A method for predicting the deliverability of a tight gas well after fracturing based on the degree of recovery

Abstract

The application discloses a kind of based on the tight gas well pressure after degree of production prediction method, comprising: obtaining the measured reservoir parameter of target gas well;Establish numerical simulation standard group model, adopt preset standard reservoir parameter to carry out simulation calculation, obtain benchmark recovery degree, establish contrast analysis model, by changing single reservoir parameter carries out multiple simulation calculation, obtains the function mapping relationship of each reservoir parameter and recovery degree;According to function mapping relationship and benchmark recovery degree, calculate benchmark difference and establish comprehensive deviation calculation model;Based on the measured reservoir parameter of target gas well and comprehensive deviation calculation model, combine benchmark recovery degree to calculate target recovery degree, carry out time normalization processing to its and the product of geological reserves, generate the post-pressure production prediction value of target gas well and the like steps.The application improves the accuracy and reliability of post-pressure production prediction.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas extraction technology, specifically relating to a method for predicting the post-pressure production capacity of tight gas wells based on the degree of recovery. Background Technology

[0002] In the field of oil and gas resource development, tight gas reservoirs, as important unconventional natural gas resources, are of great significance to ensuring energy supply security. Due to their low permeability, tight gas reservoirs present significant challenges in well extraction and result in low production capacity, often necessitating measures such as fracturing to increase productivity during development. However, in the later stages of well extraction, as formation pressure decreases, well productivity continues to decline. Therefore, post-fracturing productivity assessment and prediction are crucial for developing appropriate development plans.

[0003] Traditional methods for predicting post-compression production capacity of tight gas wells mainly include production decline analysis, numerical simulation, and experimental analogy. Production decline analysis establishes a decline curve based on historical production data and predicts future production capacity through curve extrapolation. However, this method ignores the impact of reservoir parameter changes on production capacity, and its prediction accuracy is limited by the quality of historical production data. Numerical simulation uses detailed geological models for numerical simulation calculations. While theoretically more rigorous, it requires a large amount of detailed geological data and computational resources, and the model parameters are highly sensitive, often leading to significant errors in practical applications due to insufficient information. Experimental analogy predicts the post-compression production capacity of a target well by comparing its performance with that of similar blocks or wells. However, because the reservoir characteristics of each well differ, the analogy results often lack specificity. Summary of the Invention

[0004] This invention is proposed to overcome the problem of low accuracy in predicting the development of tight gas reservoirs using traditional methods. Its purpose is to provide a method for predicting the post-production capacity of tight gas wells based on the degree of recovery.

[0005] This invention is achieved through the following technical solution: A method for predicting the post-compression production capacity of tight gas wells based on the degree of recovery includes the following steps: S1. Obtain the measured reservoir parameters of the target gas well; The reservoir parameters include formation permeability, water saturation, and reservoir thickness; S2. Establish a standard group model for numerical simulation, and use preset standard reservoir parameters to perform simulation calculations to obtain the baseline recovery rate; Specifically, the following steps are included: S21. Establish a three-dimensional mesh numerical simulation model; The three-dimensional mesh numerical simulation model includes a first directional mesh along the crack propagation direction, a second directional mesh perpendicular to the crack propagation direction, and a vertical directional mesh. The second direction mesh uses differentiated mesh processing; S22. Set standard reservoir parameters for the three-dimensional mesh numerical simulation model established in step S21, define the gas well production regime, and set the production duration. The standard reservoir parameters include standard formation permeability, standard porosity, standard water saturation, and standard reservoir thickness. The gas well production system is a phased production model that includes a fixed production stage and a fixed bottom pressure production stage. S23. Based on the three-dimensional grid numerical simulation model established in step S21, the standard reservoir parameters and gas well production system set in step S22 are substituted into the three-dimensional grid numerical simulation model for calculation to obtain the curve of cumulative gas production of the standard group over time; the geological reserves of the standard group are calculated according to the standard reservoir parameters, and the ratio of the cumulative gas production in the first year to the geological reserves of the standard group is used as the benchmark recovery degree. S3. Establish a comparative analysis model, and conduct multiple sets of simulation calculations by changing the parameters of a single reservoir to obtain the functional mapping relationship between each reservoir parameter and the degree of recovery. Specifically, the following steps are included: S31. The reservoir thickness, water saturation and formation permeability are divided into three independent analysis factor groups, and multiple value levels are set for each analysis factor group to form a control parameter matrix. S32. For each analysis factor group, keep other reservoir parameters consistent with the standard reservoir parameters, only change the reservoir parameters corresponding to the current analysis factor group, and establish multiple control group models according to the value levels in the control parameter matrix in sequence. Perform numerical simulation calculations on each control group model to obtain the first-year cumulative gas production and geological reserves of each control group model, and calculate the first-year recovery rate of each control group model. S33. Statistically analyze the first-year production rate of the control group corresponding to each reservoir thickness level, the first-year production rate of the control group corresponding to each water saturation level, and the first-year production rate of the control group corresponding to each formation permeability level. Then, perform function fitting between the value levels and the corresponding first-year production rate of the control group to obtain the functional mapping relationship between each reservoir parameter and the production rate. S4. Based on the function mapping relationship obtained in step S3 and the benchmark sampling degree obtained in step S2, calculate the benchmark difference and establish a comprehensive deviation calculation model. The comprehensive deviation calculation model characterizes the deviation contribution of formation permeability in the form of a logarithmic function and the deviation contribution of water saturation in the form of a polynomial function. Specifically, the following steps are included: S41. Based on the function mapping relationship obtained in step S3, the first-year extraction degree corresponding to each water saturation level is calculated by difference with the benchmark extraction degree to obtain the water saturation benchmark difference sequence. The first-year extraction degree corresponding to each formation permeability level is calculated by difference with the benchmark extraction degree to obtain the formation permeability benchmark difference sequence. S42. Using each level of water saturation as the independent variable and the water saturation benchmark difference sequence as the dependent variable, a polynomial function is fitted to obtain the water saturation deviation contribution function; using each level of formation permeability as the independent variable and the formation permeability benchmark difference sequence as the dependent variable, a logarithmic function is fitted to obtain the formation permeability deviation contribution function. S43. Weight the contribution function of water saturation deviation and the contribution function of formation permeability deviation to establish a comprehensive benchmark difference calculation model.

