Artificial intelligence production dynamic simulation method and system for coalbed methane well

Abstract

This invention provides an artificial intelligence-based production dynamic simulation method and system for coalbed methane wells, relating to the field of artificial intelligence technology. First, it receives coalbed methane well production-related data carrying production stage and spatial location identifiers. Then, it analyzes the data through a reservoir production collaborative sensing network to mine the coupling relationship between reservoir geology and production operations, generating reservoir production coupling characteristics. Next, it inputs these reservoir production coupling characteristics into an artificial intelligence production dynamic simulation model to dynamically simulate the entire lifecycle production process, obtaining preliminary production dynamic simulation results. Then, it introduces actual boundary conditions through a production constraint feedback mechanism to adjust the preliminary production dynamic simulation results, obtaining the target production dynamic simulation results. Finally, it outputs the target production dynamic simulation results and the direction for production parameter optimization. This invention improves the accuracy, adaptability, and practicality of coalbed methane well production dynamic simulation.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and more specifically, to an artificial intelligence-based dynamic simulation method and system for coalbed methane well production. Background Technology

[0002] In the field of coalbed methane development, accurate simulation of the production dynamics of coalbed methane wells is crucial for optimizing production strategies, improving recovery rates, and reducing development costs. The production process of coalbed methane wells is influenced by a variety of complex factors, including reservoir geological conditions, production operations, fluid properties, and wellbore structure.

[0003] Currently, existing methods for simulating coalbed methane well production dynamics have many limitations. Some methods simulate only simplified reservoir models and fixed production parameters, ignoring the complex coupling relationship between reservoir geological conditions and production operations, leading to significant deviations between simulation results and actual production conditions. Other methods, while considering some factors, lack the ability to dynamically extrapolate the entire lifecycle of the production process, failing to accurately predict trends at different production stages. Furthermore, existing methods often do not adequately consider the constraints of actual production boundary conditions during the simulation process, making the simulation results lack operability and adaptability in practical applications. Summary of the Invention

[0004] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide an artificial intelligence-based dynamic simulation method for coalbed methane well production, the method comprising:

[0005] Receive coalbed methane well production-related data, which includes coalbed methane well reservoir geological data, production operation data, fluid property data and wellbore structure data, and all data carry corresponding production stage identifiers and spatial location identifiers;

[0006] By analyzing the coalbed methane well production-related data through the reservoir production collaborative sensing network, the coupling relationship between reservoir geological conditions and production operation behavior is explored, and reservoir production coupling characteristics are generated. These reservoir production coupling characteristics include information on the interaction between reservoir parameter changes and production parameter adjustments.

[0007] The reservoir production coupling characteristics are input into the artificial intelligence production dynamic simulation model. By combining the fluid migration law of coalbed methane wells with the response characteristics of the production system, the entire life cycle production process of coalbed methane wells is dynamically simulated to obtain preliminary production dynamic simulation results.

[0008] By introducing actual production boundary conditions of coalbed methane wells through a production constraint feedback mechanism, the preliminary production dynamic simulation results are adaptively adjusted to correct the deviation between the simulation results and the boundary conditions, thereby obtaining the target production dynamic simulation results.

[0009] Output the dynamic simulation results of the target production and the corresponding optimization directions for production parameters.

[0010] Furthermore, embodiments of the present invention also provide an artificial intelligence-based dynamic simulation system for coalbed methane well production, comprising:

[0011] A processor; a machine-readable storage medium for storing machine-executable instructions of the processor; wherein the processor is configured to execute the above-described artificial intelligence production dynamic simulation method for coalbed methane wells by executing the machine-executable instructions.

[0012] In another aspect, embodiments of the present invention also provide a computer program product, the computer program product including machine-executable instructions stored in a computer-readable storage medium, a processor of an artificial intelligence production dynamic simulation system for coalbed methane wells reading the machine-executable instructions from the computer-readable storage medium, the processor executing the machine-executable instructions, causing the artificial intelligence production dynamic simulation system for coalbed methane wells to execute the aforementioned artificial intelligence production dynamic simulation method for coalbed methane wells.

[0013] Based on the above, by receiving coalbed methane well production-related data containing reservoir geological data, production operation data, fluid property data, and wellbore structure data, along with production stage and spatial location identifiers, and using a reservoir production collaborative sensing network to analyze the data, the coupling relationship between reservoir geological conditions and production operation behavior is mined. This generates reservoir production coupling characteristics containing information on the interaction between reservoir parameter changes and production parameter adjustments. These characteristics are then input into an artificial intelligence production dynamic simulation model. Combining the fluid migration patterns of coalbed methane wells with the response characteristics of the production system, a dynamic simulation of the entire lifecycle production process is performed, yielding preliminary production dynamic simulation results. This model can accurately predict the changing trends at different production stages. By introducing actual production boundary conditions through a production constraint feedback mechanism, the preliminary simulation results are adaptively adjusted, correcting deviations between the simulation results and the boundary conditions, resulting in the target production dynamic simulation results. This enhances the operability and adaptability of the simulation results in actual production. Finally, the target production dynamic simulation results and corresponding production parameter optimization directions are output, which helps improve coalbed methane recovery and development efficiency, and improves the accuracy of coalbed methane well production dynamic simulation. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the execution flow of the artificial intelligence production dynamic simulation method for coalbed methane wells provided in an embodiment of the present invention.

