A method for evaluating interwell connectivity in a gas reservoir

Abstract

The application provides a gas reservoir well group intercommunication evaluation method; comprising: step one, establishing a multi-well group supply gas reservoir material balance mathematical model; step two, a well group inter-supply gas amount calculation method; step three, model solving, obtaining each time step zone formation pressure solution; step four, after solving the formation pressure of each block at different time steps by step three, the particle swarm optimization algorithm is used to inverse each block reserves and the parameters between well groups, so that the calculated formation pressure is consistent with the actual monitoring value. The application introduces the inter-well group conductivity to consider the channeling between well groups, deforms the gas reservoir material balance equation with supply, establishes a multi-well group material balance calculation mathematical model, solves the model by using successive substitution, Newton-Raphson nonlinear iteration algorithm and the like, combines the particle swarm algorithm, fits the formation pressure, inverses the dynamic control reserves and the inter-well group conductivity, and forms a gas reservoir well / inter-well group intercommunication evaluation method.

Description

Technical Field

[0001] The present invention belongs to the field of oil and gas field development, and in particular relates to a method for evaluating connectivity between gas reservoir well groups. Background Art

[0002] Carbonate gas reservoirs in my country feature diverse pore structures, significant differences in physical properties, and strong heterogeneity. This leads to complex interwell connectivity within these reservoirs, resulting in increasingly complex reservoir pressure system distributions. Accurate assessment of interwell connectivity within these reservoirs is crucial for determining reserve utilization, optimizing well patterns and locations, and formulating development technology policies. It represents a core technical challenge that urgently needs to be addressed. Current methods for analyzing interwell connectivity within gas reservoirs can be categorized into two main types, static and dynamic, depending on the data used. Static methods include analysis of geological characteristics, fluid composition differences, and original formation pressure. These methods primarily integrate a variety of geological and testing data, including seismic inversion, well logging interpretation, drilling coring, and fluid sampling, to delineate reservoir sedimentary and flow units, clarify reservoir pressure distribution, and reveal interwell connectivity. While static methods offer a relatively simple analysis process and a wide range of applicability, including those applicable to oil and gas reservoirs, they require high data and geological information, are primarily qualitative, and thus struggle to quantitatively assess interwell connectivity. Dynamic methods are often based on production and monitoring data such as production volume and high-precision pressure, combined with a small number of geological parameters. By fitting and analyzing changes in dynamic data, they evaluate interwell connectivity. These methods are primarily divided into well testing analysis (such as pressure buildup testing and well-to-well interference testing) and production dynamic analysis.

[0003] However, the pressure buildup well test method relies on high-precision pressure monitoring data, resulting in a time-consuming and costly testing process and relatively poor applicability. Production dynamic analysis methods suffer from low accuracy and unclear connectivity indicators. Therefore, inter-well conductivity is introduced to account for crossflow between well groups. By deforming the material balance equation for gas reservoirs with replenishment, a mathematical model for multi-well group material balance calculation is established. Successive substitution and Newton-Raphson nonlinear iterative algorithms are used to solve the model. Combined with a particle swarm algorithm, formation pressure is fitted, and dynamic control reserves and inter-well conductivity are inverted. This results in a method for evaluating gas reservoir well / well group connectivity. The accuracy of the model is confirmed by comparison with multiple gas reservoir numerical simulation examples. Summary of the Invention

[0004] The present invention aims to provide a method for evaluating connectivity between well groups in gas reservoirs. This method addresses the shortcomings of dynamic methods in studying inter-well interference or inter-well connectivity. By introducing inter-well conductivity to account for crossflow, the present invention modifies the material balance equation for gas reservoirs with replenishment, establishes a mathematical model for calculating multi-well group material balances, and employs successive substitution and Newton-Raphson nonlinear iterative algorithms to solve the model. Combined with a particle swarm optimization algorithm, the method fits formation pressure and inverts dynamically controlled reserves and inter-well conductivity, resulting in a method for evaluating connectivity between wells and well groups in gas reservoirs.

