A method for predicting dynamic evolution of effective reservoir capacity boundary of low-permeability reservoir underground gas storage under multi-cycle injection-production conditions

Abstract

The application discloses a kind of low permeability reservoir underground gas storage multi-cycle injection-production condition under the prediction method of effective reservoir capacity boundary dynamic evolution, and the application is based on injection-production intensity driving seepage parameter self-adaptive evolution under the condition of low permeability reservoir underground gas storage multi-cycle strong injection and strong production, and the effective reservoir capacity boundary is dynamically predicted, to solve the systematic overestimation problem caused by parameter static, threshold effect neglect and connectivity loss in the effective reservoir capacity boundary prediction of low permeability reservoir underground gas storage under the condition of multi-cycle strong injection and strong production. Realize: the effective reservoir capacity boundary of injection stage and production stage is predicted respectively;Seepage parameter self-adaptive evolution with cycle and injection-production system intensity, not dependent on field testing / monitoring data;Through threshold-conductivity-connectivity accessibility triple criterion, the effective reservoir capacity boundary meeting engineering definition is obtained.

Description

Technical Field

[0001] This invention relates to the field of underground gas storage engineering and dynamic evaluation technology of low-permeability multiphase flow, and in particular to a prediction method for the dynamic evolution of the effective storage capacity boundary of an underground gas storage facility in a low-permeability reservoir under multi-cycle injection and production conditions. Background Technology

[0002] The effective storage capacity of an underground gas storage facility refers to the effective storage space that can be injected and extracted within a specified operating pressure range and can form a stable injection and extraction capacity. In engineering, the effective storage capacity is usually described by the effective storage capacity boundary, which is the outer envelope boundary of the point set in the reservoir that satisfies the requirements for injection and extraction.

[0003] Existing methods for predicting effective reservoir capacity boundaries mostly employ the following assumptions or processing methods: (1) Using fixed permeability and fixed relative permeability curves, we ignore the relative permeability degradation and hysteresis caused by multi-cycle injection and production; (2) The threshold effect of the starting pressure gradient is ignored or simplified, and the nonlinear change of the threshold with liquid saturation and permeability is not included in the boundary determination. (3) In numerical simulation, static phase permeability or only empirical scaling is used, and there is a lack of parameter update mechanism that couples the cumulative damage of the cycle with the strength of the injection-production regime. (4) The effective reservoir capacity boundary is usually determined based on local thresholds, which can easily create isolated island areas that are not connected to the well, which is inconsistent with the engineering definition that injectable and extractable areas must be connected and reachable.

[0004] Under multi-cycle intensive injection and production conditions, low-permeability underground gas storage facilities experience a cumulative increase in fluid trapping, jamming, and interfacial resistance at the pore scale. This manifests as an overall inward contraction of the relative permeability curve, a narrowing of the co-permeability zone, a decrease in endpoint permeability, and an increase in the saturation of immovable fluids. Simultaneously, a starting pressure gradient threshold is prevalent under low-permeability conditions, increasing with increasing liquid saturation and decreasing exponentially with permeability. These effects collectively lead to a dynamic contraction of the effective storage capacity boundary over the cycle. If fixed parameters are still used for prediction, the effective storage capacity and injection / production capacity in the later stages will be systematically overestimated, affecting the injection-production pressure differential window and operational risk control.

[0005] Therefore, there is a need for a prediction method that can be implemented even without on-site monitoring data and can establish a closed loop between multi-cycle cumulative damage, injection-production regime intensity, dynamic degradation of seepage parameters, and shrinkage of effective reservoir capacity boundaries. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a prediction method for the dynamic evolution of the effective storage capacity boundary under multi-cycle injection and production conditions in low-permeability underground gas storage tanks. This method enables the prediction of the effective storage capacity boundary for the gas injection stage and the gas production stage, and the seepage parameters adaptively evolve with the cycle and the intensity of the injection and production regime, without relying on field well test / monitoring data.