[0006] S5. Based on the measured reservoir parameters of the target gas well and the comprehensive deviation calculation model established in step S4, the target production degree is calculated in combination with the benchmark production degree. The product of the target gas well and the geological reserves is then normalized over time to generate the post-pressure production capacity prediction value of the target gas well. Specifically, the following steps are included: S51. Obtain the measured water saturation and measured formation permeability of the target gas well, input them into the comprehensive benchmark difference calculation model to obtain the target water saturation deviation contribution value and the target formation permeability deviation contribution value, sum the target water saturation deviation contribution value and the target formation permeability deviation contribution value to obtain the target comprehensive deviation value. S52. Based on the target comprehensive deviation value, the baseline recovery degree of the target gas well is corrected to obtain the corrected recovery degree, and boundary constraint processing is performed to obtain the target recovery degree. Specifically, the following steps are included: S521. Determine the sign of the target comprehensive deviation value. When the target comprehensive deviation value is positive, add the baseline production level of the target gas well to the target comprehensive deviation value in a positive direction to obtain the corrected production level. When the target comprehensive deviation value is negative, cancel the absolute value of the baseline production level of the target gas well to the target comprehensive deviation value in a negative direction to obtain the corrected production level. S522. Establish a nonlinear mapping relationship between the corrected recovery level and the target recovery level, and perform boundary constraint processing on the corrected recovery level; when the corrected recovery level exceeds the physical upper limit threshold, perform upper limit convergence mapping; when the corrected recovery level is lower than the physical lower limit threshold, perform lower limit expansion mapping to obtain the target recovery level; the target recovery level is maintained within a reasonable range allowed by the reservoir physical mechanism through nonlinear boundary mapping. S53. Multiply the target recovery rate by the geological reserves of the target gas well to obtain the cumulative recovery of the target gas well in the first year. Then, perform time normalization on the cumulative recovery of the target gas well in the first year to generate the post-pressure production capacity prediction value of the target gas well.

[0007] A tight gas well post-compression production capacity prediction system based on recovery degree for implementing the aforementioned method includes: The first unit is used to obtain the measured reservoir parameters of the target gas well; The second unit is used to establish a standard group model for numerical simulation. It uses preset standard reservoir parameters to perform simulation calculations to obtain the baseline recovery level, establish a comparative analysis model, and perform multiple sets of simulation calculations by changing a single reservoir parameter to obtain the functional mapping relationship between each reservoir parameter and the recovery level. The third unit is used to calculate the benchmark difference and establish a comprehensive deviation calculation model based on the function mapping relationship and the benchmark extraction degree. The comprehensive deviation calculation model uses a logarithmic function to characterize the deviation contribution of formation permeability and a polynomial function to characterize the deviation contribution of water saturation. The fourth unit is used to calculate the target recovery degree based on the measured reservoir parameters of the target gas well and the comprehensive deviation calculation model, combined with the baseline recovery degree, and then perform time normalization on the product of the target gas well and the geological reserves to generate the post-pressure production capacity prediction value of the target gas well.

[0008] An electronic device, characterized in that it comprises: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0009] A computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0010] The beneficial effects of this invention are: This invention provides a method for predicting the post-fracturing production capacity of tight gas wells based on the degree of recovery. It can directly predict the production capacity level of gas wells after fracturing using measured reservoir parameters, avoiding the drawbacks of traditional methods that require waiting for accurate prediction results. This provides timely and effective technical support for the formulation of gas well development strategies. The method establishes a numerical simulation standard group model and a comparative analysis model to define the functional mapping relationship between reservoir parameters and the degree of recovery. It also constructs a comprehensive deviation calculation model, which can accurately quantify the influence of each reservoir parameter on the degree of recovery, improving the accuracy and scientific validity of the prediction results. In practical applications, this method simplifies the post-fracturing production capacity prediction process for tight gas wells, reduces manual intervention and judgment, and minimizes the influence of subjective factors in the prediction process. Furthermore, time normalization processing makes the prediction results more practical and instructive, providing a reliable data foundation for gas field development decisions. Attached Figure Description

[0011] Figure 1 This is a schematic flowchart of the method of the present invention; Figure 2 This is a schematic diagram illustrating the effect of water saturation on daily gas production in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram illustrating the effect of water saturation on cumulative gas production in Embodiment 1 of the present invention; Figure 4 This is a schematic diagram illustrating the effect of formation permeability on daily gas production in Embodiment 1 of the present invention; Figure 5 This is a schematic diagram illustrating the effect of formation permeability on cumulative gas production in Embodiment 1 of the present invention.

[0012] For those skilled in the art, other related figures can be obtained from the above figures without any creative effort. Detailed Implementation

[0013] To enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0014] Example 1

[0015] like Figure 1 As shown, a method for predicting the post-compression production capacity of tight gas wells based on the degree of recovery includes the following steps: S1. Obtain the measured reservoir parameters of the target gas well; The reservoir parameters include formation permeability, water saturation, and reservoir thickness; S2. Establish a standard group model for numerical simulation, and use preset standard reservoir parameters to perform simulation calculations to obtain the baseline recovery rate; Specifically, the following steps are included: S21. Establish a three-dimensional mesh numerical simulation model; The three-dimensional mesh numerical simulation model includes a first directional mesh along the crack propagation direction, a second directional mesh perpendicular to the crack propagation direction, and a vertical directional mesh. The second direction mesh uses differentiated mesh processing; Differential meshing involves setting a denser mesh near the crack, with a mesh size of 0.1 to 0.5 meters, while the mesh size gradually increases further away from the crack, reaching a maximum of 10 to 50 meters. This approach improves computational efficiency while maintaining accuracy. For example, in a typical model, the first-direction mesh is set to a uniform 20×20 meter mesh of 50 cells, the vertical mesh is set to a thickness of 1 meter of 30 layers, and the second-direction mesh is set to a width of 0.2 meters near the crack, gradually increasing to 20 meters outwards, for a total of 60 cells.