[0005] The present invention is achieved through the following technical solutions:

[0006] The present invention relates to a method for evaluating connectivity between gas reservoir well groups, comprising the following steps:

[0007] Step 1: Establish a material balance mathematical model for multi-well group recharge gas reservoirs: Based on the heterogeneity of the gas reservoir and the formation pressure test data of different well groups, the gas reservoir is divided into independent but not closed blocks, and a material balance equation for the gas reservoir with recharge is established for each block;

[0008] Step 2: Calculate the inter-well gas supply volume. The inter-well gas supply volume is calculated by introducing the connectivity conductivity and using Darcy's law to calculate the gas cross-flow rate at a certain moment. The cumulative gas cross-flow rate is then calculated using a successive iteration method to avoid errors in direct calculation.

[0009] Step 3: Solve the model: Construct a nonlinear iterative matrix equation, introduce the coefficient matrix solution according to whether the zones are connected, and obtain the formation pressure solution for each zone at each time step;

[0010] Step 4: Invert the connectivity between well groups using the particle swarm optimization algorithm: After solving the formation pressure of each block at different time steps using step 3, the reserves of each block and the connectivity parameters between well groups are inverted using the particle swarm optimization algorithm to make the calculated formation pressure consistent with the actual monitoring value.

[0011] Preferably, the step 1 specifically includes the following steps:

[0012] According to the heterogeneity of the gas reservoir and the formation pressure test data of different well groups, the gas reservoir is divided into n independent but not closed blocks, each containing one or more gas wells. The material balance equation of the gas reservoir with replenishment in each block is established as shown in (1):

[0013]

[0014]

[0015] For zone 1 and zone n, which are special expressions of formula (1), the material balance equations are formulas (2) and (3) respectively:

[0016]

[0017]

[0018] In formulas (1)-(3), p i is the original formation pressure of the gas reservoir, MPa; p j is the formation pressure in zone j at a certain moment, MPa; G j and G pj are the original natural gas reserves of area j and the cumulative gas production at a certain moment, 10 8 m 3 ;z i and z j are the gas deviation coefficients of the original gas reservoir and the gas deviation coefficients of the j zone at a certain moment; C e is the comprehensive compressibility coefficient of gas reservoir rock and irreducible water, MPa -1 ;Δp j is the pressure drop in zone j at a certain moment, MPa; G jk , G mj are the cumulative gas cross-flow from area j to area k and from area m to area j at a certain moment, 10 8 m 3 ;

[0019] When the reserves are known, the material balance equations (1)-(3) with recharge are actually functions of the pressure in each zone. Then, equations (1)-(3) can be rewritten as the following equations (4), (5), and (6):

[0020]

[0021]

[0022]

[0023] Preferably, the specific steps of step 2 are:

[0024] For the calculation of the gas cross-flow rate from area j to area k at a certain moment in formula (1), assuming that Darcy's law is followed, the calculation formula (7) is:

[0025]

[0026] In formula (7), B g is the gas volume coefficient, μ g is the gas viscosity, mPa·s; α is the unit conversion coefficient, 0.0864; m j and m kThe pseudo formation pressure of j zone and k zone respectively, unit: MPa, the calculation formula (8) is:

[0027]

[0028] In order to directly reflect the size of the connectivity between j zone and k zone, the concept of connectivity conductivity is introduced, and the definition formula (9) is:

[0029]

[0030] T jk The conductivity between j zone and k zone, unit: mD·m or m 3 / d, is a function of permeability k jk , contact area A jk and distance L jk , which is independent of fluid parameters and only reflects the distribution characteristics of the reservoir;

[0031] Substitute formula (9) into formula (7) to obtain formula (10):

[0032]

[0033] In formula (7), T is the temperature of the gas reservoir, K; P sc , T sc are the pressure and temperature of the gas reservoir under standard conditions, 0.101 MPa, 298.15 K.