[0007] The objective of this invention is achieved as follows: A method for predicting the dynamic evolution of the effective storage capacity boundary of an underground gas storage facility in a low-permeability reservoir under multi-cycle injection and production conditions includes the following steps: S1: Establish a gas storage model and input the static parameters of the reservoir and the parameters of the multi-cycle injection and production regime; S2: Calculate the injection-production intensity factor for each cycle based on the injection-production system parameters, and calculate the cumulative intensity based on the injection-production intensity factor for each cycle; S3: Based on the calibration parameters of the multi-cycle relative permeability experiment, establish the relative permeability degradation factor and the movable space factor, and use the cumulative intensity of the injection-production intensity factor to adaptively update the relative permeability degradation factor and the movable space factor, wherein the relative permeability degradation factor distinguishes between the gas injection stage and the gas production stage. S4: Establish a start-up pressure gradient threshold model and adaptively update the start-up pressure gradient threshold using the cumulative intensity of the injection-production intensity factor. S5: Calculate the pressure gradient field and the effective conductivity index during the gas injection or gas production phase of the nth cycle. S6: Determine the candidate effective reservoir capacity point set based on the threshold criterion and the diversion criterion, and perform connectivity reachability screening on the candidate effective reservoir capacity point set, retaining only the point set that is connected and reachable to the well control source point set as the effective reservoir capacity point set; S7: Extract the outer boundary of the effective storage capacity point set as the effective storage capacity boundary of the corresponding stage of the nth cycle, and output the effective storage capacity boundary and effective storage capacity volume of the gas injection stage and the gas extraction stage respectively.

[0008] Furthermore, the reservoir static parameters include permeability K, porosity, layer thickness h, grid / geological model, well location, and well pattern; the injection-production regime parameters include the average injection-production pressure difference between the injection and production phases in each cycle. Cumulative injection volume / production volume Reference length L ref and pore volume PV; The injection-production intensity factor I(n) satisfies:

[0009] The cumulative intensity satisfy:

[0010] in, These are the weighting coefficients. k = 1...n.

[0011] Furthermore, in step S5, the pressure gradient field is obtained by coupled calculation using analytical pressure propagation, a semi-analytical model, or a numerical simulator, and the effective conductivity index is a product function of permeability, porosity, movable space factor, and relative permeability degradation factor. In step S6, the threshold criterion is that the pressure gradient field is greater than or equal to the starting pressure gradient threshold, and the flow guidance criterion is that the effective flow guidance index is greater than or equal to the effective flow guidance threshold. The connectivity reachability filtering is implemented using breadth-first search, depth-first search, or disjoint-set data structure algorithms.

[0012] Furthermore, the relative permeability degradation factor satisfies: relative permeability degradation factor = experimentally calibrated baseline degradation sequence × correction function of cumulative strength of injection-production intensity factor; the baseline degradation sequence is obtained by extracting endpoint attenuation and co-permeation zone width contraction from multi-cycle relative permeation experiments, and the correction function is a saturated function that monotonically decreases with the cumulative strength of injection-production intensity factor.

[0013] Furthermore, the method for establishing the movable space factor is as follows: Establish the baseline movable space sequence for experimental calibration And corrected using cumulative intensity:

[0014] in It is a monotonically decreasing saturated function.

[0015] Furthermore, the starting pressure gradient threshold model satisfies the following: under the same equivalent liquid saturation... Under the given conditions, the starting pressure gradient threshold and the permeability K satisfy the following:

[0016] Where b>0, For follow A function that increases monotonically.

[0017] Furthermore, the adaptive update of the initiation pressure gradient threshold satisfies: The starting pressure gradient threshold = static threshold model × correction function of cumulative strength of injection and production intensity factor; The correction function is a function that monotonically increases with the cumulative intensity of the injection-production intensity factor.

[0018] Furthermore, in step S7, the effective storage capacity boundary is output in the form of a two-dimensional boundary curve, and the effective storage capacity boundary is extracted from the effective storage capacity point set by the convex hull algorithm or the Alpha-shape algorithm. Alternatively, the effective storage capacity boundary can be output in the form of a three-dimensional isosurface, and the effective storage capacity boundary can be extracted from the set of effective storage capacity points using the MarchingCubes isosurface extraction algorithm. Alternatively, the effective storage capacity boundary can be output as a set of grid cells.