[0015] Figure 2This is a schematic diagram of exemplary hardware and software components of an artificial intelligence production dynamic simulation system for coalbed methane wells provided in an embodiment of the present invention. Detailed Implementation

[0016] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating an artificial intelligence production dynamic simulation method for coalbed methane wells according to an embodiment of the present invention. The following is a detailed description of this artificial intelligence production dynamic simulation method for coalbed methane wells.

[0017] Step S110: Receive coalbed methane well production-related data, which includes coalbed methane well reservoir geological data, production operation data, fluid property data and wellbore structure data. All data carries corresponding production stage identifiers and spatial location identifiers.

[0018] In this embodiment, the production process of a coalbed methane well in a specific area is used as the application scenario. Various relevant data generated during the production process of the coalbed methane well are continuously received. This data covers multiple aspects, including: coalbed methane well reservoir geological data, which mainly includes information reflecting reservoir characteristics, such as the pore distribution and permeability of different areas; production operation data, which involves various operational parameters in daily production, such as pressure control values ​​and the amount of produced liquid during gas production; fluid property data, which includes the physical properties of the fluid flowing in the reservoir, such as viscosity and mass per unit volume; and wellbore structure data, which reflects the structural characteristics of the wellbore itself, such as the diameter and casing depth. Furthermore, each data item clearly carries a corresponding production stage identifier, which clearly indicates the production stage to which the data belongs, such as the initial drainage stage, stable production stage, or declining production stage. It also includes a spatial location identifier to indicate the specific reservoir area or wellbore location corresponding to the data.

[0019] Step S120: Analyze the coalbed methane well production-related data through the reservoir production collaborative sensing network, explore the coupling relationship between reservoir geological conditions and production operation behavior, and generate reservoir production coupling features. The reservoir production coupling features include the interaction information between reservoir parameter changes and production parameter adjustments.

[0020] Step S121: Divide the coalbed methane well production-related data into reservoir geology data subsets, production operation data subsets, fluid property data subsets, and wellbore structure data subsets according to data attributes. Each data subset retains complete production stage identifiers and spatial location identifiers.

[0021] In the aforementioned application scenario, the received coalbed methane well production-related data is categorized and processed. Based on the inherent characteristics of the data, it is divided into four distinct subsets. The reservoir geology subset specifically collects data related to reservoir geology, such as measurements of reservoir porosity and permeability test results. The production operation subset summarizes operational parameter data during the production process, such as gas production pressure records at different time points and statistical data on fluid production. The fluid property subset contains data on various physical properties of the fluid, such as measured viscosity and density. The wellbore structure subset integrates structural information data about the wellbore, such as diameter data at different locations and casing depth data. During the categorization process, it is ensured that each data subset fully retains its original production stage and spatial location identifiers, enabling accurate tracing of the data's source, stage, and location during subsequent analysis.

[0022] Step S122: Extract the core attribute features of each data subset through the attribute analysis layer of the reservoir production collaborative sensing network. The core attribute features of the reservoir geology data subset reflect the distribution characteristics of reservoir porosity and permeability. The core attribute features of the production operation data subset reflect the regulation characteristics of gas production pressure and liquid production. The core attribute features of the fluid property data subset reflect the physical characteristics of fluid viscosity and density. The core attribute features of the wellbore structure data subset reflect the structural characteristics of wellbore diameter and casing depth.

[0023] The attribute analysis layer of the reservoir production collaborative sensing network extracts core attribute features from each partitioned data subset. For the reservoir geological data subset, this attribute analysis layer analyzes information that reflects the distribution characteristics of reservoir porosity and permeability. Through the analysis of a large amount of porosity and permeability data, it extracts core attribute features reflecting the overall distribution pattern, such as the distribution pattern of porosity in different regions and the trend of permeability changes. For the production operation data subset, the attribute analysis layer focuses on the regulation characteristics of gas production pressure and liquid production. From the adjustment records of gas production pressure and the change data of liquid production, it extracts core attribute features that reflect the regulation pattern and effect, such as the regulation amplitude and frequency of gas production pressure and the change of liquid production with regulation. For the fluid property data subset, the extraction of core attribute features revolves around the physical properties of fluid viscosity and density. By analyzing fluid viscosity and density data under different conditions, core attributes that can represent its physical properties are obtained, such as the characteristics of viscosity change with temperature or pressure, and the stable range of density. For the wellbore structure data subset, the core attribute features mainly reflect the structural characteristics of wellbore diameter and casing depth. From the measurement data of wellbore diameter and the records of casing depth, core attributes that reflect the characteristics of wellbore structure are extracted, such as the variation law of wellbore diameter and the correspondence between casing depth and reservoir location.