[0034] At t+Δt, the cumulative gas channeling flow from j zone to k zone is solved by successive iteration method, and the calculation formula (11) is:

[0035] G jk (t+Δt)=G jk (t)+0.5[q jk (t)+q jk (t+Δt)]Δt (11)

[0036] Substitute formula (10) into formula (11) to obtain formula (12):

[0037] G jk (t+Δt)=G jk (t)+0.5T jk S r {[m j (t)-m k (t)]+[m j (t+Δt)-m k (t+Δt)]}Δt(12)

[0038] Preferably, the specific steps of step three are:

[0039] The Newton-Raphson nonlinear equations iteration method is introduced to solve the constructed gas reservoir material balance calculation model (4)-(6) with replenishment. The calculation formula is:

[0040]

[0041] In formula (13), l is the number of iterations. According to formula (13), the nonlinear iterative matrix equation (14) is constructed:

[0042]

[0043] The calculation formula (15) for each variable in the Jacobi coefficient matrix on the left side of formula (14) is:

[0044]

[0045] If area j and area k are adjacent, they are considered to be connected. The calculation formula (16) is:

[0046]

[0047] If the j area and the k area are not adjacent, they are considered to be not connected, and the calculation formula (17) is:

[0048]

[0049] Formula (14) is used to accurately obtain the formation pressure solution of each zone at each time step.

[0050] Preferably, the specific steps of step 4 are:

[0051] Using the solution method proposed in step 3, the mathematical model of the multi-well group material balance equation established in step 1 is solved to obtain the formation pressure of each block at different time steps. The calculation results mainly depend on the reserves of each block and the parameters between the well groups. In practical applications, by optimizing and adjusting these model parameters, the calculated formation pressure is consistent with the actual monitoring value, that is, the inversion solution of these parameters is realized, as shown in formula (18):

[0052]

[0053] In formula (18), is the objective function, reflecting the gap between the calculated value and the actual value; is a parameter vector, which includes the gas reservoir reserves and the conductivity between wells (well groups) in all blocks; and are the formation pressure of each block obtained from actual testing and its error covariance matrix; The formation pressure data vectors of each block are obtained by using the model in this paper;

[0054] For the optimization problem shown in formula (18), the particle swarm optimization algorithm is used here to solve it. This algorithm is a group-based random optimization technology. It assumes that a possible solution is a particle. Each particle can be regarded as an individual in the D-dimensional search space. The current position of the particle is a candidate solution to the corresponding optimization problem. The optimal solution searched by each particle individually is called the individual extreme value. The optimal individual extreme value in the group is regarded as the current global optimal solution. Through continuous iterative search calculation, the speed and position of each particle are updated until the optimal solution that meets the termination condition is obtained. Then, for the jth particle, the update formulas (19) and (20) of the particle speed and position are:

[0055]

[0056]

[0057] In formulas (19) and (20), is the velocity and position of the jth particle at the lth step; ω is the compression factor; ψ=c1r1+c2r2(ψ>4), usually, ψ is 4.1, then c1r1=c2r2=2.05, ω=0.729.

[0058] The present invention has the following advantages:

[0059] The present invention addresses the shortcomings of the dynamic method in studying inter-well interference or inter-well connectivity. By introducing the conductivity between well groups to consider inter-well crossflow, the material balance equation of a gas reservoir with replenishment is deformed to establish a mathematical model for material balance calculation of multiple well groups. The model is solved using successive substitution and Newton-Raphson nonlinear iterative algorithms. Combined with the particle swarm algorithm, the formation pressure is fitted, and the dynamic control reserves and inter-well conductivity are inverted to form a method for evaluating the connectivity of gas reservoir wells / well groups. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 is a flow chart of a method for determining a carbon dioxide displacement front by well testing according to the present invention;

[0061] Figure 2 It is a schematic diagram of a multi-well group recharge gas reservoir;

[0062] Figure 3 It is a flow chart of formation pressure fitting and model parameter inversion calculation;

[0063] Figure 4 The method of the present invention is used to establish a numerical simulation model diagram with two gas wells;

[0064] Figure 5Fig. 1 is a comparison chart of results before and after formation pressure fitting and gas supply calculation results; wherein (a) is a comparison chart of results before and after formation pressure fitting; (b) is a comparison chart of interval gas supply calculation results. DETAILED DESCRIPTION

[0065] The application will be described in detail below with specific examples. It should be noted that the following examples are only further illustrations of the application, and the protection scope of the application is not limited to the following examples.