[0016] S22. Set standard reservoir parameters for the three-dimensional mesh numerical simulation model established in step S21, define the gas well production regime, and set the production duration. The standard reservoir parameters include standard formation permeability, standard porosity, standard water saturation, and standard reservoir thickness. In this embodiment, the standard formation permeability is set to 0.1 millidarcy, the standard porosity to 8%, the standard water saturation to 30%, and the standard reservoir thickness to 30 meters. These parameters are typical parameters for compact sandstone gas reservoirs, derived from extensive field data analysis. Simultaneously, the model also needs to set fluid property parameters such as relative permeability curves and capillary pressure curves. The gas-water two-phase relative permeability curve adopts an exponential model, with the gas phase terminal relative permeability set to 0.8, the water phase terminal relative permeability set to 0.2, the gas phase residual saturation to 10%, and the immobile water saturation to 30%.

[0017] A phased production system for gas wells is defined, comprising a constant production phase and a constant bottomhole pressure production phase, with a set production duration. In the constant production phase, the daily production of the gas well is set at 100,000 cubic meters per day. When the bottomhole flowing pressure drops to a set minimum of 5 MPa, the system transitions to the constant bottomhole pressure production phase, maintaining a constant bottomhole flowing pressure of 5 MPa for continued production. The production duration is set at 20 years. Simulation results indicate that the constant production phase typically lasts 3 to 5 years, after which it transitions to the constant bottomhole pressure production phase. This phased production model more realistically reflects actual production conditions. Initially, the gas well has high production capacity, maintaining stable output. As reservoir energy is depleted, production capacity declines, leading to a transition to constant pressure production.

[0018] S23. Based on the three-dimensional grid numerical simulation model established in step S21, the standard reservoir parameters and gas well production system set in step S22, numerical simulation calculations are performed to obtain the curve of cumulative gas production of the standard group over time; the geological reserves of the standard group are calculated according to the standard reservoir parameters, and the ratio of the cumulative gas production in the first year to the geological reserves of the standard group is used as the benchmark recovery rate. In this embodiment, specifically: based on the established three-dimensional grid numerical simulation model, the set standard reservoir parameters and gas well production system, numerical simulation calculations are performed. During the calculation process, the Implicit Pressure Explicit Saturation (IMPES) algorithm is used for solution, and the time step is adaptively adjusted. The initial time step is 0.1 days, and the maximum time step is limited to 30 days. Through simulation calculations, the cumulative gas production curve of the standard group over time is obtained. For example, under the above parameter conditions, the simulation results show that the cumulative gas production in the first year is 36.5 million cubic meters, the cumulative gas production in 5 years is 146 million cubic meters, the cumulative gas production in 10 years is 213 million cubic meters, and the cumulative gas production in 20 years is 275 million cubic meters.

[0019] The standard group geological reserves are calculated based on standard reservoir parameters. The geological reserves calculation takes into account parameters such as the initial pressure, temperature, and gas deviation coefficient of the gas reservoir. In this embodiment, the initial pressure of the gas reservoir is 30 MPa, the temperature is 85°C, and the gas deviation coefficient is 0.92. The calculated standard group geological reserves are 650 million cubic meters.

[0020] The ratio of the first-year cumulative gas production to the standard group geological reserves is used as the baseline recovery rate. In this embodiment, the first-year cumulative gas production is 36.5 million cubic meters, and the standard group geological reserves are 650 million cubic meters, resulting in a baseline recovery rate of 5.62%. This baseline recovery rate can be used as a reference standard for evaluating the development effect of other gas reservoirs.

[0021] By establishing the aforementioned standard model and obtaining the baseline recovery level, a unified reference benchmark can be provided for the development effects of different gas reservoirs. In practical applications, the first-year recovery level of an actual gas reservoir can be compared with this baseline recovery level to evaluate the ease of development and production potential of the actual gas reservoir, providing a scientific basis for formulating gas reservoir development plans.

[0022] S3. Establish a comparative analysis model, and conduct multiple sets of simulation calculations by changing the parameters of a single reservoir to obtain the functional mapping relationship between each reservoir parameter and the degree of recovery. Specifically, the following steps are included: S31. Reservoir thickness, water saturation, and formation permeability are treated as three independent analytical factor groups, with different value levels set for each factor group to form a control parameter matrix. Taking a gas reservoir as an example, the standard reservoir parameters are set as follows: reservoir thickness 10 meters, water saturation 30%, and formation permeability 0.5 millidarcy. The reservoir thickness values ​​are set at 5 meters, 10 meters, 15 meters, 20 meters, and 25 meters; the water saturation values ​​are set at 10%, 20%, 30%, 40%, and 50%; and the formation permeability values ​​are set at 0.1 millidarcy, 0.5 millidarcy, 1.0 millidarcy, 2.0 millidarcy, and 5.0 millidarcy.

[0023] S32. For each analysis factor group, keep other reservoir parameters consistent with the standard reservoir parameters, only change the reservoir parameters corresponding to the current analysis factor group, and establish multiple control group models according to the value levels in the control parameter matrix in sequence. Perform numerical simulation calculations on each control group model to obtain the first-year cumulative gas production and geological reserves of each control group model, and calculate the first-year recovery rate of each control group model. In this embodiment, specifically: For the reservoir thickness as an analytical factor, the water saturation is kept constant at 30%, and the formation permeability is kept constant at 0.5 millidarcy. Only the reservoir thickness parameter is changed, and five control group models are established according to the reservoir thickness levels (5 meters, 10 meters, 15 meters, 20 meters, and 25 meters) in the control parameter matrix. Numerical simulation calculations are performed on each control group model to obtain its respective first-year cumulative gas production. For example, when the reservoir thickness is 5 meters, the simulated first-year cumulative gas production is 1.5 million cubic meters, the geological reserves are 30 million cubic meters, and the calculated first-year recovery rate for this control group is 5.00%; when the reservoir thickness is 10 meters, the first-year cumulative gas production is 4 million cubic meters, the geological reserves are 60 million cubic meters, and the first-year recovery rate is 6.67%; when the reservoir thickness is 15 meters, the first-year cumulative gas production is 68 million cubic meters. The first-year production rate is 7.56% when the reservoir thickness is 0 cubic meters, the geological reserves are 90 million cubic meters, and the recovery rate is 7.56%. When the reservoir thickness is 20 meters, the first-year cumulative gas production is 9.6 million cubic meters, the geological reserves are 120 million cubic meters, and the recovery rate is 8.00%. When the reservoir thickness is 25 meters, the first-year cumulative gas production is 12 million cubic meters, the geological reserves are 150 million cubic meters, and the recovery rate is 8.00%.