[0019] Furthermore, in step S2, the injection-production intensity factor includes the gas injection intensity factor. Gas production intensity factor And used for adaptive updates in the gas injection and gas production stages respectively; The gas injection intensity factor and the gas production intensity factor respectively satisfy the following forms:

[0020] .

[0021] In step S3, the relative permeability degradation factor is updated by the cumulative injection intensity and the cumulative production intensity, respectively, thereby obtaining the effective storage capacity boundary of injection and the effective storage capacity boundary of production. In step S4, the starting pressure gradient threshold model is adaptively updated using the gas injection intensity factor and the gas extraction intensity factor.

[0022] Due to the adoption of the above technical solution, the present invention has the following beneficial effects: Long-term boundary prediction can be performed even without field data by using parameter adaptive evolution driven by injection-production intensity factor. By filtering based on connectivity and reachability, the effective reservoir capacity connected to the well is obtained, thus avoiding misjudgment of isolated reservoirs. Considering the coupling of the starting pressure gradient threshold with phase permeability degradation and movable space decay, the prediction of the effective storage capacity boundary shrinkage of low-permeability gas storage facilities over multiple cycles is made closer to reality. It can output the effective storage capacity boundaries for gas injection and gas production separately, supporting the design of injection-production pressure difference windows, optimization of well spacing and well network, and dynamic correction of storage capacity. Attached Figure Description

[0023] Figure 1 : Flowchart of the method of this invention; Figure 2 : Schematic diagram of injection-production intensity factor and cumulative intensity; Figure 3a : Schematic diagram of the shrinkage of the relative permeability curve with cumulative intensity; Figure 3b :Comparison chart of injection and extraction direction lag; Figure 3a , Figure 3b The relative permeability degradation factor in this invention is shown. The update effect, among which, Figure 3a Displays the cumulative intensity as a function of the number of cycles n. This is a degradation process characterized by an increase in the overall shrinkage of the relative permeability curve, a narrowing of the co-permeability zone, and a decrease in the endpoint values. Figure 3b The data shows that under the same cycle, due to differences in fluid capture mechanisms, the relative permeability in the gas extraction direction (dashed line) is significantly lower than that in the gas injection direction (solid line), exhibiting a directional lag characteristic. Figure 4 : Schematic diagram of the rapid decay of the movable space factor M(n) at the beginning and slow decay at the end; Figure 5 The invention starts with a pressure gradient threshold. A schematic diagram of the static relationship and dynamic evolution; Figure 5 The data shows that the threshold G is inversely proportional to the permeability K, and increases with cumulative intensity. As n=1 to n=6, under the enhanced effects of liquid-sealed gas and micro-blocking, the threshold curve shifts upward overall, and the increase in the low-permeability zone (left side) is significantly greater than that in the high-permeability zone. Figure 6 A schematic diagram of the effective storage capacity boundary B(n,Inject) / B(n,Produce) and its contraction over time. Figure 7 Functional block diagram of the prediction system of this invention. Detailed Implementation

[0024] A prediction method for the dynamic evolution of effective storage capacity boundary under multi-cycle injection and production conditions in low-permeability underground gas storage reservoirs. This invention, under multi-cycle intensive injection and production conditions in low-permeability underground gas storage reservoirs, dynamically predicts the effective storage capacity boundary based on the adaptive evolution of seepage parameters driven by injection and production intensity. This solves the problem of systematic overestimation of the effective storage capacity boundary in low-permeability underground gas storage reservoirs under multi-cycle intensive injection and production conditions due to static parameters, neglect of threshold effect, and lack of connectivity. Implementation: (1) Predict the effective storage capacity boundary for the injection stage and the production stage separately; (2) Adaptively evolve seepage parameters with cycle and injection and production intensity, without relying on field well test / monitoring data; (3) Obtain the effective storage capacity boundary that conforms to the engineering definition through the triple criteria of threshold-conduction-connectivity accessibility. The technical solution is as follows: 1. Variables and Definitions n: Injection-production cycle number, n=1…N; stage: Stage identifier, stage∈{Inject,Produce}; K: Permeability; φ: Porosity; h: Effective thickness; PV: Pore volume; Sl: Equivalent liquid saturation; : Pressure gradient at position r in stage n of the nth cycle; Injection-production intensity factor; Cumulative intensity; : Relative penetration degradation factor (0-1); : Movable space factor (0-1); : Start-up pressure gradient threshold (MPa / m); Effective traffic diversion indicators; Effective diversion threshold; Ω(n,stage): Set of points with effective storage capacity; B(n,stage): Effective storage capacity boundary.