[0024] Step S123: Based on the extracted core attribute features, construct attribute association chains within each data subset. The attribute association chains reflect the mutual influence relationships between different attributes within the same data subset. The attribute association chains of reservoir geology data subsets reflect the relationship between porosity and permeability, while the attribute association chains of production operation data subsets reflect the relationship between gas production pressure and liquid production.

[0025] After extracting the core attribute features of each data subset, the next step is to construct attribute correlation chains within each subset. Taking the reservoir geology data subset as an example, porosity and permeability are two important core attribute features. Analysis of a large amount of historical data revealed that porosity affects permeability; generally, areas with higher porosity tend to have higher permeability. Based on this mutual influence, an attribute correlation chain between porosity and permeability is constructed. This chain can demonstrate the relationship between the two, i.e., how permeability changes when porosity changes. For the production operation data subset, gas production pressure and liquid production are key core attribute features. During production, adjustments to gas production pressure directly affect the amount of liquid produced. For example, when gas production pressure increases to a certain level, liquid production may decrease. Based on the aforementioned mutual influence, an attribute correlation chain between gas production pressure and liquid production is constructed to reflect the correlation pattern between the two.

[0026] Step S124: Through the cross-set association mining unit of the reservoir production collaborative sensing network, analyze the attribute interaction relationship between the reservoir geological data subset and the production operation data subset, identify the influence path of reservoir parameter changes on production operation parameters, and quantify the influence of permeability changes on the gas production pressure regulation effect.

[0027] The cross-set association mining unit of the reservoir production collaborative sensing network has commenced operation, focusing on analyzing the attribute interaction relationships between the reservoir geological data subset and the production operation data subset. First, reservoir parameters, such as permeability, are extracted from the reservoir geological data subset, and production operation parameters, such as gas production pressure, are extracted from the production operation data subset. Through comparison and analysis of long-term data from both sets, the impact paths of reservoir parameter changes on production operation parameters are identified. For example, when the permeability of a certain region of the reservoir changes, how does it affect the regulation of gas production pressure? Simultaneously, the impact of permeability changes on the effectiveness of gas production pressure regulation is quantified, i.e., determining the degree of change in the effect of gas production pressure regulation under different permeability changes, such as how much the effective range of gas production pressure regulation can be expanded when permeability increases by a certain percentage.

[0028] Step S1241: Extract key reservoir parameters that affect production operations from the core attribute features of the reservoir geological data subset. Key reservoir parameters include permeability, porosity, and gas saturation.

[0029] When analyzing the attribute interactions between the reservoir geology data subset and the production operation data subset, we first extract key reservoir parameters that affect production operations from the core attribute characteristics of the reservoir geology data subset. These key reservoir parameters are reservoir characteristic indicators that significantly impact production operations, mainly including permeability, porosity, and gas saturation. Permeability reflects the reservoir's ability to allow fluids to pass through, porosity reflects the size of the reservoir's fluid-accommodating space, and gas saturation indicates the proportion of natural gas in the reservoir's pores. They all directly or indirectly affect the setting of production operation parameters and production results.

[0030] Step S1242: Extract key production parameters affected by reservoir parameters from the core attribute features of the production operation data subset. Key production parameters include gas production pressure, liquid production rate, and extraction rate.

[0031] Next, key production parameters affected by reservoir parameters are extracted from the core attribute features of the production operation data subset. The setting and changes of these production parameters are influenced by reservoir parameters, mainly including gas production pressure, liquid production rate, and extraction rate. Gas production pressure is an important parameter for controlling gas production, and its magnitude is affected by parameters such as reservoir permeability; the amount of liquid produced is related to parameters such as reservoir porosity and gas saturation; the determination of the extraction rate also needs to consider factors such as reservoir permeability to ensure stable and efficient production.

[0032] Step S1243: Construct a correlation matrix between key reservoir parameters and key production parameters. The elements in the matrix represent the potential correlation between the corresponding reservoir parameters and production parameters.