[0024] For the water saturation factor group, the reservoir thickness and formation permeability were kept constant at 10 meters and 0.5 millidarcy, respectively. Only the water saturation parameter was changed, establishing five control group models according to the water saturation levels (10%, 20%, 30%, 40%, and 50%) in the control parameter matrix. Numerical simulations showed that when the water saturation was 10%, the first-year cumulative gas production was 5.2 million cubic meters, the geological reserves were 65 million cubic meters, and the first-year recovery rate was 8.00%; when the water saturation was 20%, the first-year cumulative gas production was 4.5 million cubic meters, the geological reserves were 62.5 million cubic meters, and the first-year recovery rate was 7.20%; when the water saturation was 30%, the first-year cumulative gas production was 4 million cubic meters. The gas production capacity is 10,000 cubic meters, with geological reserves of 60 million cubic meters and an extraction rate of 6.67% in the first year. When the water saturation is 40%, the cumulative gas production in the first year is 3.2 million cubic meters, with geological reserves of 57.5 million cubic meters and an extraction rate of 5.57% in the first year. When the water saturation is 50%, the cumulative gas production in the first year is 2.5 million cubic meters, with geological reserves of 55 million cubic meters and an extraction rate of 4.55% in the first year.

[0025] For the formation permeability factor, the reservoir thickness and water saturation were kept constant at 10 meters, with only the formation permeability parameter being changed. Five control group models were established according to the formation permeability values ​​(0.1 mDarcy, 0.5 mDarcy, 1.0 mDarcy, 2.0 mDarcy, and 5.0 mDarcy) in the control parameter matrix. Numerical simulations showed that when the formation permeability was 0.1 mDarcy, the cumulative gas production in the first year was 1.2 million cubic meters, the geological reserves were 60 million cubic meters, and the recovery rate in the first year was 2.00%; when the formation permeability was 0.5 mDarcy, the cumulative gas production in the first year was 4 million cubic meters, the geological reserves were 60 million cubic meters, and the recovery rate in the first year was 6.67%; and when the formation permeability was 1.0 mDarcy, the cumulative gas production in the first year was 6.6 million cubic meters. The first-year production rate is 11.00% when the formation permeability is 2.0 millidarcy, the first-year cumulative gas production is 9.6 million cubic meters, the geological reserves are 60 million cubic meters, and the first-year production rate is 16.00%. When the formation permeability is 5.0 millidarcy, the first-year cumulative gas production is 12 million cubic meters, the geological reserves are 60 million cubic meters, and the first-year production rate is 20.00%.

[0026] S33. Statistically analyze the first-year production rate of the control group corresponding to each reservoir thickness level, the first-year production rate of the control group corresponding to each water saturation level, and the first-year production rate of the control group corresponding to each formation permeability level. Then, perform a correlation analysis between the value levels and the corresponding first-year production rate of the control group to obtain the functional mapping relationship between each reservoir parameter and the production rate. In this embodiment, specifically: after completing the simulation calculation, statistical and correlation analysis is performed on the data of each factor group. For reservoir thickness, the thickness values ​​(5 meters, 10 meters, 15 meters, 20 meters, and 25 meters) are correlated with the corresponding first-year production rates (5.00%, 6.67%, 7.56%, 8.00%, and 8.00%). It is found that the production rate increases with increasing thickness, but tends to stabilize after 20 meters, which can be described as a piecewise function relationship. For water saturation, the saturation values ​​(10%, 20%, 30%, 40%, and 50%) are correlated with the corresponding first-year production rates. Correlation analysis of the extraction degree (8.00%, 7.20%, 6.67%, 5.57%, 4.55%) revealed that the extraction degree decreased with increasing water saturation, exhibiting an approximately linear decreasing trend. For formation permeability, correlation analysis of permeability values ​​(0.1 mDarcy, 0.5 mDarcy, 1.0 mDarcy, 2.0 mDarcy, and 5.0 mDarcy) with the corresponding first-year extraction degree (2.00%, 6.67%, 11.00%, 16.00%, 20.00%) revealed a significant positive correlation between extraction degree and permeability, with an approximately linear increasing trend.

[0027] In a specific case Figure 2 and Figure 3 This paper shows the changes in daily and cumulative gas production under different water saturation conditions. Keeping the reservoir thickness and formation permeability constant, when simulating the effect of water saturation on the recovery rate, the gas well needs to be allowed to stabilize production for a period of time before pressure production is set. Based on the relationship between reservoir thickness and recovery rate, the initial production allocation is set at the daily gas production rate when the recovery rate is 40%. The graph of daily gas production change over time shows that as water saturation increases, the stable production time becomes shorter and shorter, and the production rate will not change much after 1000 days.

[0028] The changes in daily and cumulative gas production over development time under different formation permeability conditions are as follows: Figure 4 and Figure 5 As shown, similar to the simulation of the relationship between water saturation and the first-year production rate, the water saturation and reservoir thickness are kept constant. When simulating the effect of formation permeability, the gas well needs to be allowed to produce steadily for a period before setting the bottom hole pressure for production. Similarly, the initial production allocation is determined based on 40% of the geological reserves. The relationship between daily gas production and time is obtained, and similar to the effect of water saturation, it can be seen that the gas well production will not change significantly after 1000 days.

[0029] Through the above correlation analysis, the functional mapping relationships between reservoir thickness, water saturation, and formation permeability—three reservoir parameters—and the degree of recovery were obtained. These mapping relationships can be used for gas reservoir evaluation and production capacity prediction. This method can also be extended to the influence analysis of other reservoir parameters, such as porosity, gas-water interface location, and formation pressure, providing quantitative basis for optimizing gas reservoir development schemes.

[0030] S4. Based on the function mapping relationship obtained in step S3 and the benchmark sampling degree obtained in step S2, calculate the benchmark difference and establish a comprehensive deviation calculation model. The comprehensive deviation calculation model characterizes the deviation contribution of formation permeability in the form of a logarithmic function and the deviation contribution of water saturation in the form of a polynomial function. Specifically, the following steps are included: S41. Based on the function mapping relationship obtained in step S3, the first-year extraction degree corresponding to each water saturation level is calculated by difference with the benchmark extraction degree to obtain the water saturation benchmark difference sequence. The first-year extraction degree corresponding to each formation permeability level is calculated by difference with the benchmark extraction degree to obtain the formation permeability benchmark difference sequence. S42. Using each level of water saturation as the independent variable and the water saturation benchmark difference sequence as the dependent variable, a polynomial function is fitted to obtain the water saturation deviation contribution function; using each level of formation permeability as the independent variable and the formation permeability benchmark difference sequence as the dependent variable, a logarithmic function is fitted to obtain the formation permeability deviation contribution function. S43. Weight the contribution function of water saturation deviation and the contribution function of formation permeability deviation to establish a comprehensive benchmark difference calculation model.

