents the sum of the differences between the formation pressure at the end of the gas injection period and the formation pressure at the end of the gas production period corresponding to the first cycle, the difference between the formation pressure at the end of the gas injection period and the formation pressure at the end of the gas production period corresponding to the second cycle, and so on, up to the sum of the differences between the formation pressure at the end of the gas injection period and the formation pressure at the end of the gas production period corresponding to the m-th cycle.

[0051] For example, when the current injection-production cycle is 5, that is, the expression for the relative permeability lag coefficient corresponding to the 5th cycle is: .

[0052] Step 204: Based on the least squares method, calculate the phase permeation lag coefficients for all historical periods up to the i-th period. Construct a set of relative permeation hysteresis coefficients; based on the set of relative permeation hysteresis coefficients, establish an optimal function relating the relative permeation hysteresis coefficients to the period. By performing fitting calculations, the optimal phase permeation hysteresis coefficient corresponding to the i-th period is obtained. To achieve accurate calibration of the phase permeability hysteresis coefficient, among which, .

[0053] For example, a hysteresis model is used to describe the dynamic evolution of residual gas saturation. The residual gas saturation of the current period is composed of the residual gas saturation of the previous period and the residual gas increment driven by the pressure change amplitude of the current period. Since the pressure change amplitude of different periods will cause cumulative damage to rock pores, the influence of pressure change in each period on the current residual gas saturation has an additive effect. At the same time, the pressure change amplitudes of different periods are different, and their impact on reservoir fluid distribution and pore structure varies. Therefore, a weighting coefficient is constructed based on the pressure change amplitude of each period. By normalizing the pressure change amplitude, periods with larger pressure change amplitudes are given higher weights, achieving a reasonable allocation and quantitative characterization of the influence of pressure change amplitudes across multiple periods.

[0054] By extracting the injection volume, production volume, water production, and residual gas saturation for each cycle from multi-cycle injection-production experimental data, and based on the net difference between injection and production volumes and the residual gas saturation for each cycle, the dynamic variation law of the relative permeability lag coefficient with the injection-production cycle is clarified. This clarified relative permeability lag coefficient for each cycle serves as a key value for determining the evolution of residual gas saturation, driving the dynamic change of the gas storage capacity with the cycle.

[0055] The variation range and trend of the corrected residual gas saturation are determined by the optimal phase permeation hysteresis coefficient. Characterization.

[0056] The optimal phase permeation hysteresis coefficient for the current period is output by this method. It can quantitatively describe the dynamic evolution of reservoir seepage capacity under multi-cycle injection and production, providing key parameter basis for gas storage capacity prediction, injection and production system optimization and seepage mechanism research, and significantly improving the accuracy and reliability of multi-cycle injection and production simulation of water-invaded condensate gas reservoirs.

[0057] Determining the stress sensitivity coefficient includes: Step 201': Based on the multi-cycle injection and production experimental data, extract the measured core permeability data corresponding to each cycle; Step 202': Based on the measured permeability data of the core corresponding to each cycle, construct a fitted exponential decay model; quantify the decay law of permeability with cumulative pressure, and the model's coefficient of determination R²>0.98; For example, based on multi-cycle injection-production experimental data, core permeability measurement data is extracted systematically, and an exponential decay model is fitted to establish the multi-cycle cumulative permeability damage law. Simultaneously, in multi-cycle injection-production, pressure damage to the pore structure is cumulative and irreversible, requiring quantification of the total damage degree through continuous cycle superposition. Furthermore, the phase permeability hysteresis phenomenon caused by condensate oil precipitation and pore throat blockage exacerbates the damage—under the same pressure change, phase permeability hysteresis leads to a decrease in the effective pressure-bearing capacity of the pore structure, and the damage rate is significantly higher than in the ideal elastic deformation stage. Therefore, it is necessary to combine the phase permeability hysteresis coefficient... The damage model was modified by incorporating the phase penetration hysteresis effect into the permeability decay law, thereby achieving a precise characterization of both elastoplastic deformation and phase penetration hysteresis damage.

[0058] Step 203': Based on the fitted exponential decay model, determine the stress sensitivity coefficient corresponding to each period. The expression for the stress sensitivity coefficient is:

[0059] in,

[0060] Because the formation pressure increases from the formation pressure at the end of the previous cycle to the formation pressure at the end of the current cycle during the gas injection phase, the increased formation pressure reduces the effective stress, controlling pore opening. Furthermore, this stage coincides with the water body retreat phase; therefore, according to the... i Periodic injection end formation pressure , and passed i- Formation pressure at the end of one cycle Establish the pressure change range during the water recession stage and describe the stress sensitivity coefficient.

[0061] In the formula, Indicates the first i Cycle penetration rate; Indicates the initial penetration rate; Indicates the first i Periodic stress sensitivity coefficient; Indicates the first i The phase permeation hysteresis coefficient corresponding to the period; Indicates the first i The magnitude of pressure changes during the periodic receding water level; Indicates the first i Formation pressure at the end of the periodic injection; Indicates the first i -1 cycle of formation pressure at the end of mining; This indicates the degree to which the total penetration rate is maintained under the cumulative effect of the first i cycles; m This indicates the current injection / production cycle.