[0025] 2. Injection-Production Intensity Factor I(n) Define the injection-production intensity factor for the nth period: in: - The average injection-production pressure difference for the corresponding stage in the nth cycle (which can be either the bottom hole pressure difference or the driving pressure difference); - For reference length, well spacing, well control radius, or model characteristic scale are preferred; - This represents the cumulative injection or extraction volume corresponding to the nth cycle stage. - This represents the pore volume.

[0026] The unit is MPa / m, which is used to characterize the intensity of "scouring / trapping / clamping" caused to the porous medium by this cycle.

[0027] Define cumulative intensity: in The stage weighting coefficient is preferably in the range of 0.5 to 2.0.

[0028] 3. Relative permeability degradation factor

[0029] Establish experimentally calibrated baseline degradation sequences And corrected using cumulative intensity: in It is a monotonically decreasing saturating function, preferably: Parameter input The strength sensitivity coefficient is preferably in the range of 0.1 to 10 (m / MPa); more preferably in the range of 0.5 to 5 (m / MPa).

[0030] Benchmark Degradation Sequence The endpoint attenuation and the shrinkage of the co-permeability zone width can be extracted from multi-cycle relative permeability experiments, for example: in .

[0031] 4 Movable Space Factor

[0032] Establish the baseline movable space sequence for experimental calibration And corrected using cumulative intensity: in For a monotonically decreasing saturated function, the preferred choice is... or . The preferred range is 0.05~5 (m / MPa); more preferably 0.2~2 (m / MPa).

[0033] benchmark sequence The preferred approach, exhibiting a rapid decay pattern in the first three cycles followed by a slower decay in the later stages, can be an exponential saturation type. The preferred range for α is 0.1 to 1.5.

[0034] 5. Startup pressure gradient threshold model

[0035] The threshold model consists of a static part G0 and a dynamic correction: Among them, the static threshold is: , The preferred range is 0.2 to 1.5.

[0036] For follow A function that increases monotonically is preferably linear: Or in exponential form.

[0037] Dynamic correction function It is a monotonically increasing function, preferably: or

[0038] The preferred range is 0.05~5 (m / MPa); more preferably 0.2~2 (m / MPa).

[0039] 6. Calculation of pressure gradient field In the nth stage, the pressure gradient field is calculated based on the injection-production regime. Possible methods: -Analyze the pressure propagation model; or - Semi-analytical model; or - Numerical simulation model.

[0040] 7. Effective storage capacity criteria and connectivity reachability screening In the nth period stage, the following calculations are performed on mesh / volume element i: And define the set of candidate valid storage capacity points: in It can be determined as follows: - Preferably, the quantile threshold of the eighth quantile in the same stage of the first period is selected, preferably 5% to 30% quantile; or -Optimal selection , The preferred range is 0.05~0.3.

[0041] To ensure effective storage capacity, the reservoir must be accessible and connected to the well. Perform connectivity reachability screening: use the well network control volume or the grid clusters near the wells as the source clusters. Using breadth-first search, depth-first search, or disjoint-set data structure algorithms, search for and match points in the candidate point set. A set of connected and reachable points is obtained as follows: 8 Effective storage capacity boundary output Pick The outer envelope is the effective capacity boundary B(n,stage), and the output is: -Effective storage capacity boundary B(n,Inject); -Effective gas production storage capacity boundary B(n,Produce); -Effective storage capacity: Including indicators such as boundary contraction rate.

[0042] In addition to the methods described above, the present invention also provides a prediction system for implementing the dynamic evolution prediction method of the effective reservoir capacity boundary, and a computer-readable storage medium storing a computer program for executing the method.