[0031] In this embodiment, the specific steps are as follows: In practical applications, the water saturation and formation permeability parameters of the oilfield block are obtained. Using reservoir simulation software, numerical simulation of the reservoir is performed for different combinations of water saturation and formation permeability values ​​to obtain first-year recovery data for each parameter combination. For example, water saturation can be set to five values: 0.15, 0.25, 0.35, 0.45, and 0.55, and formation permeability can be set to five values: 10mD, 50mD, 100mD, 500mD, and 1000mD. This forms a 5×5 parameter combination matrix. Through reservoir simulation, 25 sets of first-year recovery data can be obtained, establishing a functional mapping relationship between water saturation, formation permeability, and first-year recovery.

[0032] When determining the baseline recovery level, the first-year recovery level corresponding to the intermediate combination of water saturation and formation permeability can be selected as the benchmark point. For example, when the water saturation is 0.35 and the formation permeability is 100mD, the first-year recovery level is 7.2%, and this value is set as the baseline recovery level.

[0033] In calculating the baseline difference, for each water saturation level, the difference between the corresponding first-year extraction rate and the baseline extraction rate is calculated. For example, the first-year extraction rate at a water saturation of 0.15 is 9.8%, with a difference of 2.6% from the baseline extraction rate of 7.2%; the first-year extraction rate at a water saturation of 0.25 is 8.5%, with a difference of 1.3%; the first-year extraction rate at a water saturation of 0.45 is 5.9%, with a difference of -1.3%; and the first-year extraction rate at a water saturation of 0.55 is 4.6%, with a difference of -2.6%. This results in a water saturation baseline difference sequence of [2.6%, 1.3%, 0%, -1.3%, -2.6%].

[0034] Similarly, for each value of formation permeability, the difference between the corresponding first-year extraction rate and the benchmark extraction rate is calculated. For example, the first-year extraction rate is 2.8% when the formation permeability is 10 mD, and the difference between it and the benchmark extraction rate of 7.2% is -4.4%; the first-year extraction rate is 5.6% when the formation permeability is 50 mD, and the difference is -1.6%; the first-year extraction rate is 8.9% when the formation permeability is 500 mD, and the difference is 1.7%; the first-year extraction rate is 10.3% when the formation permeability is 1000 mD, and the difference is 3.1%. This yields the formation permeability benchmark difference sequence as [-4.4%, -1.6%, 0%, 1.7%, 3.1%].

[0035] When establishing the water saturation deviation contribution function, the water saturation values ​​[0.15, 0.25, 0.35, 0.45, 0.55] are used as independent variables, and the corresponding benchmark difference sequence [2.6%, 1.3%, 0%, -1.3%, -2.6%] are used as dependent variables. A polynomial function fitting is performed, using a cubic polynomial fitting method. The resulting water saturation deviation contribution function is expressed as: the water saturation deviation contribution value equals the cube of the water saturation multiplied by coefficient A, plus the square of the water saturation multiplied by coefficient B, plus the first power of the water saturation multiplied by coefficient C, plus a constant term D. Through fitting calculations, A = -120.5, B = 138.6, C = -53.2, and D = 6.8. This function can be used to calculate the deviation contribution of any water saturation value to the first-year extraction rate. For example, when the water saturation is 0.30, substituting into the function yields a deviation contribution of 0.65%.

[0036] When establishing the formation permeability deviation contribution function, the formation permeability values ​​[10, 50, 100, 500, 1000] are used as independent variables, and the corresponding baseline difference sequence [-4.4%, -1.6%, 0%, 1.7%, 3.1%] is used as the dependent variable, and a logarithmic function is fitted. The resulting formation permeability deviation contribution function is expressed as follows: the formation permeability deviation contribution value equals the natural logarithm of the formation permeability multiplied by a coefficient E, plus a constant term F. Through fitting calculations, E = 1.25 and F = -5.75. This function can be used to calculate the deviation contribution of any formation permeability value to the first-year production rate. For example, when the formation permeability is 200 mD, the deviation contribution calculated using the function is 0.85%.

[0037] When establishing a comprehensive deviation calculation model, it is necessary to determine the weighting coefficients of the water saturation deviation contribution function and the formation permeability deviation contribution function. These weighting coefficients can be determined by analyzing the influence of these two parameters on the first-year production rate. For example, through sensitivity analysis, the weighting coefficient for water saturation is determined to be 0.4, and the weighting coefficient for formation permeability is 0.6.

[0038] The comprehensive deviation calculation model is expressed as follows: the comprehensive deviation value equals the calculated result of the water saturation deviation contribution function multiplied by the water saturation weighting coefficient, plus the calculated result of the formation permeability deviation contribution function multiplied by the formation permeability weighting coefficient. For example, for a parameter combination with a water saturation of 0.30 and a formation permeability of 200 mD, the water saturation deviation contribution is 0.65%, and the formation permeability deviation contribution is 0.85%. Substituting these values ​​into the comprehensive deviation calculation model yields: 0.65% × 0.4 + 0.85% × 0.6 = 0.77%. This indicates that the expected deviation of the first-year production rate relative to the baseline production rate under this parameter combination is 0.77%, i.e., the expected first-year production rate is 7.2% + 0.77% = 7.97%.

[0039] The comprehensive deviation calculation model established by the above methods can accurately predict the first-year recovery rate under different combinations of water saturation and formation permeability, providing a scientific basis for optimizing oilfield development plans.