[0066] Example 1

[0067] This example relates to a method for evaluating inter-well group connectivity of a gas reservoir, as shown in Figs. 1 to 3, comprising the following steps: Figure 1 、 Figure 2

[0068] Step one, establishing a multi-well group supply gas reservoir material balance mathematical model: according to the heterogeneity of the gas reservoir and the different well group formation pressure test data, the gas reservoir is divided into n independent but not closed blocks, and a gas reservoir material balance equation with supply for each block is established.

[0069] Step two, calculating the inter-well group gas supply: wherein the inter-well group gas supply calculation method is: according to Darcy's law, the gas channeling flow at a certain time is obtained; the error generated in the direct calculation process is avoided, the connectivity conductivity is introduced, and the successive iteration method is used to solve the cumulative gas channeling flow.

[0070] Step three, solving the model: constructing a nonlinear iterative matrix equation, introducing the solving formula of the coefficient matrix according to whether the zones are connected, and obtaining the formation pressure solution of each zone at each time step.

[0071] Step four, inverting the inter-well group connectivity through the particle swarm algorithm: after solving the formation pressure of each block at different time steps by step three, the reserves of each block and the connectivity parameters between well groups are inverted through the particle swarm optimization algorithm, so that the calculated formation pressure is consistent with the actual monitoring value.

[0072] Preferably, step one specifically comprises the following steps:

[0073] According to the heterogeneity of the gas reservoir and the different well group formation pressure test data, the gas reservoir is divided into n independent but not closed blocks, and each block contains one or more gas wells, and a gas reservoir material balance equation with supply for each block is established as shown in equation (1):

[0074]

[0075]

[0076] ​For zone 1 and zone n, which are special expressions of formula (1), the material balance equations are formulas (2) and (3) respectively:

[0077]

[0078]

[0079] In formulas (1)-(3), p i is the original formation pressure of the gas reservoir, MPa; p j is the formation pressure in zone j at a certain moment, MPa; G j and G pj are the original natural gas reserves of area j and the cumulative gas production at a certain moment, 10 8 m 3 ;z i and z j are the gas deviation coefficients of the original gas reservoir and the gas deviation coefficients of the j zone at a certain moment; C e is the comprehensive compressibility coefficient of gas reservoir rock and irreducible water, MPa -1 ;Δp j is the pressure drop in zone j at a certain moment, MPa; G jk , G mj are the cumulative gas cross-flow from area j to area k and from area m to area j at a certain moment, 10 8 m 3 ;

[0080] When the reserves are known, the material balance equations (1)-(3) with recharge are actually functions of the pressure in each zone. Then, equations (1)-(3) can be rewritten as the following equations (4), (5), and (6):

[0081]

[0082]

[0083]

[0084] Preferably, the specific steps of step 2 are:

[0085] For the calculation of the gas cross-flow rate from area j to area k at a certain moment in formula (1), assuming that Darcy's law is followed, the calculation formula (7) is:

[0086]

[0087] In formula (7), B g is the gas volume coefficient, μ g is the gas viscosity, mPa·s; α is the unit conversion coefficient, 0.0864; m j and m kare the pseudo formation pressures of zone j and zone k, respectively, in MPa, and the calculation formula (8) is:

[0088]

[0089] In order to directly reflect the connectivity between the j-zone and the k-zone, the concept of connectivity conductivity is introduced and the definition of formula (9) is:

[0090]

[0091] T jk is the conductivity between the j region and the k region, in mD·m or m 3 / d, is about the permeability k jk , contact area A jk and distance L jk It is a function of , which has nothing to do with fluid parameters and only reflects the reservoir distribution characteristics;

[0092] Substituting formula (9) into formula (7) yields formula (10):

[0093]

[0094] In formula (7), T is the gas reservoir temperature, K; P sc 、T sc The gas reservoir pressure and temperature under standard conditions are 0.101MPa and 298.15K respectively.