[0033] Construct a correlation matrix between key reservoir parameters and key production parameters. The rows of this matrix represent key reservoir parameters, such as permeability, porosity, and gas saturation, while the columns represent key production parameters, such as gas production pressure, liquid production rate, and extraction rate. Each element in the matrix represents the potential correlation between the corresponding reservoir parameter and production parameter. Through analysis of historical data and production mechanisms, each element is assigned a numerical value or level indicating the magnitude of the correlation probability, thus visually demonstrating the potential correlation between the two.

[0034] Step S1244: By using the association verification module of the cross-set association mining unit and combining it with the coalbed methane well production mechanism, verify the rationality of potential associations in the association matrix and eliminate false associations that do not conform to the production mechanism.

[0035] The correlation verification module of the cross-set correlation mining unit is activated to verify the potential correlations represented in the correlation matrix, based on the coalbed methane well production mechanism. The coalbed methane well production mechanism is a set of rules summarized from long-term production practice and theoretical research, which can help determine whether the correlation between reservoir parameters and production parameters is reasonable. For example, according to the production mechanism, there should be a certain correlation between permeability and gas production pressure. However, if a strong correlation appears in the correlation matrix between permeability and an unrelated production parameter, it may be a spurious correlation and needs to be eliminated.

[0036] Step S1245: Perform strength analysis on the verified reasonable correlation to determine the influence strength of each reservoir parameter on the corresponding production parameter, and quantify the difference between the influence of permeability change on gas production pressure and the influence of porosity change on gas production pressure.

[0037] Strength analysis is performed on validated correlations to determine the influence of each reservoir parameter on its corresponding production parameter. Through statistical analysis of large amounts of data, combined with production mechanisms, the magnitude of the impact of different reservoir parameters on production parameters is assessed. For example, the influence of permeability changes on gas production pressure and the influence of porosity changes on gas production pressure are quantified, and the differences between the two are compared to determine which reservoir parameter has a more significant impact on gas production pressure.

[0038] Step S1246: Based on the results of the influence intensity analysis, construct the direct influence path from reservoir parameters to production parameters, and establish the direct influence relationship between the increase in permeability and the expansion of the adjustable range of gas production pressure.

[0039] Based on the results of the influence intensity analysis, direct influence paths are constructed for the correlation between reservoir parameters and production parameters with significant influence intensity. For example, when the analysis detects that the influence intensity of permeability on gas production pressure is significant, a direct influence relationship is established between the increase in permeability and the expansion of the adjustable range of gas production pressure. That is, as permeability increases, gas production pressure can be controlled within a larger range, thus forming a direct influence path from permeability to gas production pressure.

[0040] Step S1247: Analyze the path through which reservoir parameters indirectly affect production parameters via intermediate parameters, and identify the indirect influence relationship between porosity and reservoir gas saturation, thereby affecting liquid production.

[0041] In addition to the direct impact pathways, the study also analyzes the pathways through which reservoir parameters indirectly affect production parameters via intermediate parameters. Intermediate parameters are those that act as intermediaries between reservoir parameters and production parameters, capable of transmitting influence. For example, porosity affects reservoir gas saturation, which in turn affects the amount of produced liquid. Therefore, an indirect influence pathway was identified, whereby porosity affects reservoir gas saturation and thus, consequently, the amount of produced liquid.

[0042] Step S1248: Integrate the direct and indirect influence paths to form a complete influence path network of reservoir parameter changes on production operation parameters. The influence path network includes the influence order and correlation strength of each path.

[0043] By integrating the constructed direct and indirect influence paths, a complete network of influence paths of reservoir parameter changes on production operation parameters is formed. This network illustrates the order of influence of various paths—that is, which parameter affects which intermediate parameter first, and then which production parameter—as well as the correlation strength of each path, i.e., the magnitude of the influence. Through this network, a comprehensive understanding of the complex process by which reservoir parameter changes affect production operation parameters can be obtained.

[0044] Step S1249: Simplify the influence path network and output the simplified influence path network to output the main influence paths of reservoir parameter changes on production operation parameters.

[0045] Since the influence path network may contain numerous paths and parameters, it is simplified for ease of practical application and analysis. Influence paths with significant impact and major influence on changes in production operating parameters are retained, while less significant and secondary paths are removed. This simplification results in an influence path network that highlights the primary influence paths of reservoir parameter changes on production operating parameters.

[0046] Step S125: Analyze the attribute matching relationship between the fluid property data subset and the reservoir geology data subset, calculate the matching degree between fluid viscosity, density and reservoir pore structure, and evaluate the reservoir's ability to contain and transport fluids based on the matching degree.