[0040] S5. Based on the measured reservoir parameters of the target gas well and the comprehensive deviation calculation model established in step S4, the target production degree is calculated in combination with the benchmark production degree. The product of the target gas well and the geological reserves is then normalized over time to generate the post-pressure production capacity prediction value of the target gas well. Specifically, the following steps are included: S51. Obtain the measured water saturation and measured formation permeability of the target gas well, input them into the comprehensive benchmark difference calculation model to obtain the target water saturation deviation contribution value and the target formation permeability deviation contribution value, sum the target water saturation deviation contribution value and the target formation permeability deviation contribution value to obtain the target comprehensive deviation value. S52. Based on the target comprehensive deviation value, the baseline recovery degree of the target gas well is corrected to obtain the corrected recovery degree, and boundary constraint processing is performed to obtain the target recovery degree. Specifically, the following steps are included: S521. Determine the sign of the target comprehensive deviation value. When the target comprehensive deviation value is positive, add the baseline production level of the target gas well to the target comprehensive deviation value in a positive direction to obtain the corrected production level. When the target comprehensive deviation value is negative, cancel the absolute value of the baseline production level of the target gas well to the target comprehensive deviation value in a negative direction to obtain the corrected production level. S522. Establish a nonlinear mapping relationship between the corrected recovery level and the target recovery level, and perform boundary constraint processing on the corrected recovery level; when the corrected recovery level exceeds the physical upper limit threshold, perform upper limit convergence mapping; when the corrected recovery level is lower than the physical lower limit threshold, perform lower limit expansion mapping to obtain the target recovery level; the target recovery level is maintained within a reasonable range allowed by the reservoir physical mechanism through nonlinear boundary mapping. In this embodiment, the specific steps are as follows: the baseline production level of the target gas well is corrected according to the target comprehensive deviation value to obtain the corrected production level, and boundary constraint processing is performed to obtain the target production level. The correction process includes two main steps: positive and negative judgment and superposition processing of the comprehensive deviation value, and nonlinear boundary constraint processing.

[0041] In the first step, the sign of the target comprehensive deviation value is determined. For a target gas well, assuming its baseline recovery rate is 0.45 (i.e., 45%), the target comprehensive deviation value is +0.08 (i.e., +8%). Since the target comprehensive deviation value is positive, it indicates that the actual condition of the gas well is more favorable for gas production than the baseline condition. Therefore, the baseline recovery rate and the target comprehensive deviation value need to be positively superimposed. Specifically, the calculation is: 0.45 + 0.08 = 0.53, resulting in a corrected recovery rate of 0.53 (i.e., 53%).

[0042] If the baseline recovery rate of another target gas well is 0.60 (60%), the target comprehensive deviation value is -0.15 (-15%). Since the target comprehensive deviation value is negative, it indicates that the actual situation of the gas well is more unfavorable for gas production than the baseline situation. Therefore, the absolute values ​​of the baseline recovery rate and the target comprehensive deviation value need to be offset inversely. The specific calculation is: 0.60 - 0.15 = 0.45, resulting in a corrected recovery rate of 0.45 (45%).

[0043] In the second step, a nonlinear mapping relationship is established between the corrected recovery level and the target recovery level. Boundary constraints are applied to the corrected recovery level. In gas reservoir development, there are physical upper and lower thresholds for the recovery level. Assume the upper threshold is 0.85 (i.e., 85%) and the lower threshold is 0.20 (i.e., 20%).

[0044] For the first gas well example above, the corrected recovery rate is 0.53, which does not exceed the upper limit threshold of 0.85, nor is it lower than the lower limit threshold of 0.20. Therefore, no boundary constraint processing is required, and the target recovery rate is 0.53.

[0045] Suppose there is a third gas well whose corrected recovery rate is 0.92 (92%), exceeding the physical upper limit threshold of 0.85. In this case, upper limit convergence mapping is required. The calculation method for nonlinear upper limit convergence mapping is as follows: the excess portion (0.92 - 0.85 = 0.07) is nonlinearly compressed, resulting in a value of 0.04. The final target recovery rate is 0.85 + 0.04 = 0.89 (89%). This nonlinear compression can be achieved by setting an asymptote to a function with the upper limit threshold, ensuring that the closer the corrected recovery rate is to the upper limit threshold, the more the excess portion is compressed, thus preventing the final result from infinitely approaching the theoretical upper limit value.

[0046] Similarly, suppose the corrected recovery rate of the fourth gas well is calculated to be 0.15 (i.e., 15%), which is lower than the physical lower limit threshold of 0.20. In this case, a lower limit expansion mapping is required. The calculation method for the nonlinear lower limit expansion mapping is as follows: the insufficient portion (0.20-0.15=0.05) is nonlinearly expanded to 0.03, and the final target recovery rate is 0.20-0.03=0.17 (i.e., 17%). This nonlinear expansion can be achieved by setting the asymptote as a function of the lower limit threshold, so that the closer the corrected recovery rate is to the lower limit threshold, the less its insufficient portion is expanded, ensuring that the final result does not infinitely approach the theoretical lower limit value.

[0047] Through the above nonlinear boundary mapping process, it can be ensured that the target recovery level is kept within a reasonable range allowed by the reservoir physical mechanism, while also reflecting the influence of actual gas reservoir conditions on the recovery level.

[0048] This method can also be extended to different types of gas reservoirs. For example, for low-permeability gas reservoirs, an upper threshold of 0.70 (70%) and a lower threshold of 0.15 (15%) can be set; for high-permeability gas reservoirs, an upper threshold of 0.90 (90%) and a lower threshold of 0.25 (25%) can be set. By adjusting the upper and lower thresholds for different gas reservoir types, the target recovery rate can be made more consistent with the physical characteristics of the actual gas reservoir.

[0049] To verify the effectiveness of this method, 50 gas wells from a gas field were selected for testing. Of these, 42 wells had recovery rates within the boundary constraints after correction, requiring no boundary mapping. Five wells exceeded the upper threshold; after upper limit convergence mapping, their recovery rates decreased by an average of 0.05 (5 percentage points). Three wells were below the lower threshold; after lower limit expansion mapping, their recovery rates increased by an average of 0.02 (2 percentage points). Compared to traditional methods, this method yields target recovery rates that better reflect the actual development patterns of gas reservoirs, improving prediction accuracy by over 15%.

[0050] S53. Multiply the target recovery rate by the geological reserves of the target gas well to obtain the cumulative recovery of the target gas well in the first year. Then, perform time normalization on the cumulative recovery of the target gas well in the first year to generate the post-pressure production capacity prediction value of the target gas well.

[0051] In this embodiment, specifically, it is necessary to obtain the measured water saturation and measured formation permeability of the target gas well. The measured water saturation can be obtained through well logging data, such as resistivity logging or nuclear magnetic resonance logging; the measured formation permeability can be obtained through core analysis or formation testing. Assume that the measured water saturation of a target gas well is 45%, and the measured formation permeability is 25 millidarcy.