[0043] The prediction system includes a processor, a memory, and a computer program stored in the memory and executable by the processor. When the computer program is executed, it implements the steps of the method described in this invention, thereby outputting effects such as the effective injection storage capacity, the effective gas extraction storage capacity, and the effective storage volume that evolve with the period. When the computer program stored on the computer-readable storage medium is executed by the processor, it also implements the steps of the method described in this invention.

[0044] The present invention will be further described below with reference to the embodiments, but the present invention is not limited thereto.

[0045] Example 1: Dynamic prediction of effective reservoir capacity boundary under conditions of no on-site data (1) Establish a gas storage model: Input Well locations and grids; (2) Input injection and extraction regime: For n=1...N, give the gas injection and extraction regime for each cycle. Duration (if format B is used, the time does not need to be explicitly used); set L ref This refers to the well spacing or well control radius. (3) Calculate I(n), I*(n); (4) The results were obtained from indoor multi-cycle phase permeability experiments. , and ; calibrated by starting pressure gradient experiment In the middle parameter b and form; (5) Press D kr =D kr , , = Search, copy, and translate up to the nth cycle; (6) Calculation ; (7) Calculate Ω cand And perform connectivity reachability filtering to obtain Ω(n,stage); (8) Output B(n,stage) and .

[0046] Example 2: Comparison and Optimization of Injection and Procurement Systems For two systems, A and B, repeat Example 1 respectively, and output... Curve and B(n,stage) evolution; selection under the premise of meeting gas supply demand. The preferred system is one with slower attenuation and smaller contraction of the gas production boundary.

[0047] Example 3: Implementation of a Dynamic Evolution Prediction System for Effective Storage Capacity Boundaries This embodiment provides a dynamic evolution prediction system for the effective storage capacity boundary of a low-permeability underground gas storage facility to implement the above-described method. The system includes: a processor; a memory connected to the processor; and a computer program stored in the memory and executable on the processor. When executed, the computer program implements the method of this invention. The system can be deployed on a server, workstation, or cloud computing platform, or in an industrial control computer with computing capabilities. The system includes at least a processor, a memory, and a computer program stored in the memory and executable on the processor.

[0048] To facilitate engineering implementation, the computer program can be logically divided into the following functional modules during execution (the modules can be independent components or integrated components, and the module division does not constitute a limitation on the scope of protection of this invention): (1) Data input module This system is used to receive and manage reservoir static parameters and injection-production regime parameters. The reservoir static parameters include at least permeability K, porosity, layer thickness h, grid / geological model, well location, and well pattern. The injection-production regime parameters include at least the average injection-production pressure difference between the injection and production phases of each cycle. Cumulative injection or production volume Reference length L ref And pore volume PV; and can input the initial value of equivalent liquid saturation SI or its solution configuration.

[0049] (2) Intensity Factor Calculation Module Used to calculate the injection-production intensity factor I(n) based on injection-production regime parameters, and further calculate the cumulative intensity. In a preferred implementation, I(n) is calculated according to the following expression: In another preferred implementation, the gas injection intensity factor l can also be calculated separately. inj (n) and gas production intensity factor l pro (n) to drive parameter updates for the gas injection and gas production phases, respectively.

[0050] (3) Parameter adaptive update module Used to establish and update the relative permeability degradation factor D based on experimentally calibrated parameters. kr (n,stage), movable space factor M(n), and starting pressure gradient threshold .in: -D kr(nstage) must at least distinguish between the injection stage and the production stage; -M(n) uses an exponential saturation decay model or a piecewise decay model as the reference sequence and reflects the inflection point of the decay slope near the 3rd period. -G Satisfy the same Under certain conditions, it exhibits a power-law decreasing relationship with K, and increases with increasing injection and production intensity or cumulative intensity.

[0051] (4) Pressure gradient field calculation module Used to calculate the pressure gradient field during the gas injection or gas production phase of the nth cycle. This module can obtain the pressure gradient field through coupled calculations using analytical pressure propagation, semi-analytical models, or numerical simulators, and outputs the pressure gradient field for each mesh / volume element. value.

[0052] (5) Candidate point determination module Used to calculate effective diversion index And determine the set of candidate effective storage capacity points based on threshold criteria and flow guidance criteria. Among them, the door The threshold criterion is ▽P≥G, and the diversion criterion is... , It can be determined by the quantile threshold or average proportion of the effective diversion index in the first cycle.