[0062] For example, when the current injection-production cycle m=5, the expression for the stress sensitivity coefficient is: .

[0063] Step 204': Based on the least squares method, construct a set of stress sensitivity coefficients for all historical periods up to the i-th period; establish an optimal function relating the stress sensitivity coefficients to the period based on the set of stress sensitivity coefficients. By performing fitting calculations, the optimal stress sensitivity coefficient for the i-th period is obtained. To achieve accurate calibration of the stress sensitivity coefficient, among which, = .

[0064] By fitting experimental data using the least squares method, the optimal stress sensitivity coefficient for each period was extracted from the modified permeability decay model. Compared to the traditional approach of treating permeability as a constant or a simple linear correction, this method uses an exponential decay model and multi-cycle experimental data fitting to accurately capture the stress-sensitive effect of reservoir rocks under long-term pressure alternation. Furthermore, it demonstrates a strong correlation between permeability and porosity changes, quantifying the physical nature of the irreversible decrease in permeability and porosity.

[0065] The stress sensitivity coefficient enables the subsequent water intrusion-seepage-pressure dynamic coupling model to accurately reflect the changes in reservoir permeability and porosity caused by repeated injection and production in the gas storage facility. This significantly improves the accuracy of long-term decline prediction of gas well productivity and the reliability of overall injection and production dynamic simulation of the gas storage facility, providing core parameter support for injection and production system optimization and dynamic capacity assessment.

[0066] Determining the water intrusion coefficient includes: Step 201: Based on the multi-cycle injection and production experimental data, extract the water intrusion, water receding, formation pressure at the end of gas injection, and formation pressure at the end of gas production for each cycle. Step 202: Based on the water intrusion, water retreat, formation pressure at the end of gas injection, and formation pressure at the end of gas production for each cycle, construct the dynamic response model of water intrusion and the dynamic response model of water retreat for each cycle. According to the amount of water intrusion Water Receding It can invert the relationship between water intrusion rate and pressure to achieve high-precision calibration of the dynamic response of water intrusion and water retreat with an error of <5%.

[0067] Step 203: Based on the dynamic response model of water intrusion for each cycle, determine the water intrusion coefficient for each cycle; based on the dynamic response model of water retreat for each cycle, determine the water retreat coefficient for each cycle.

[0068] The expression for the water erosion coefficient is: ; The expression for the water recession coefficient is: ; In the formula,

[0069]

[0070] In the formula, Indicates the first i Periodic water intrusion coefficient; Indicates the first i Periodic water intrusion volume; Indicates the first i Periodic water receding coefficient; Indicates the first i Periodic water receding volume; Indicates the first i Formation pressure at the end of cyclic gas injection; Indicates the first i Formation pressure at the end of cyclic gas production; Indicates the first i- Formation pressure at the end of a single gas production cycle; Indicates the first i Periodic water intrusion time; Indicates the first i Periodic water receding time; Indicates the first i The magnitude of pressure changes during the periodic flooding phase; Indicates the first i The magnitude of pressure changes during the cyclical water recession phase.

[0071] Step 204: Based on the least squares method, construct a set of water intrusion coefficients and a set of water retreat coefficients for all historical periods up to the i-th period; based on the sets of water intrusion coefficients and water retreat coefficients, establish optimal functions related to the periods for the sets of water intrusion coefficients and water retreat coefficients respectively. , By performing fitting calculations, the optimal value of the water intrusion coefficient for the i-th period is obtained. Optimal value of water recession coefficient .

[0072] Based on the established dynamic response model for water intrusion and regression, the parameters obtained through inversion calculation are directly output. , This parameter is the final water intrusion and retreat coefficient. This coefficient is not a theoretical estimate, but a dynamic parameter calibrated through long core injection-production simulation experiments. It accurately reflects the true response characteristics of the water body in the gas reservoir under multi-cycle high-speed injection-production conditions, transforming the process from theoretical estimations based on static geological parameters or macroscopic models based on historical fitting to direct calibration through dynamic physical experiments. The obtained water intrusion and retreat coefficients accurately reflect the nonlinear response and hysteresis effect of water intrusion under rapid pressure fluctuations, which is crucial for water-intruded gas reservoirs. Applying this high-precision water intrusion coefficient to the reservoir capacity prediction model can significantly improve the accuracy of predicting the amount of water compressed and retreated during gas injection and the amount of water intrusion during gas production, more accurately assessing the effective working gas volume of the gas reservoir and preventing engineering risks such as edge water inrush.