[0047] Next, the attribute fit relationship between the fluid property data subset and the reservoir geology data subset is analyzed. Fluid viscosity and density are important attributes in the fluid property data subset, while reservoir pore structure is a key attribute in the reservoir geology data subset. Using specific analytical methods, the matching degree between fluid viscosity, density, and reservoir pore structure is calculated. This matching degree reflects the fluid's adaptability to flow within the reservoir pores. Based on the calculated matching degree, the reservoir's capacity to accommodate and migrate fluids is further evaluated. A high matching degree indicates that the reservoir's pore structure is suitable for the presence and flow of that fluid, and the reservoir has a strong capacity to accommodate and migrate fluids; conversely, a low matching degree indicates a weak capacity to accommodate and migrate fluids.

[0048] Step S126: Analyze the attribute matching relationship between the wellbore structure data subset and the production operation data subset, determine the matching degree of wellbore diameter, casing depth and production pressure control, and determine the effective control range of production operation parameters under different wellbore structures.

[0049] Then, the attribute compatibility relationship between the wellbore structure data subset and the production operation data subset is analyzed. Wellbore diameter and casing depth are the main structural attributes in the wellbore structure data subset, while production pressure control is an important operational parameter in the production operation data subset. Through analysis of both sets of data, the degree of compatibility between wellbore diameter, casing depth, and production pressure control is determined, i.e., how well different wellbore diameters and casing depths adapt to production pressure control. Based on this degree of compatibility, the effective control range of production operation parameters under different wellbore structures is further determined. For example, under a certain combination of wellbore diameter and casing depth, within what range can the production pressure be effectively controlled to achieve better production results?

[0050] Step S127: Integrate the influence paths, matching status and adaptation degree obtained from cross-set association mining to form preliminary reservoir production association features. The preliminary reservoir production association features include unidirectional influence and bidirectional adaptation information between attributes of different data subsets.

[0051] The information obtained during the cross-set correlation mining process, including the impact path of reservoir parameter changes on production operation parameters, the matching status between fluid properties and reservoir geology, and the degree of adaptation between wellbore structure and production operations, is integrated. During integration, different types of information are organized and correlated according to predefined rules to form preliminary reservoir production correlation characteristics. These preliminary characteristics comprehensively include unidirectional influence information between different data subset attributes, such as the unidirectional impact of reservoir parameter changes on production operation parameters, as well as bidirectional adaptation information, such as the mutual adaptation between fluid properties and reservoir geology, and the mutual adaptation between wellbore structure and production operations.

[0052] Step S128: Through the coupling reinforcement layer of the reservoir production collaborative sensing network, the preliminary correlation features of reservoir production are interactively reinforced to enhance the representation weight of the bidirectional interaction information between reservoir conditions and production operations, and to strengthen the feedback of production parameter adjustment caused by changes in reservoir parameters and the reaction information of production operations on reservoir state.

[0053] The coupling enhancement layer of the reservoir production collaborative sensing network is activated to interactively enhance the preliminary correlation features of reservoir production. The core of this process is to strengthen the representation weight of the bidirectional interaction information between reservoir conditions and production operations. Specifically, on the one hand, it strengthens the feedback information of production parameter adjustments triggered by changes in reservoir parameters, making this feedback relationship more prominent in the correlation features; on the other hand, it strengthens the feedback information of the reaction of production operations on the reservoir state, enhancing the information on how production operations affect the reservoir state. Through this interactive enhancement process, the preliminary correlation features of reservoir production can more accurately and comprehensively reflect the complex bidirectional interaction between reservoir conditions and production operations.

[0054] Step S1281: Input the preliminary correlation features of reservoir production into the feature decomposition module of the coupled reinforcement layer to decompose and obtain the unidirectional influence features of reservoir on production, the unidirectional influence features of production on reservoir, and the bidirectional adaptation features of the two.

[0055] After receiving the preliminary correlation features of reservoir production, the feature decomposition module of the coupling enhancement layer decomposes them. Using specific algorithms and rules, the preliminary correlation features are decomposed into three parts: unidirectional influence features of the reservoir on production, which mainly reflect the one-sided impact of reservoir parameter changes on production operation parameters; unidirectional influence features of production on the reservoir, which reflect the one-sided impact of production operation parameter adjustments on reservoir state; and bidirectional adaptation features between the reservoir and production, which reflect the mutual adaptation and matching relationship between reservoir conditions and production operations.

[0056] Step S1282: Enhance the unidirectional influence characteristics of the reservoir on production, amplify the characterization of the influence of reservoir parameter changes on production operation parameters, and enhance the characterization of the influence of permeability and porosity parameter changes on gas production pressure and liquid production.

[0057] The unidirectional impact characteristics of reservoirs on production obtained from the decomposition are enhanced. By increasing the weight of relevant features or adjusting the expression of features, the representation of the degree of influence of reservoir parameter changes on production operation parameters is amplified. Emphasis is placed on enhancing the representation of the influence of reservoir parameters such as permeability and porosity on production operation parameters such as gas production pressure and liquid production rate, making these influence relationships more prominent in the associated features so that the model can better capture the impact of the reservoir on production.