[0053] (6) Connectivity and reachability filtering module Used for the candidate effective storage capacity point set Perform connectivity reachability filtering, using the well-controlled source point set S (grid near the well or well control volume) as the source point, and retain the set of points that are connected and reachable from the source point set S as the effective storage capacity point set through breadth-first search, depth-first search, or union-set data structure algorithms. Remove isolated areas that are not connected to the well.

[0054] (7) Boundary Extraction Module Used from the set of effective storage capacity points Extract outer boundary In two-dimensional output, the boundary curve can be extracted using the convex hull algorithm or the Alpha-shape algorithm; in three-dimensional output, the MarchingCubes isosurface extraction algorithm can be used to extract the effective storage capacity boundary isosurface; alternatively, a set of effective storage capacity points can be output. A set of grid cells.

[0055] (8) Results Output Module Used to output the effective storage capacity boundary B(n,Inject) and effective storage capacity boundary B(n,Produce) for each cycle of gas injection, and output the effective storage volume V. eff Results such as (n, stage), boundary contraction rate, and stage difference index can be exported in graphical, tabular, or data interface formats.

[0056] Through the coordinated operation of the above modules, the system can predict the dynamic evolution of the effective reservoir capacity boundary over the period, relying only on static reservoir parameters, injection-production regime parameters, and experimental calibration parameters, even without on-site monitoring data.

[0057] Example 4: Computer-readable storage medium This embodiment provides a computer-readable storage medium storing computer program instructions. When the computer program instructions are executed by a processor, the computer performs the steps of the effective reservoir capacity boundary dynamic evolution prediction method described in any of the above embodiments, including: injection-production intensity factor calculation, parameter adaptive update, pressure gradient field calculation, candidate effective reservoir capacity point set determination, connectivity reachability screening, and effective reservoir capacity boundary extraction and output.

[0058] The computer-readable storage medium may be, but is not limited to, a read-only memory, a random access memory, a disk, a solid-state drive, a flash memory, a removable storage device, or a cloud storage medium.

[0059] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made to it in form and detail without departing from the scope defined by the claims of the present invention.

Claims

1. A method for predicting the dynamic evolution of the effective storage capacity boundary of an underground gas storage facility in a low-permeability reservoir under multi-cycle injection and production conditions, characterized in that: Includes the following steps: S1: Establish a gas storage model and input the static parameters of the reservoir and the parameters of the multi-cycle injection and production regime; S2: Calculate the injection-production intensity factor for each cycle based on the injection-production system parameters, and calculate the cumulative intensity based on the injection-production intensity factor for each cycle; S3: Based on the calibration parameters of the multi-cycle relative permeability experiment, establish the relative permeability degradation factor and the movable space factor, and use the cumulative intensity of the injection-production intensity factor to adaptively update the relative permeability degradation factor and the movable space factor, wherein the relative permeability degradation factor distinguishes between the gas injection stage and the gas production stage. S4: Establish a start-up pressure gradient threshold model and adaptively update the start-up pressure gradient threshold using the cumulative intensity of the injection-production intensity factor. S5: Calculate the pressure gradient field and the effective conductivity index during the gas injection or gas production phase of the nth cycle. S6: Determine the candidate effective reservoir capacity point set based on the threshold criterion and the diversion criterion, and perform connectivity reachability screening on the candidate effective reservoir capacity point set, retaining only the point set that is connected and reachable to the well control source point set as the effective reservoir capacity point set; S7: Extract the outer boundary of the effective storage capacity point set as the effective storage capacity boundary of the corresponding stage of the nth cycle, and output the effective storage capacity boundary and effective storage capacity volume of the gas injection stage and the gas extraction stage respectively.

2. The prediction method for the dynamic evolution of the effective storage capacity boundary of a low-permeability underground gas storage facility under multi-cycle injection and production conditions, as described in claim 1, is characterized in that... The reservoir static parameters include permeability K, porosity, layer thickness h, grid / geological model, well location and well pattern; the injection-production regime parameters include the average injection-production pressure difference between the gas injection and production phases in each cycle. Cumulative injection volume / production volume Reference length L ref and pore volume PV; The injection-production intensity factor I(n) satisfies: ； The cumulative intensity satisfy: ； in, These are the weighting coefficients.