[0073] In a specific embodiment of the present invention, step 3, based on the phase permeation hysteresis coefficient, stress sensitivity coefficient, water intrusion coefficient, and water retreat coefficient corresponding to each cycle, constructs a dynamic coupling model of water intrusion-seepage-pressure, including: Based on the i-th period, for all periods, construct the set of relative permeation hysteresis coefficient, stress sensitivity coefficient, water intrusion coefficient, and water retreat coefficient corresponding to the i-th period; By processing multi-cycle injection-production experimental data, the relative permeability lag coefficient, stress sensitivity coefficient, water intrusion coefficient, and water regression coefficient corresponding to each cycle are determined, and these four coefficients corresponding to each cycle are combined to generate a dynamic parameter set; for example, the relative permeability lag coefficient set {α1, α2, ..., α3} for the i-th cycle is... i}, the set of stress sensitivity coefficients {γ1, γ2, ..., γ} during the i-th period i}, the set of water intrusion coefficients {C} during the i-th period e (1) C e (2) ...C e (i)}, the water retreat coefficient {C} in the i-th period t (1) C t (1) ...C t (1)}, Based on the least squares method, for the i-th period, the set of phase hysteresis coefficients {α1, α2, ..., α3} is obtained. i}, the set of stress sensitivity coefficients {γ1, γ2, ..., γ} during the i-th period i}, the set of water intrusion coefficients {C} during the i-th period e (1) C e (2) ...C e (i)}, the water retreat coefficient {C} in the i-th period t (1) C t (1) ...C t (1)} Perform fitting calculations respectively to obtain the optimal phase permeation hysteresis coefficient, optimal stress sensitivity coefficient, optimal water intrusion coefficient, and optimal water retreat coefficient for the i-th period; For example, based on the fact that the fluid distribution was not stable in the early stage of injection and production, the residual gas, bound water, and condensate oil were unstablely distributed in the pore space. The complex fluid occupied the space, hindering the migration of injected gas and formation water, resulting in an increase in the water intrusion coefficient, water retreat coefficient, pore space decay, and bound fluid saturation. Based on the corresponding experimental data, a power function model was selected to specifically describe the evolution trend. The water production, gas production, water intrusion coefficient, and water retreat coefficient were obtained from each cycle of experiments. The experimental phase permeability lag coefficient, stress sensitivity coefficient, and water intrusion and water retreat coefficient were calculated. As the experimental cycle increased, the optimal phase permeability lag coefficient, optimal stress sensitivity coefficient, optimal water intrusion coefficient, and optimal water retreat coefficient for each cycle under the injection and production cycle were dynamically fitted.

[0074] To investigate the evolution trends of the relative permeability hysteresis coefficient, stress sensitivity coefficient, water intrusion coefficient, and water regression coefficient, a power function model was fitted using the least squares method. The power function model is as follows:

[0075] In the formula: SThese are the optimization parameter values ​​that need to be fitted, such as phase penetration hysteresis coefficient, stress sensitivity coefficient, water intrusion coefficient, and water retreat coefficient. a、b These are the fitting parameters; T It is a periodicity.

[0076] Based on the coefficient values ​​obtained from the previous experimental cycle, and combined with the operating cycle as the independent variable, the optimized coefficients of the dependent variable are predicted. The error is calculated based on the current predicted relationship and the new independent variable. If the current parameters are relatively certain, the error is small, and the correction magnitude is small; if the parameters are unstable, the error is large, and the correction magnitude is also large. The information matrix is ​​updated using a recursive formula, gradually updating the fitted parameter values ​​to reduce the error. This process repeats cyclically with the increase of experimental cycles. Each time data from a new cycle is added, the cumulative sum of squared residuals is minimized again based on the original data, achieving iterative least-squares fitting with continuously increasing cycles.

[0077] Based on the optimal relative permeability hysteresis coefficient, optimal stress sensitivity coefficient, optimal water intrusion coefficient, optimal water retreat coefficient, and the first injection-production data corresponding to the i-th cycle, a dynamic coupling model of water intrusion-seepage-pressure is constructed; whereby... When the difference between the storage capacity of period i and period i-1 is ≤3%, the difference between the storage capacity of period i-1 and period i-2 is ≤3%, and the relevant parameters of condensate oil for periods i, i-1, and i-2 are... Content fluctuation ≤1%. For example, based on long core injection-production simulation experiments, in the initial stage of injection-production, the gas storage space is disturbed by water and occupied by condensate components, preventing stable equilibrium development. When the storage capacity evolves to within a stable threshold, and the condensate-related physical properties no longer fluctuate significantly, it can be determined that the fluid seepage and phase change processes in the rock pores have completely left the non-equilibrium disturbance stage of depletion development and the initial operation phase, entering a stable cyclic injection-production dynamic equilibrium state. The difference in storage capacity ≤3% and condensate-related parameters... A content fluctuation of ≤1% indicates that the fluid in the gas storage space is in a stable stage.

[0078] For example, a dynamic parameter set is input into a numerical simulation framework to build and run a dynamic coupling model of water intrusion-seepage-pressure, and the reservoir capacity prediction result is output based on the running results of the dynamic coupling model of water intrusion-seepage-pressure.

[0079] Based on experimental results , , , The optimal functional relationship related to the cycle is fitted using the least squares method to predict the future cycle evolution. The predicted cycle is obtained by calculating the predicted cycle using a dynamic coupling model of water intrusion-seepage-pressure. i Forecast value of cycle storage capacity; The criteria for terminating multi-cycle high-speed injection-production experiments or determining the dynamic coupling model of water intrusion-seepage-pressure during the construction of the water intrusion-seepage-pressure model are as follows: The rate of change in storage capacity is ≤3% for two consecutive periods; that is, the difference between the storage capacity in period i and period i-1 is ≤3%, and the difference between the storage capacity in period i-1 and period i-2 is ≤3%. The bound water saturation tends to stabilize for two consecutive periods; that is, the bound water saturation tends to stabilize in the i-th period and the (i-1)-th period. Water erosion coefficient Water Retreat Coefficient The operating pressure variation trend tends to stabilize over several consecutive cycles; that is, the water intrusion coefficient in the i-th cycle, the (i-1)-th cycle, and the (i-2)-th cycle. Water Retreat Coefficient The relative change rate of operating pressure is less than 1%; Under conditions of phase change, relevant parameters of condensate oil The content fluctuation is ≤1% for three consecutive periods (e.g., the i-th period, the (i-1)-th period, and the (i-2)-th period); Total injection and extraction cycles ≥ 5 times.