[0052] The acquired measured data are input into the comprehensive benchmark difference calculation model, which is a regression model established based on historical production well data, used to quantify the impact of reservoir parameter deviations on the degree of recovery. In this embodiment, the comprehensive benchmark difference calculation model consists of two parts: calculation of the contribution of water saturation deviation and calculation of the contribution of formation permeability deviation.

[0053] To calculate the contribution value of water saturation deviation, the model calculates the difference between the measured water saturation of the target gas well and the reference water saturation. Assuming the reference water saturation is 38%, the deviation is 45% - 38% = 7%. This deviation is converted using the model's weighting coefficients to obtain the water saturation deviation contribution value. Assuming that the weighting coefficient corresponding to each 1% deviation in water saturation in the model is -0.8, the contribution value of the target water saturation deviation is 7% × (-0.8) = -5.6%.

[0054] To calculate the contribution value of formation permeability deviation, the model calculates the logarithm of the ratio of the measured formation permeability of the target gas well to the benchmark formation permeability. Assuming the benchmark formation permeability is 35 millidarcy, the ratio is 25 / 35 = 0.714, and the logarithm is approximately -0.146. This logarithmic value is converted using the model's weighting coefficients to obtain the formation permeability deviation contribution value. Assuming the weighting coefficient for the logarithmic deviation of formation permeability in the model is 6.5, the contribution value of the target formation permeability deviation is -0.146 × 6.5 = -0.949%.

[0055] The target comprehensive deviation value is obtained by summing the contribution values ​​of water saturation deviation and formation permeability deviation. In this example, the target comprehensive deviation value is -5.6% + (-0.949%) = -6.549%.

[0056] Based on the calculated target comprehensive deviation value, the baseline recovery rate of the target gas well is corrected. Assuming that the baseline recovery rate of the gas well in this area is 65%, the corrected recovery rate is 65%×(1+(-6.549%))=65%×0.93451=60.74%.

[0057] The corrected recovery level is subject to boundary constraints to ensure it remains within a reasonable range. In this embodiment, the reasonable range for recovery level is set to 20% to 85%. Since 60.74% falls within this range, no adjustment is needed, so the target recovery level is 60.74%.

[0058] Multiply the target recovery rate by the geological reserves of the target gas well to calculate the cumulative recovery volume of the target gas well in the first year. Assuming the geological reserves of the target gas well are 80 million cubic meters, the cumulative recovery volume in the first year would be 80 million × 60.74% = 48.592 million cubic meters.

[0059] The cumulative production volume in the first year is time-normalized to generate the post-compression production capacity forecast for the target gas well. Time normalization refers to converting the annual cumulative production volume into daily average production capacity. In this embodiment, considering that the production capacity after the gas well is put into production is not uniformly distributed, a decreasing curve is used for time normalization. Assuming that the gas well follows an exponential decreasing law after production, and the first month's production is 15% of the annual production, then the first month's production is 48.592 million × 15% = 7.2888 million cubic meters, and the daily average production capacity is 7.2888 million ÷ 30 days = 242,960 cubic meters / day.

[0060] Through the above steps, the post-compression production capacity of the target gas well was successfully predicted to be 242,960 cubic meters per day. This predicted value can be used for gas field development scheme design, production capacity construction planning, and economic evaluation.

[0061] In practical applications, the parameters and weighting coefficients of the comprehensive benchmark difference calculation model can be adjusted according to different gas reservoir characteristics to improve prediction accuracy. Furthermore, the post-fracturing production capacity prediction results can be further optimized by incorporating factors such as geological structure and reservoir heterogeneity. This method enables the scientific prediction of post-fracturing production capacity before gas well fracturing stimulation, providing strong support for gas field development decisions.

[0062] Example 2

[0063] A tight gas well post-compression production capacity prediction system based on recovery degree for implementing the method of Example 1 includes: The first unit is used to obtain the measured reservoir parameters of the target gas well, including formation permeability, water saturation and reservoir thickness. The second unit is used to establish a standard group model for numerical simulation. It uses preset standard reservoir parameters to perform simulation calculations to obtain the baseline recovery level, establish a comparative analysis model, and perform multiple sets of simulation calculations by changing a single reservoir parameter to obtain the functional mapping relationship between each reservoir parameter and the recovery level. The third unit is used to calculate the benchmark difference and establish a comprehensive deviation calculation model based on the function mapping relationship and the benchmark extraction degree. The comprehensive deviation calculation model uses a logarithmic function to characterize the deviation contribution of formation permeability and a polynomial function to characterize the deviation contribution of water saturation. The fourth unit is used to calculate the target recovery degree based on the measured reservoir parameters of the target gas well and the comprehensive deviation calculation model, combined with the baseline recovery degree, and then perform time normalization on the product of the target gas well and the geological reserves to generate the post-pressure production capacity prediction value of the target gas well.

[0064] Example 3

[0065] An electronic device, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method of Embodiment 1.

[0066] Example 4

[0067] A computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the method described in Embodiment 1.

[0068] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0069] The applicant declares that the above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention fall within the protection and disclosure scope of the present invention.

Claims

1. A method for predicting post-compression production capacity of tight gas wells based on recovery degree, characterized in that: Includes the following steps: S1. Obtain the measured reservoir parameters of the target gas well; S2. Establish a standard group model for numerical simulation, and use preset standard reservoir parameters to perform simulation calculations to obtain the baseline recovery rate; S3. Establish a comparative analysis model, and conduct multiple sets of simulation calculations by changing the parameters of a single reservoir to obtain the functional mapping relationship between each reservoir parameter and the degree of recovery. S4. Based on the function mapping relationship obtained in step S3 and the benchmark sampling degree obtained in step S2, calculate the benchmark difference and establish a comprehensive deviation calculation model. S5. Based on the measured reservoir parameters of the target gas well and the comprehensive deviation calculation model established in step S4, the target production level is calculated in combination with the benchmark production level. The product of the target gas well and the geological reserves is then normalized over time to generate the post-pressure production capacity prediction value of the target gas well.

2. The method for predicting post-compression production capacity of tight gas wells based on recovery degree according to claim 1, characterized in that: The reservoir parameters include formation permeability, water saturation, and reservoir thickness.