[0058] Step S1283: Enhance the unidirectional impact characteristics of production on the reservoir, highlight the reaction of production operation parameter adjustment on reservoir state, and enhance the characterization of the influence of gas production pressure adjustment on reservoir pressure distribution and gas saturation.

[0059] Similarly, the unidirectional impact of production on the reservoir is enhanced. A similar approach is adopted to emphasize the characterization of the reaction of production operation parameter adjustments on reservoir state. In particular, the influence of gas production pressure adjustments on reservoir state parameters such as pressure distribution and gas saturation is strengthened, making the reaction information of production operations on the reservoir more apparent in the associated features, thereby improving the model's ability to capture these reaction relationships.

[0060] Step S1284: By coupling the bidirectional interaction module of the reinforcement layer, an interactive feedback loop between the reservoir and production is constructed to simulate the cyclic process in which changes in reservoir parameters trigger adjustments in production parameters, and then the adjustments in production parameters have a feedback effect on reservoir parameters.

[0061] The bidirectional interaction module of the coupling reinforcement layer constructs an interactive feedback loop between the reservoir and production. This interactive feedback loop simulates a cyclical process: first, reservoir parameters change, which triggers adjustments to production parameters; then, the adjustments to production parameters, in turn, affect the reservoir, causing further changes in reservoir parameters, forming a continuous cycle. By constructing this interactive feedback loop, the dynamic interaction between the reservoir and production can be more realistically reflected.

[0062] Step S1285: Based on the interactive feedback loop, generate a bidirectional interaction enhancement feature, which includes the change trajectory of each parameter and the intensity of mutual influence during the cyclic interaction process.

[0063] Based on the constructed interactive feedback loop, a two-way interaction enhancement feature is generated. During the cyclical interaction process, the change trajectory of each parameter is recorded, i.e., the numerical changes of each parameter at different time points or stages, and the strength of the mutual influence between parameters. Integrating this information forms the two-way interaction enhancement feature, which reflects the two-way interaction relationship between the reservoir and production.

[0064] Step S1286: The enhanced unidirectional influence feature and the bidirectional action enhancement feature are fused together to form a preliminary enhanced coupling feature, which simultaneously reflects unidirectional influence and bidirectional interaction information.

[0065] The unidirectional influence characteristics of the enhanced reservoir on production, the unidirectional influence characteristics of production on the reservoir, and the bidirectional interaction enhancement characteristics generated are fused together. During the fusion process, the three characteristics are combined according to set weights and rules to form a preliminary enhancement coupling characteristic. This preliminary enhancement coupling characteristic not only contains unidirectional influence information but also bidirectional interaction information, which can more comprehensively reflect the complex relationship between the reservoir and production.

[0066] Step S1287: Perform a consistency check between the preliminary enhanced coupling features and the bidirectional interaction instances in the historical data of coalbed methane well production dynamics, and adjust the feature fusion weights based on the check results.

[0067] The consistency of the preliminary enhanced coupling features with bidirectional interaction instances in the historical production dynamics data of coalbed methane wells is verified. These bidirectional interaction instances represent actual bidirectional interactions between the reservoir and production processes during past production. By comparing the degree of consistency between the preliminary enhanced coupling features and these instances, if discrepancies are detected, the weights in the feature fusion process are adjusted based on the verification results to achieve a higher degree of consistency between the preliminary enhanced coupling features and the historical instances.

[0068] Step S1288: Based on the verification results, adjust the fusion weights of the unidirectional influence feature and the bidirectional effect enhancement feature, perform detailed optimization on the adjusted fusion feature, supplement the difference information of bidirectional effect under different production conditions, and enable the feature to adapt to multiple production scenarios.

[0069] Based on the consistency test results, the fusion weights of the unidirectional influence features and the bidirectional effect enhancement features were further adjusted. For parts that differ significantly from historical examples, the weights of the corresponding features were appropriately adjusted. Simultaneously, the adjusted fusion features underwent detailed optimization to supplement information on the differences in bidirectional effects under different production conditions. Different production conditions, such as different reservoir conditions and different production operation strategies, will result in differences in the bidirectional effects between the reservoir and production. Supplementing this difference information allows the features to adapt to various production scenarios, improving the model's versatility.

[0070] Step S1289: Output the correlation features after interactive enhancement processing, which highlight the bidirectional interaction information between reservoir conditions and production operations.

[0071] After completing the above processing, the correlation features are output after interactive enhancement. These correlation features, through a series of processes including decomposition, enhancement, fusion, verification, and optimization, can highlight the bidirectional interaction information between reservoir conditions and production operations, and more accurately and comprehensively reflect the complex relationship between the two.