[0080] Based on indoor experimental studies, an injection-production cycle of more than 5 times can stabilize the evolution of bound fluid saturation, pore space compression, and condensate liquid occupancy within the pore space, ensuring the stability of the gas storage space and thus stabilizing the reservoir capacity prediction.

[0081] In a specific embodiment of the present invention, the expression of the water intrusion-seepage-pressure dynamic coupling model is as follows:

[0082] In the formula, Among them, water intrusion and water retreat are affected by the complex fluid distribution under the injection and production cycle, and the net water intrusion fluctuates in the initial stage of injection and production. Therefore, by optimizing the water intrusion coefficient and water retreat coefficient, the dynamics of water intrusion can be accurately grasped.

[0083]

[0084] oil saturation The bound water saturation was obtained through PVT experiments combined with gas chromatography monitoring and inversion. Calculated using the formula:

[0085] Based on the high accuracy of long core experiments in obtaining measured water intrusion, water recession, and water intake for each cycle, the actual experimental values ​​are used to determine the bound water saturation. An evaluation was conducted, but no optimization fitting was performed.

[0086] In the formula, Indicates the first i Periodic storage capacity; This represents the total pore volume of the core. Indicates the first i Periodic residual gas saturation; Indicates the first i Periodically bound water saturation; Indicates the first i Oil saturation during the cycle; Indicates the first i Formation pressure at the end of cyclic gas injection; Indicates the first i Formation pressure at the end of cyclic gas production; Indicates the first i The optimal stress sensitivity coefficient corresponding to the period; Indicates the foundation i The optimal hysteresis coefficient corresponding to the period; Indicates the first i Cycle water intrusion volume; Indicates the upper limit pressure gas volume coefficient; Indicates the first i Periodic water intrusion time; Indicates the first i Periodic water receding time; Indicates the first i The magnitude of pressure changes during the periodic flooding phase; Indicates the first i The magnitude of pressure changes during the periodic receding water level; Indicates the first i The optimal water intrusion coefficient corresponding to the period; Indicates the first i Periodic water intrusion volume; Indicates the first i The optimal water receding coefficient corresponding to the cycle; Indicates the first i Periodic water receding volume; Indicates the first i Cycle water production.

[0087] Based on experimental results , , , The optimal functional relationship related to the cycle was fitted by the least squares method to predict the future cycle evolution. Based on the fact that the fluid was not stably distributed in the early stage of injection and production, the residual gas, bound water and condensate oil were unstablely distributed in the pore space. The complex fluid occupied the space, which hindered the migration of injected gas and formation water, resulting in water intrusion and retreat, pore space decay and increased saturation of bound fluid. Based on the corresponding experimental data, a power function model was selected to specifically describe the evolution trend. The predicted reservoir capacity of the i-th cycle was obtained by calculating the dynamic coupling model of water intrusion-seepage-pressure. Since the pore space of the rock and the saturation of the residual gas in the bound water change periodically with the increase of the injection and production cycle, the results can be made closer to the actual operating state by increasing the number of injection and production cycles, thereby increasing the reliability of experimental results and reservoir capacity prediction.

[0088] In a specific embodiment of the present invention, the coupling mechanism between dynamic parameters and the mass balance equation is as follows: The effects of the phase permeation hysteresis coefficient, stress sensitivity coefficient, and water intrusion coefficient on the mass balance equation were analyzed to establish the mechanistic relationship between the dynamic parameters of multi-cycle injection and production and the working gas volume of the gas storage facility.

[0089] During multi-cycle injection and production, the precipitation of condensate oil and the redistribution of the aqueous phase occupy and block the pore-throat structure, leading to an increase in the volume of immobile fluid. This pore-throat occupation and blockage restricts gas flow and also causes some of the aqueous phase to become bound. This is further compounded by the residual gas saturation and the phase permeability hysteresis coefficient. The relationship between them can realize the mapping of the phase permeation hysteresis effect to the bound water saturation.

[0090] During multi-cycle injection and production, the reservoir rock skeleton undergoes elastoplastic deformation under repeated changes in effective stress, leading to pore throat radius contraction and reduced connectivity, thus causing a continuous decline in permeability. According to porous media theory, there is a correlation between permeability and porosity; compression of the pore structure not only affects flow capacity but also reduces pore volume. Therefore, the stress sensitivity coefficient... While characterizing permeability changes, it can further reflect the dynamic evolution of reservoir porosity under pressure alternation, thereby affecting the effective gas storage space of the reservoir.