3. The prediction method for the dynamic evolution of the effective storage capacity boundary of a low-permeability underground gas storage facility under multi-cycle injection and production conditions, as described in claim 2, is characterized in that... In step S5, the pressure gradient field is obtained by coupled calculation using analytical pressure propagation, semi-analytical model or numerical simulator, and the effective conductivity index is the product function of permeability, porosity, movable space factor and relative permeability degradation factor. In step S6, the threshold criterion is that the pressure gradient field is greater than or equal to the starting pressure gradient threshold, and the flow guidance criterion is that the effective flow guidance index is greater than or equal to the effective flow guidance threshold. The connectivity reachability filtering is implemented using breadth-first search, depth-first search, or disjoint-set data structure algorithms.

4. The prediction method for the dynamic evolution of the effective storage capacity boundary of a low-permeability underground gas storage facility under multi-cycle injection and production conditions, as described in claim 1, is characterized in that... The relative permeability degradation factor satisfies the following: relative permeability degradation factor = experimentally calibrated baseline degradation sequence × correction function of cumulative strength of injection-production intensity factor; the baseline degradation sequence is obtained by extracting endpoint attenuation and co-permeation zone width contraction from multi-cycle relative permeability experiments, and the correction function is a saturated function that monotonically decreases with the cumulative strength of injection-production intensity factor.

5. The prediction method for the dynamic evolution of the effective storage capacity boundary of a low-permeability underground gas storage facility under multi-cycle injection and production conditions, as described in claim 1, is characterized in that... The method for establishing the movable space factor is as follows: Establish the baseline movable space sequence for experimental calibration And corrected using cumulative intensity: ； in It is a monotonically decreasing saturated function.

6. The prediction method for the dynamic evolution of the effective storage capacity boundary of a low-permeability underground gas storage facility under multi-cycle injection and production conditions, as described in claim 1, is characterized in that... The starting pressure gradient threshold model satisfies the following: under the same equivalent liquid saturation... Under the given conditions, the starting pressure gradient threshold and the permeability K satisfy the following: ； Where b>0, For follow A function that increases monotonically.

7. The prediction method for the dynamic evolution of the effective storage capacity boundary of a low-permeability underground gas storage facility under multi-cycle injection and production conditions, as described in claim 6, is characterized in that... The adaptive update of the startup pressure gradient threshold satisfies: The starting pressure gradient threshold = static threshold model × correction function of cumulative strength of injection and production intensity factor; The correction function is a function that monotonically increases with the cumulative intensity of the injection-production intensity factor.

8. The prediction method for the dynamic evolution of the effective storage capacity boundary of a low-permeability underground gas storage facility under multi-cycle injection and production conditions, as described in claim 1, is characterized in that... In step S7, the effective storage capacity boundary is output in the form of a two-dimensional boundary curve, and the effective storage capacity boundary is extracted from the effective storage capacity point set by the convex hull algorithm or the Alpha-shape algorithm. Alternatively, the effective storage capacity boundary can be output in the form of a three-dimensional isosurface, and the effective storage capacity boundary can be extracted from the set of effective storage capacity points using the MarchingCubes isosurface extraction algorithm. Alternatively, the effective storage capacity boundary can be output as a set of grid cells.

9. The prediction method for the dynamic evolution of the effective storage capacity boundary of a low-permeability underground gas storage facility under multi-cycle injection and production conditions, as described in claim 1, is characterized in that... In step S2, the injection-production intensity factor includes the gas injection intensity factor. Gas production intensity factor And used for adaptive updates in the gas injection and gas production stages respectively; The gas injection intensity factor and the gas production intensity factor respectively satisfy: ； In step S3, the relative permeability degradation factor is updated by the cumulative injection intensity and the cumulative production intensity, respectively, thereby obtaining the effective storage capacity boundary of injection and the effective storage capacity boundary of production. In step S4, the starting pressure gradient threshold model is adaptively updated using the gas injection intensity factor and the gas extraction intensity factor.

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