[0072] Step S129: Calculate the similarity between the enhanced association features and the coupling relationship instances in the historical production data of coalbed methane wells. When the similarity is lower than the preset threshold, supplement the coupling association details of the current association features and the changes in coupling relationships under different production stages based on the coupling relationship instances in the historical data.

[0073] After the enhanced correlation features are generated, their similarity is calculated with coupling relationship instances in the historical production data of coalbed methane wells. The historical production data contains numerous instances of coupling relationships formed between reservoir conditions and production operations during past production processes. By calculating the similarity between the current correlation features and these historical instances, the adequacy of the current correlation features is determined. When the similarity is below a preset threshold, it indicates that the current correlation features are lacking in certain aspects. In this case, it is necessary to supplement the coupling relationship details of the current correlation features based on coupling relationship instances in the historical data. Simultaneously, it is also necessary to supplement the differences in coupling relationships across different production stages. Because reservoir conditions and production operations differ at different production stages, the coupling relationships between them will also vary. Supplementing this difference information makes the current correlation features more comprehensive and accurate.

[0074] Step S1210: Perform unified characterization processing on the improved correlation features, integrate different types of coupling correlation information into a unified format feature expression, and generate reservoir production coupling features that contain interaction information of reservoir parameter changes and production parameter adjustments.

[0075] After supplementing and refining the correlation features, a unified characterization process is performed. Since the correlation features contain various types of coupling correlation information, which may have different formats and expressions, the unified characterization process integrates these different types of information into a unified feature expression format. Through this process, the reservoir production coupling features have a standardized and unified structure, facilitating subsequent model processing and analysis. The final generated reservoir production coupling features contain information on the interaction between reservoir parameter changes and production parameter adjustments, which is a crucial foundation for subsequent dynamic production simulations.

[0076] Step S130: Input the reservoir production coupling characteristics into the artificial intelligence production dynamic simulation model, and combine the fluid migration law of the coalbed methane well with the response characteristics of the production system to dynamically simulate the entire life cycle production process of the coalbed methane well and obtain preliminary production dynamic simulation results.

[0077] Step S131: Divide the reservoir production coupling characteristics according to the coalbed methane well production stages to form stage coupling characteristics corresponding to different production stages. The production stages include the initial drainage period, the stable production period, and the production decline period.

[0078] In the aforementioned application scenarios, after obtaining the reservoir production coupling characteristics, the wells are divided according to their production stages. The production stages of a coalbed methane well are mainly divided into the initial drainage phase, the stable production phase, and the production decline phase. The initial drainage phase is the starting stage of production, during which both the reservoir and the production system are in an adjustment and adaptation phase. During the stable production phase, various production indicators are relatively stable, and a balance is reached between the reservoir and production operations. During the production decline phase, production gradually decreases as reservoir resources diminish. Based on the characteristics and classification criteria of these production stages, the parts of the reservoir production coupling characteristics corresponding to each stage are extracted to form unique stage coupling characteristics for each production stage, enabling more accurate dynamic production simulations for different stages.

[0079] Step S132: Input the coupling features of each stage into the stage adaptation layer of the AI ​​production dynamic simulation model, adjust the simulation parameters of the model according to the core features of different production stages, and configure the simulation parameters of the model according to the production characteristics of each stage.

[0080] The coupling characteristics of each stage are input into the stage adaptation layer of the AI-powered production dynamics simulation model. The main function of the stage adaptation layer is to adjust the model's simulation parameters based on the core characteristics of different production stages. For the stage coupling characteristics of the initial drainage phase, the core characteristics of this stage are the rapid changes in reservoir pressure and the initial flow characteristics of the fluid. The stage adaptation layer will adjust the simulation parameters related to the pressure change rate and the initial fluid flow accordingly. The core characteristics of the stable production period are the stability of various production indicators and the regularity of production operations. The stage adaptation layer will configure simulation parameters related to maintaining the stable state and fine-tuning the operating parameters. The core characteristics of the production decline period are the continuous decrease in production and the gradual decay of reservoir parameters. The stage adaptation layer will adjust the simulation parameters related to the production decay rate and the trend of reservoir parameter changes accordingly to ensure that the model can adapt to the production characteristics of different stages and improve the accuracy of the simulation.

[0081] Step S133: Using the fluid migration simulation unit of the artificial intelligence production dynamic extrapolation model, combined with the fluid migration law of coalbed methane wells, extrapolate the fluid flow process of each production stage, and simulate the fluid seepage in the reservoir, the flow in the wellbore, and the production process.