[0091] During the operation of water-inundated gas storage facilities, water intrusion and receding driven by pressure differential are crucial factors affecting storage capacity changes. According to the principle of mass balance, water intrusion is essentially the volume of water replenished by the pressure differential, and a functional relationship can be established between water intrusion and pressure differential. Therefore, the water intrusion coefficient... Water Retreat Coefficient It can serve as a characterization parameter of the functional relationship, used to describe the dynamic process of water intrusion and water retreat, and can be further transformed into the water intrusion term in the mass balance equation, realizing the mechanistic expression of the dynamic behavior of water intrusion.

[0092] Based on the above analysis, the phase permeation hysteresis coefficient Stress sensitivity coefficient and water intrusion coefficient Water Retreat Coefficient The dynamic changes of the reservoir are characterized from three aspects: fluid distribution, pore structure evolution, and water recharge. The relationship between the coefficients and the optimal periodic function is fitted using the least squares method to predict the corrected optimal coefficients for each future operating cycle. These coefficients can be mapped to the saturation, porosity, and water intrusion terms in the mass balance equation, respectively, thus achieving a mechanistic coupling calculation of the gas storage capacity under multi-cycle injection and production conditions. Based on the expression of the above-mentioned water intrusion-seepage-pressure dynamic coupling model, the evolution of stable storage capacity is quantitatively described by optimizing the immovable pore space.

[0093] In a specific embodiment of the present invention, the multi-cycle injection-production coordinated control process is as follows: Injection stage: Inject dry gas to restore the pressure to 5 MPa above the dew point pressure, control the intensity of water intrusion, and suppress the phenomenon of anti-condensation under the condition of phase change; Equilibrium phase: Maintain pressure balance, monitor pressure decay curve, and invert stress sensitivity coefficient under water intrusion control conditions; Gas extraction stage: control the pressure drop rate ≤ 0.3 MPa / h, and calculate the working gas volume per cycle based on the material balance equation.

[0094] The core technological innovation of this invention lies in: 1) Full-cycle time-varying effect capture: The cumulative effect of stress sensitivity and phase permeability lag is quantified through long core multi-cycle injection and production experiments (≥10 times), which solves the defect of traditional methods with less than 5 simulation cycles and improves the accuracy of reservoir capacity prediction. The computer is used to process the multi-dimensional data obtained in real time in the multi-cycle injection and production experiments, and the parameter set that can reflect the dynamic changes of the reservoir is generated and integrated into the numerical model. This overcomes the model distortion problem caused by the use of static or simplified parameters in traditional methods, so that the calculation results of the model can closely approximate the actual operating state of the gas storage. 2) A method for inverting dynamic parameters of water intrusion based on multi-cycle injection and production experiments is proposed to realize the quantitative characterization of the nonlinear response of water intrusion, construct a multi-field coupled reservoir capacity simulation model controlled by water intrusion, and introduce the relative permeability hysteresis coefficient, stress sensitivity coefficient, water intrusion coefficient, and water retreat coefficient as dynamic parameters to improve the prediction accuracy under complex working conditions. 3) Through forward-looking risk warning calculations, the safety and stability of the gas storage facility in the long term are enhanced. By using a coupled model to extrapolate and calculate future operating conditions, it is possible to predict in advance the engineering risks that may be caused by the cumulative damage to reservoir properties or the aggravation of water intrusion. This shift from passive monitoring to proactive prediction provides a basis for decision-making to take preventive measures, effectively shortens the risk response time, and ensures the integrity of the gas storage facility assets and the continuity of operation.

[0095] This invention also provides a device for dynamic prediction of multi-cycle injection-production capacity of water-inundated gas reservoirs, the device comprising, The acquisition unit is used to acquire data from each cycle of injection and extraction experiments based on multi-cycle injection and extraction experiments. The determination unit is used to determine the relative permeability hysteresis coefficient, stress sensitivity coefficient, water intrusion coefficient, and water retreat coefficient for each cycle based on the injection and production data of each cycle. The building blocks are used to construct a dynamic coupling model of water intrusion-seepage-pressure based on the phase permeation hysteresis coefficient, stress sensitivity coefficient, water intrusion coefficient, and water retreat coefficient corresponding to each cycle. The prediction unit is used to predict the storage capacity of water-inundated gas reservoirs based on the dynamic coupling model of water intrusion-seepage-pressure.

[0096] Regarding the apparatus in the above embodiments, the specific manner in which each unit performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0097] Based on the above disclosure, the present invention also provides an electronic device. For example... Figure 3 As shown, the electronic device of this disclosure includes at least one processor electrically connected to the present invention and at least one memory electrically connected to the processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method steps as executed by the controller above.

[0098] An embodiment of the present invention also provides a storable medium storing computer instructions, which, when executed by a processor, are specifically executed according to the steps in the method described in the above embodiment.

[0099] An embodiment of the present invention also provides a computer program product, including computer instructions, which, when executed by a processor, specifically follow the steps in the method described in the above embodiment.

[0100] The technical solution of the present invention will be further described below with reference to specific embodiments.

[0101] To verify the feasibility of this invention in practice, a simulation was applied to the dynamic assessment of storage capacity in a deep high-pressure water-intruded gas reservoir conversion project. The challenge of this project is that during the multi-cycle high-speed injection and production process, the reservoir is subject to active water intrusion, complex condensate oil phase changes, and strong rock stress sensitivity. Traditional simulation methods use static parameters, which leads to huge deviations in the prediction of the effective working gas volume for long-term operation of the gas storage facility, making it difficult to guide operation.