3. The method for predicting post-compression production capacity of tight gas wells based on recovery degree according to claim 1, characterized in that: Step S2 specifically includes the following steps: S21. Establish a three-dimensional mesh numerical simulation model; The three-dimensional mesh numerical simulation model includes a first directional mesh along the crack propagation direction, a second directional mesh perpendicular to the crack propagation direction, and a vertical directional mesh. The second direction mesh uses differentiated mesh processing; S22. Set standard reservoir parameters for the three-dimensional mesh numerical simulation model established in step S21, define the gas well production regime, and set the production duration. The standard reservoir parameters include standard formation permeability, standard porosity, standard water saturation, and standard reservoir thickness. The gas well production system is a phased production model that includes a fixed production stage and a fixed bottom pressure production stage. S23. Based on the three-dimensional grid numerical simulation model established in step S21, the standard reservoir parameters and gas well production system set in step S22 are substituted into the three-dimensional grid numerical simulation model for calculation to obtain the curve of cumulative gas production of the standard group over time; the geological reserves of the standard group are calculated according to the standard reservoir parameters, and the ratio of the cumulative gas production in the first year to the geological reserves of the standard group is used as the benchmark recovery degree.

4. The method for predicting post-compression production capacity of tight gas wells based on recovery degree according to claim 1, characterized in that: Step S3 specifically includes the following steps: S31. The reservoir thickness, water saturation and formation permeability are divided into three independent analysis factor groups, and multiple value levels are set for each analysis factor group to form a control parameter matrix. S32. For each analysis factor group, keep other reservoir parameters consistent with the standard reservoir parameters, only change the reservoir parameters corresponding to the current analysis factor group, and establish multiple control group models according to the value levels in the control parameter matrix in sequence. Perform numerical simulation calculations on each control group model to obtain the first-year cumulative gas production and geological reserves of each control group model, and calculate the first-year recovery rate of each control group model. S33. Statistically analyze the first-year production rate of the control group corresponding to each reservoir thickness level, the first-year production rate of the control group corresponding to each water saturation level, and the first-year production rate of the control group corresponding to each formation permeability level. Then, perform function fitting between the value levels and the corresponding first-year production rate of the control group to obtain the functional mapping relationship between each reservoir parameter and the production rate.

5. The method for predicting post-compression production capacity of tight gas wells based on recovery degree according to claim 1, characterized in that: Step S4 specifically includes the following steps: S41. Based on the function mapping relationship obtained in step S3, the first-year extraction degree corresponding to each water saturation level is calculated by difference with the benchmark extraction degree to obtain the water saturation benchmark difference sequence. The first-year extraction degree corresponding to each formation permeability level is calculated by difference with the benchmark extraction degree to obtain the formation permeability benchmark difference sequence. S42. Using each level of water saturation as the independent variable and the water saturation benchmark difference sequence as the dependent variable, a polynomial function is fitted to obtain the water saturation deviation contribution function; using each level of formation permeability as the independent variable and the formation permeability benchmark difference sequence as the dependent variable, a logarithmic function is fitted to obtain the formation permeability deviation contribution function. S43. Weight the contribution function of water saturation deviation and the contribution function of formation permeability deviation to establish a comprehensive benchmark difference calculation model. The comprehensive deviation calculation model characterizes the deviation contribution of formation permeability in the form of a logarithmic function and the deviation contribution of water saturation in the form of a polynomial function.

6. The method for predicting post-compression production capacity of tight gas wells based on recovery degree according to claim 1, characterized in that: Step S5 specifically includes the following steps: S51. Obtain the measured water saturation and measured formation permeability of the target gas well, input them into the comprehensive benchmark difference calculation model to obtain the target water saturation deviation contribution value and the target formation permeability deviation contribution value, sum the target water saturation deviation contribution value and the target formation permeability deviation contribution value to obtain the target comprehensive deviation value. S52. Based on the target comprehensive deviation value, the baseline recovery degree of the target gas well is corrected to obtain the corrected recovery degree, and boundary constraint processing is performed to obtain the target recovery degree. S53. Multiply the target recovery rate by the geological reserves of the target gas well to obtain the cumulative recovery of the target gas well in the first year. Then, perform time normalization on the cumulative recovery of the target gas well in the first year to generate the post-pressure production capacity prediction value of the target gas well.

7. The method for predicting post-compression production capacity of tight gas wells based on recovery degree according to claim 51, characterized in that: Step S52 specifically includes the following steps: S521. Determine the sign of the target comprehensive deviation value. When the target comprehensive deviation value is positive, add the baseline production level of the target gas well to the target comprehensive deviation value in a positive direction to obtain the corrected production level. When the target comprehensive deviation value is negative, cancel the absolute value of the baseline production level of the target gas well to the target comprehensive deviation value in a negative direction to obtain the corrected production level. S522. Establish a nonlinear mapping relationship between the corrected recovery level and the target recovery level, and perform boundary constraint processing on the corrected recovery level; when the corrected recovery level exceeds the physical upper limit threshold, perform upper limit convergence mapping; when the corrected recovery level is lower than the physical lower limit threshold, perform lower limit expansion mapping to obtain the target recovery level; the target recovery level is maintained within a reasonable range allowed by the reservoir physical mechanism through nonlinear boundary mapping.

8. A tight gas well post-compression production capacity prediction system based on recovery degree for implementing the method as described in any one of claims 1 to 7, characterized in that, include: The first unit is used to obtain the measured reservoir parameters of the target gas well; The second unit is used to establish a standard group model for numerical simulation. It uses preset standard reservoir parameters to perform simulation calculations to obtain the baseline recovery level, establish a comparative analysis model, and perform multiple sets of simulation calculations by changing a single reservoir parameter to obtain the functional mapping relationship between each reservoir parameter and the recovery level. The third unit is used to calculate the benchmark difference and establish a comprehensive deviation calculation model based on the function mapping relationship and the benchmark extraction degree. The comprehensive deviation calculation model uses a logarithmic function to characterize the deviation contribution of formation permeability and a polynomial function to characterize the deviation contribution of water saturation. The fourth unit is used to calculate the target recovery degree based on the measured reservoir parameters of the target gas well and the comprehensive deviation calculation model, combined with the baseline recovery degree, and then perform time normalization on the product of the target gas well and the geological reserves to generate the post-pressure production capacity prediction value of the target gas well.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method of any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method of any one of claims 1 to 7.

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