[0095] At time t+Δt, the cumulative gas crossflow from area j to area k is solved using the successive iteration method, and the calculation formula (11) is:

[0096] G jk (t+Δt)=G jk (t)+0.5[q jk (t)+q jk (t+Δt)]Δt (11)

[0097] Substituting formula (10) into formula (11) yields formula (12):

[0098] G jk (t+Δt)=G jk (t)+0.5T jk S r {[m j (t)-m k (t)]+[m j (t+Δt)-m k (t+Δt)]}Δt(12)

[0099] Preferably, the specific steps of step three are:

[0100] The Newton-Raphson nonlinear equations iteration method is introduced to solve the constructed gas reservoir material balance calculation model with recharge (Equations (4)-(6)). The calculation formula is:

[0101]

[0102] In formula (13), l is the number of iterations. According to formula (13), the nonlinear iterative matrix equation (14) is constructed:

[0103]

[0104] The calculation formula (15) for each variable in the Jacobi coefficient matrix on the left side of formula (14) is:

[0105]

[0106] If area j and area k are adjacent, they are considered to be connected. The calculation formula (16) is:

[0107]

[0108] If the j area and the k area are not adjacent, they are considered to be not connected, and the calculation formula (17) is:

[0109]

[0110] Formula (14) is used to accurately obtain the formation pressure solution of each zone at each time step.

[0111] Preferably, the specific steps of step 4 are:

[0112] Using the solution method proposed in step 3, the mathematical model of the multi-well group material balance equation established in step 1 is solved to obtain the formation pressure of each block at different time steps. The calculation results mainly depend on the reserves of each block and the parameters between the well groups. In practical applications, by optimizing and adjusting these model parameters, the calculated formation pressure is consistent with the actual monitoring value, that is, the inversion solution of these parameters is realized, as shown in formula (18):

[0113]

[0114] In formula (18), is the objective function, reflecting the gap between the calculated value and the actual value; is a parameter vector, which includes the gas reservoir reserves and the conductivity between wells (well groups) in all blocks; and are the formation pressure of each block obtained from actual testing and its error covariance matrix; The formation pressure data vectors of each block are obtained by using the model in this paper;

[0115] For the optimization problem shown in formula (18), the particle swarm optimization algorithm is used here to solve it. This algorithm is a group-based random optimization technology. It assumes that a possible solution is a particle. Each particle can be regarded as an individual in the D-dimensional search space. The current position of the particle is a candidate solution to the corresponding optimization problem. The optimal solution searched by each particle individually is called the individual extreme value. The optimal individual extreme value in the group is regarded as the current global optimal solution. Through continuous iterative search calculation, the speed and position of each particle are updated until the optimal solution that meets the termination condition is obtained. Then, for the jth particle, the update formulas (19) and (20) of the particle speed and position are:

[0116]

[0117]

[0118] In formulas (19) and (20), is the velocity and position of the jth particle at step l; ω is the compression factor; ψ=c1r1+c2r2(ψ>4). Under normal circumstances, ψ is 4.1, then c1r1=c2r2=2.05, ω=0.729. The flow chart of formation pressure fitting and model parameter inversion for each block is shown in the figure. Figure 3 shown.

[0119] Example 2

[0120] This embodiment relates to a method for evaluating connectivity between gas reservoir well groups, and its application in an example oil reservoir is as follows:

[0121] Based on the method described in Example 1, a numerical simulation model with two gas wells was established. Figure 4 As shown in Figure 1, the area where well P1 is located is defined as Zone 1, and the area where well P2 is located is defined as Zone 2. For the base scenario, it is assumed that the two gas wells have the same controlled area and reserves. A region with different permeabilities exists between the two blocks to characterize seepage channels with different connectivity and conductivity. The specific model parameter design is shown in Table 1.