[0102] In this embodiment, representative long core samples are first obtained from the gas reservoir for basic physical property determination. These samples are then placed in a long core holder integrating a high-precision pressure sensor, permeability measuring device, and online gas chromatograph. Subsequently, the following methods are employed... Figure 2 The apparatus shown was used to initiate a multi-cycle injection-production experiment comprising 20 complete cycles. Each cycle was strictly executed in the sequence of three stages: gas injection, balancing, and gas production. During the experiment, initial formation pressure, formation pressure at the end of gas injection, formation pressure at the end of gas production, gas injection rate, gas production rate, water production rate, water intrusion rate, water retreat rate, bound water saturation, oil saturation, residual gas saturation, total core pore volume, and outlet gas were continuously collected and processed. The data, including content and other dimensions, formed a high-precision multi-cycle injection and extraction experimental dataset.

[0103] Based on this dataset, dynamic parameters were calculated. First, using a hysteresis model, the increment of residual gas saturation over multiple cycles was calculated from the injection volume, production volume, water production, residual gas saturation, formation pressure at the end of injection, and formation pressure at the end of production for each cycle. Based on this, the residual gas evolution trend was dynamically updated, and the relative permeability hysteresis coefficient for the final experimental cycle was calculated. The value is 0.12, and the corrected residual gas saturation is as follows: Figure 4 As shown in the figure, the dynamic parameters of residual gas saturation after correction in cycles 1-3 are unstable, only stabilizing after cycle 4. Secondly, core permeability data were obtained simultaneously using a permeability measuring device, and these data were fitted to a fitted exponential decay model to analyze the stress sensitivity coefficient. 0.0075 MPa -1 The permeability loss and changes after correction and inversion are as follows: Figure 5 As shown, finally, the net water intrusion volume was calculated based on the cumulative difference between injection and production during the experiment, and the corresponding reservoir pressure drop was correlated. A dynamic response model for water intrusion was established through regression analysis, and the water intrusion coefficient was solved. The water receding coefficient is 4.8 m³ / MPa. The value is 4.6 m³ / MPa. These four coefficients constitute the core set of dynamic parameters describing the dynamic characteristics of this reservoir.

[0104] Subsequently, it will include , , , The dynamic parameter set was input into a nonlinear grid-based material balance numerical simulation framework within the numerical simulation platform to construct a dynamic coupled model of water intrusion-seepage-pressure. In the initial stage of model construction, experimental data from the first five cycles were used for playback verification. The optimal values ​​of the dynamic parameters obtained through five multi-cycle injection-production experiments combined with the least squares method were used to predict the reservoir capacity for the fifth cycle. This prediction was compared with the reservoir capacity predicted by the experimentally calculated dynamic parameters obtained through five multi-cycle injection-production experiments. The prediction error, calculated using the relative error formula, was 9.2%, exceeding the 5% engineering accuracy requirement. Based on the initial parameter inversion, the parameters were iteratively optimized according to the error between the model prediction results and the experimental data. By making small perturbations to the relative permeability hysteresis coefficient, stress sensitivity coefficient, and water intrusion coefficient as dynamic parameters, and recalculating the model response, the prediction error was gradually reduced. After two rounds of iterative optimization, the error between the model calculation results and the experimental data was brought to a preset range. The optimal values ​​of the dynamic parameters obtained by combining the five multi-cycle injection-production experiments after the above iterative optimization with the least squares method were used to predict the reservoir capacity of the fifth cycle. The prediction of the reservoir capacity of the fifth cycle was compared with the prediction of the experimental dynamic parameters obtained by the five multi-cycle injection-production cycle experiments. According to the relative error formula, the prediction error was reduced to 3.7%, which verified the accuracy of the water intrusion-seepage-pressure dynamic coupling model.

[0105] During the 12th experimental cycle, the online gas chromatograph detected [something] in the produced gas. The content showed a decreasing trend of 2.5%. Based on the prediction results of the coupled model and combined with this real-time data, the system generated an optimized injection and production plan: increasing the target value of the injection pressure for the next cycle by 8 MPa and extending the equilibrium time by 6 hours. Simultaneously, the model proactively calculated that the optimal pressure drop rate should be controlled at 0.2 MPa / h. After implementing this optimized plan, in the 13th cycle, The content successfully rebounded by 1.8%, effectively improving the recovery rate of condensate oil.

[0106] Furthermore, when simulating future operating conditions, the system used a dynamic parameter set to predict the operational risks of the 18th cycle. The prediction results showed that during the high-intensity gas production phase of this cycle, the reservoir permeability decay rate would reach 11.5%, and the water intrusion rate would reach 2.1 m³ / h, both exceeding the preset safety thresholds of 10% and 2.0 m³ / h, respectively. The system then output a structured risk warning signal, prompting operators to adjust the gas production plan for this cycle to avoid irreversible reservoir damage and well water flooding risks.

[0107] The comparison between the obtained reservoir capacity prediction results and the traditional method (using static parameters) shows a significant difference. The coupled model of this invention predicts an effective working gas volume of 85.6 × 10⁻⁶ after a 10-year operating period.8 m³, while the traditional model predicts a value of 98.2 × 10⁻⁶. 8 The deviation was as high as 14.7% in m³, and the deviation increased with the extension of operating time. This demonstrates the superiority of the present invention in handling complex coupling effects and provides a reliable basis for the scientific planning and efficient operation of the gas storage facility.

[0108] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for dynamic prediction of multi-cycle injection-production capacity of a water-inundated gas reservoir, characterized in that, The method includes, Based on multi-cycle injection and extraction experiments, data from each cycle of injection and extraction experiments were obtained. Based on the injection and production experimental data of each cycle, the relative permeability hysteresis coefficient, stress sensitivity coefficient, water intrusion coefficient, and water retreat coefficient corresponding to each cycle were determined. Based on the phase permeation hysteresis coefficient, stress sensitivity coefficient, water intrusion coefficient, and water retreat coefficient corresponding to each cycle, a dynamic coupling model of water intrusion-seepage-pressure is constructed. Based on the aforementioned dynamic coupling model of water intrusion-seepage-pressure, the storage capacity of water-intrusion gas reservoirs is predicted. Based on the relative permeability hysteresis coefficient, stress sensitivity coefficient, water intrusion coefficient, and water regression coefficient corresponding to each cycle, a dynamic coupling model of water intrusion-seepage-pressure is constructed, including: Based on the relative permeation lag coefficient, stress sensitivity coefficient, water intrusion coefficient, and water retreat coefficient corresponding to all historical cycles at the i-th cycle, construct the relative permeation lag coefficient set, stress sensitivity coefficient set, water intrusion coefficient set, and water retreat coefficient set corresponding to the i-th cycle. Based on the least squares method, the set of relative permeation hysteresis coefficients, stress sensitivity coefficients, water intrusion coefficients and water retreat coefficients corresponding to the i-th period are fitted and calculated respectively to obtain the optimal relative permeation hysteresis coefficient, optimal stress sensitivity coefficient, optimal water intrusion coefficient and optimal water retreat coefficient corresponding to the i-th period. Based on the optimal phase permeation hysteresis coefficient, optimal stress sensitivity coefficient, optimal water intrusion coefficient, optimal water retreat coefficient, and the first injection-production data corresponding to the i-th period, a dynamic coupling model of water intrusion-seepage-pressure is constructed.

2. The method for dynamic prediction of multi-cycle injection-production capacity of water-inundated gas reservoirs according to claim 1, characterized in that, The injection-production experimental data for each cycle include initial formation pressure, formation pressure at the end of gas injection, formation pressure at the end of gas production, gas injection rate, gas production rate, water production rate, water intrusion rate, water retreat rate, total core pore volume, and the first injection-production data. The first injection and production data includes residual gas saturation, bound water saturation, oil saturation, pressure variation, net water intrusion, and upper pressure gas volume coefficient.

3. The method for dynamic prediction of multi-cycle injection-production capacity of water-inundated gas reservoirs according to claim 1, characterized in that, The expression for the dynamic coupling model of water intrusion-seepage-pressure is: in, In the formula, Indicates the first i Periodic storage capacity; This represents the total pore volume of the core. Indicates the first i Periodic residual gas saturation; Indicates the first i Periodically bound water saturation; Indicates the first i Oil saturation during the cycle; This represents the optimal stress sensitivity coefficient corresponding to the i-th period; Indicates the first i The optimal phase permeation hysteresis coefficient corresponding to the period; Indicates the first i Cycle water intrusion volume; Indicates the upper limit pressure gas volume coefficient; Indicates the first i Periodic water intrusion time; Indicates the first i Periodic water receding time; Indicates the first i Formation pressure at the end of cyclic gas injection; Indicates the first i Formation pressure at the end of cyclic gas production; Indicates the first i The magnitude of pressure changes during the periodic flooding phase; Indicates the first i The magnitude of pressure changes during the periodic receding water level; It means that the first i The optimal water intrusion coefficient corresponding to the period; Indicates the first i The optimal water receding coefficient corresponding to the cycle; Indicates the first i Periodic water intrusion volume; Indicates the first i Periodic water receding volume; Indicates the first i Cycle water production; m This indicates the current injection / production cycle.

4. The method for dynamic prediction of multi-cycle injection-production capacity of water-inundated gas reservoirs according to any one of claims 1-3, characterized in that, Determining the phase permeability hysteresis coefficient includes: Based on multi-cycle injection and production experiments, the injection volume, production volume, water production, residual gas saturation, formation pressure at the end of injection, and formation pressure at the end of production were obtained for each cycle. A hysteresis model was constructed based on the gas injection volume, gas production volume, water production volume, residual gas saturation, formation pressure at the end of gas injection, and formation pressure at the end of gas production for each cycle. Based on the hysteresis effect model, the phase permeation hysteresis coefficient corresponding to each cycle is obtained.