[0122] Table 1

[0123]

[0124] According to the numerical simulation model established above, the cumulative gas production and formation pressure data of P1 and P2 wells were obtained. According to modern production decline analysis and conductivity calculation methods, the initial geological reserves of the two areas before fitting were determined to be 10×10 8 m 3 The conductivity of the two intervals is 8mD·m, which is used as the initial input parameter to fit the formation pressure ( Figure 5(a)), the geological reserves and conductivity of the two zones were obtained by inversion, and the cumulative gas supply of the two zones was calculated and compared with the numerical simulation results ( Figure 5 (b)). In the actual production process, since the gas production of P2 gas well is higher than that of P1 well, the formation pressure of area 2 is lower than that of area 1, which causes gas supply from area 1 to area 2. The single well controlled reserves continue to increase and exceed the initial value. Figure 5 As can be seen in (a), if the recharge between the two intervals is not considered, the calculated gas reservoir reserves in area 1 will be too high, while those in area 2 will be too low. Using the model established in this paper, the formation pressure can be predicted more accurately.

[0125] In addition, the calculation results show that the geological reserves and conductivity inversion values ​​of Area 1 and Area 2 are 11×10 8 m 3 , 26.5mD·m, the errors between them and the actual values ​​are 1.0% and 2.1%, which are relatively small, confirming the accuracy of the inversion results. Figure 5 It can be found in (b) that the calculated results of the interval gas supply volume are completely consistent with the actual values.

[0126] The present invention addresses the shortcomings of the dynamic method in studying inter-well interference or inter-well connectivity. By introducing the conductivity between well groups to consider inter-well crossflow, the material balance equation of a gas reservoir with replenishment is deformed to establish a mathematical model for material balance calculation of multiple well groups. The model is solved using successive substitution and Newton-Raphson nonlinear iterative algorithms. Combined with the particle swarm algorithm, the formation pressure is fitted, and the dynamic control reserves and inter-well conductivity are inverted to form a method for evaluating the connectivity of gas reservoir wells / well groups.

[0127] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art may make various variations or modifications within the scope of the claims, which do not affect the essence of the present invention.

Claims

1. A method for evaluating connectivity between gas reservoir well groups, characterized in that: The following steps are involved: Step 1: Establish a material balance mathematical model for multi-well group recharge gas reservoirs: Based on the heterogeneity of the gas reservoir and the formation pressure test data of different well groups, the gas reservoir is divided into independent but not closed blocks, and a material balance equation for the gas reservoir with recharge is established for each block; Step 2: Calculate the inter-well gas supply volume. The inter-well gas supply volume is calculated by introducing the connectivity conductivity and using Darcy's law to calculate the gas cross-flow rate at a certain moment. The cumulative gas cross-flow rate is then calculated using a successive iteration method to avoid errors in direct calculation. Step 3: Solve the model: Construct a nonlinear iterative matrix equation, introduce the solution formula of the coefficient matrix according to whether the zones are connected, and obtain the formation pressure solution of each zone at each time step; Step 4: Invert the connectivity between well groups using the particle swarm optimization algorithm: After solving the formation pressure of each block at different time steps using step 3, the reserves of each block and the connectivity parameters between well groups are inverted using the particle swarm optimization algorithm to make the calculated formation pressure consistent with the actual monitored value; In step 1, the specific steps of establishing a material balance mathematical model for a multi-well group gas reservoir are as follows: According to the heterogeneity of the gas reservoir and the formation pressure test data of different well groups, the gas reservoir is divided into n independent but not closed blocks, each containing one or more gas wells. The material balance equation of the gas reservoir with replenishment in each block is established as shown in (1): For zone 1 and zone n, which are special expressions of formula (1), the material balance equations are formulas (2) and (3) respectively: In formulas (1)-(3), p i is the original formation pressure of the gas reservoir, MPa; p j is the formation pressure in zone j at a certain moment, MPa; G j and G pj are the original natural gas reserves of area j and the cumulative gas production at a certain moment, 10 8 m 3 ;z i and z j are the gas deviation coefficients of the original gas reservoir and the gas deviation coefficients of the j zone at a certain moment; C e is the comprehensive compressibility coefficient of gas reservoir rock and irreducible water, MPa -1 ;Δp j is the pressure drop in zone j at a certain moment, MPa; G jk , G mj are the cumulative gas cross-flow from area j to area k and from area m to area j at a certain moment, 10 8 m 3 ; When the reserves are known, the material balance equations (1)-(3) with recharge are actually functions of the pressure in each zone. Then, equations (1)-(3) can be rewritten as the following equations (4), (5), and (6):