5. The method for dynamic prediction of multi-cycle injection-production capacity of water-inundated gas reservoirs according to claim 4, characterized in that, The expression for the phase permeation hysteresis coefficient corresponding to each cycle is: In the formula, Indicates the first i The phase permeation hysteresis coefficient corresponding to the period; Indicates the first i Periodic residual gas saturation; Indicates the first i -1 cycle residual gas saturation; Indicates the first i The weight of cyclical pressure changes in historical cyclical cumulative pressure changes; Indicates the first i Formation pressure at the end of cyclic gas injection; Indicates the first i Formation pressure at the end of cyclic gas production; m This indicates the current injection / production cycle.

6. The method for dynamic prediction of multi-cycle injection-production capacity of water-inundated gas reservoirs according to any one of claims 1-3, characterized in that, Determine the stress sensitivity coefficients for each cycle, including: Based on multi-cycle injection and production experimental data, core permeability measurement data, formation pressure data at the end of gas injection, and formation pressure data at the end of gas production were extracted for each cycle. Based on the measured core permeability data for each cycle, a fitted exponential decay model was constructed. Based on the fitted exponential decay model, the stress sensitivity coefficient corresponding to each period is determined.

7. The method for dynamic prediction of multi-cycle injection-production capacity of water-inundated gas reservoirs according to claim 6, characterized in that, The expression for the stress sensitivity coefficient corresponding to each period is: In the formula, Indicates the first i Cycle penetration rate; Indicates the initial penetration rate; Indicates the first i Periodic stress sensitivity coefficient; Indicates the first i The phase permeation hysteresis coefficient corresponding to the period; Indicates the first i The magnitude of pressure changes during the periodic receding water level; m This indicates the current injection / production cycle.

8. The method for dynamic prediction of multi-cycle injection-production capacity of water-inundated gas reservoirs according to any one of claims 1-3, characterized in that, Determine the flooding and recession coefficients for each cycle, including: Based on multi-cycle injection and production experimental data, the water intrusion, water receding, formation pressure at the end of gas injection, and formation pressure at the end of gas production were extracted for each cycle. Based on the water intrusion, water retreat, formation pressure at the end of gas injection, and formation pressure at the end of gas production for each cycle, dynamic response models for water intrusion and water retreat for each cycle are constructed. Based on the dynamic response model of water intrusion for each cycle, the water intrusion coefficient for each cycle is determined. Based on the dynamic response model of water recession corresponding to each cycle, the water recession coefficient corresponding to each cycle is determined.

9. The method for dynamic prediction of multi-cycle injection-production capacity of water-inundated gas reservoirs according to claim 8, characterized in that, The expressions for the water intrusion coefficients corresponding to each cycle are as follows: The expression for the water recession coefficient is: In the formula, In the formula, Indicates the first i Periodic water intrusion coefficient; Indicates the first i Periodic water intrusion volume; Indicates the first i Periodic water receding coefficient; Indicates the first i Periodic water receding volume; Indicates the first i Formation pressure at the end of cyclic gas injection; Indicates the first i Formation pressure at the end of cyclic gas production; Indicates the first i- Formation pressure at the end of a single gas production cycle; Indicates the first i Periodic water intrusion time; Indicates the first i Periodic water receding time; Indicates the first i The magnitude of pressure changes during the periodic flooding phase; Indicates the first i The magnitude of pressure changes during the cyclical water recession phase.

10. A device for dynamic prediction of multi-cycle injection-production capacity of a water-inundated gas reservoir, characterized in that, The device includes, The acquisition unit is used to acquire data from each cycle of injection and extraction experiments based on multi-cycle injection and extraction experiments. The determination unit is used to determine the relative permeability hysteresis coefficient, stress sensitivity coefficient, water intrusion coefficient, and water retreat coefficient for each cycle based on the injection and production experimental data of each cycle. The building blocks are used to construct a dynamic coupling model of water intrusion-seepage-pressure based on the relative permeability hysteresis coefficient, stress sensitivity coefficient, water intrusion coefficient, and water regression coefficient corresponding to each cycle, including: Based on the relative permeation hysteresis coefficient, stress sensitivity coefficient, water intrusion coefficient, and water retreat coefficient corresponding to all cycles at the i-th cycle, construct the relative permeation hysteresis coefficient set, stress sensitivity coefficient set, water intrusion coefficient set, and water retreat coefficient set corresponding to the i-th cycle. Based on the least squares method, the set of relative permeation hysteresis coefficients, stress sensitivity coefficients, water intrusion coefficients and water retreat coefficients corresponding to the i-th period are fitted and calculated respectively to obtain the optimal relative permeation hysteresis coefficient, optimal stress sensitivity coefficient, optimal water intrusion coefficient and optimal water retreat coefficient corresponding to the i-th period. Based on the optimal phase permeation hysteresis coefficient, optimal stress sensitivity coefficient, optimal water intrusion coefficient, optimal water retreat coefficient, and first injection-production data corresponding to the i-th cycle, a dynamic coupling model of water intrusion-seepage-pressure is constructed. The prediction unit is used to predict the storage capacity of water-inundated gas reservoirs based on the dynamic coupling model of water intrusion-seepage-pressure.

11. An electronic device comprising at least one processor and at least one memory, the memory being data-connected to the processor, wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-9.

12. A computer-storable medium, characterized in that, The storable medium stores computer instructions, which, when executed by a processor, specifically perform the steps of the method as described in any one of claims 1-9.

13. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they specifically perform the steps in the method as described in any one of claims 1-9.

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