2. The method for evaluating connectivity between gas reservoir well groups according to claim 1, wherein: In step 2, the specific steps of calculating the gas supply volume between well groups are: For the calculation of the gas cross-flow rate from area j to area k at a certain moment in formula (1), assuming that Darcy's law is followed, the calculation formula (7) is: In formula (7), B g is the gas volume coefficient, μ g is the gas viscosity, mPa·s; α is the unit conversion coefficient, 0.0864; m j and m k are the pseudo formation pressures of zone j and zone k, respectively, in MPa, and the calculation formula (8) is: In order to directly reflect the connectivity between the j-zone and the k-zone, the concept of connectivity conductivity is introduced and the definition of formula (9) is: T jk is the conductivity between the j region and the k region, in mD·m or m 3 / d, is about the permeability k jk , contact area A jk and distance L jk It is a function of , which has nothing to do with fluid parameters and only reflects the reservoir distribution characteristics; Substituting formula (9) into formula (7) yields formula (10): In formula (7), T is the gas reservoir temperature, K; P sc 、T sc They are the reservoir pressure and temperature under standard conditions, 0.101 MPa, 298.15 K; At time t+Δt, the cumulative gas crossflow from area j to area k is solved using the successive iteration method, and the calculation formula (11) is: G jk (t+Δt)=G jk (t)+0.5[q jk (t)+q jk (t+Δt)]Δt (11) Substituting formula (10) into formula (11) yields the following formula (12): G jk (t+Δt)=G jk (t)+0.5T jk S r {[m j (t)-m k (t)]+[m j (t+Δt)-m k (t+Δt)]}Δt (12)。 3. The method for evaluating connectivity between gas reservoir well groups according to claim 1, wherein: In step three, the specific steps of solving the model are: The Newton-Raphson nonlinear equations iteration method is introduced to solve the constructed gas reservoir material balance calculation model with recharge (Equations (4)-(6)), and the calculation formula (13) is: In formula (13), l is the number of iterations. According to formula (13), the nonlinear iterative matrix equation (14) is constructed: The calculation formula (15) for each variable in the Jacobi coefficient matrix on the left side of formula (14) is: If area j and area k are adjacent, they are considered to be connected. The calculation formula (16) is: If the j area and the k area are not adjacent, they are considered to be not connected, and the calculation formula (17) is: Formula (14) is used to accurately obtain the formation pressure solution of each zone at each time step.

4. The method for evaluating connectivity between gas reservoir well groups according to claim 1, wherein: In step 4, the specific steps of performing inversion using the particle swarm algorithm are: Using the solution method proposed in step 3, the mathematical model of the multi-well group material balance equation established in step 1 is solved to obtain the formation pressure of each block at different time steps. The calculation results mainly depend on the reserves of each block and the parameters between the well groups. In practical applications, by optimizing and adjusting these model parameters, the calculated formation pressure is consistent with the actual monitoring value, that is, the inversion solution of these parameters is realized, as shown in formula (18): In formula (18), is the objective function, reflecting the gap between the calculated value and the actual value; is a parameter vector, which includes the gas reservoir reserves and the conductivity between wells (well groups) in all blocks; and are the formation pressure of each block obtained from actual testing and its error covariance matrix; The formation pressure data vectors of each block are obtained by using the model in this paper; For the optimization shown in formula (18), for the jth particle, the update formulas (19) and (20) for the particle velocity and position are: In formulas (19) and (20), is the velocity and position of the jth particle in the lth step; ω is the compression factor; ψ=c1r1+c2r2(ψ>4), usually, ψ is 4.1, then c1r1=c2r2=2.05, ω=0.729.

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