[0082] The fluid transport simulation unit of the AI-powered production dynamics model has begun operation, performing detailed simulations of the fluid flow process at each production stage based on the fluid transport patterns in coalbed methane wells. First, it simulates the fluid seepage process in the reservoir, simulating the flow path and velocity within the reservoir pores based on parameters such as pore structure and permeability, as well as the fluid's properties. Next, it simulates the fluid flow within the wellbore, considering wellbore structural characteristics, fluid pressure and velocity variations, and the upward flow of the fluid after it enters the wellbore from the reservoir. Finally, it simulates the fluid production process at the wellhead, considering fluid separation and metering, thus comprehensively simulating the entire flow process from the reservoir to the wellhead.

[0083] Step S1331: Input the stage coupling features corresponding to the production stage into the fluid transport simulation unit, and extract the feature information related to fluid transport, including the relevant features of reservoir porosity, permeability, fluid viscosity, density and production pressure.

[0084] When simulating the fluid flow process at each production stage, the stage coupling characteristics of the corresponding production stage are first input into the fluid transport simulation unit. The fluid transport simulation unit analyzes the input stage coupling characteristics and extracts feature information related to fluid transport. This information includes reservoir porosity characteristics, such as pore size distribution and connectivity; permeability characteristics, such as permeability in different directions; fluid viscosity characteristics, such as viscosity changes at different temperatures and pressures; fluid density characteristics, such as the mass of fluid per unit volume; and production pressure characteristics, such as the pressure variation range and stable value during the production process.

[0085] Step S1332: Based on the extracted relevant features, determine the initial conditions for fluid migration in this production stage. The initial conditions include the initial reservoir pressure, initial gas saturation, and initial fluid distribution state.

[0086] Based on the extracted features related to fluid migration, the initial conditions for fluid migration in this production stage are determined. Initial reservoir pressure refers to the pressure state inside the reservoir at the start of this production stage; initial gas saturation refers to the proportion of natural gas in the reservoir pores at the beginning of the production stage; and the initial fluid distribution describes the distribution of fluid in the reservoir at the start of the production stage, such as which areas have higher fluid content and which have lower content. These initial conditions are the starting point for fluid migration simulation.

[0087] Step S1333: Based on the fluid migration law of coalbed methane wells, set the basic assumptions for fluid migration. The assumptions include reasonable assumptions that the fluid flow is isothermal and the reservoir medium is a homogeneous and isotropic medium, which are consistent with actual production.

[0088] Based on the fluid migration patterns in coalbed methane wells, basic assumptions regarding fluid migration are established. These assumptions are designed to simplify the simulation process while also reflecting actual production conditions and ensuring the reliability of the simulation results. For example, it is assumed that the fluid flow is isothermal, meaning that the fluid temperature remains constant during flow, and the influence of temperature changes on fluid properties and flow is not considered. It is also assumed that the reservoir medium is homogeneous and isotropic, meaning that the physical properties of the reservoir are the same in all directions and are uniformly distributed throughout the entire reservoir. This simplifies the calculation and analysis of fluid flow within the reservoir.

[0089] Step S1334: Using the seepage simulation module of the fluid transport simulation unit, calculate the seepage process of fluid in the reservoir based on Darcy's law, and deduce the flow trajectory and flow rate of fluid from the reservoir pores to the wellbore.

[0090] The seepage simulation module of the fluid transport simulation unit begins operation, calculating the seepage process of fluid in the reservoir based on Darcy's law. Darcy's law describes the flow law of fluid in porous media. The seepage simulation module applies Darcy's law to calculate parameters such as reservoir permeability, fluid viscosity, and pressure gradient inside the reservoir, thereby deducing the specific trajectory of fluid flowing from reservoir pores towards the wellbore, as well as the flow rate at different locations.

[0091] Step S1335: Simulate the phase change process of fluid in the reservoir, and combine the characteristics of fluid properties to deduce the process of coalbed methane desorbing from the adsorbed state to the free state and the mixing flow of the free state fluid with water.

[0092] This study simulates the phase change process of fluids in a reservoir. Coalbed methane (CBM) typically exists in two forms in reservoirs: adsorbed and free. Combining fluid properties such as temperature, pressure, and composition, the process of CBM desorption from the adsorbed state to the free state is simulated. Specifically, when the reservoir pressure decreases to a certain level, CBM molecules adsorbed on the coal and rock surface begin to detach and become free. Simultaneously, the mixing and flow process of free CBM with water in the reservoir is simulated, considering their interaction and flow characteristics.

[0093] Step S1336: Using the wellbore flow simulation module of the fluid transport simulation unit, simulate the flow process of fluid after entering the wellbore, and deduce the upward trajectory, velocity change and pressure loss of the fluid in the wellbore.

[0094] The wellbore flow simulation module of the fluid transport simulation unit is activated to simulate the flow process of fluid after it enters the wellbore. After entering the wellbore, the fluid flows upward under the action of pressure difference. The wellbore flow simulation module extrapolates the upward trajectory of the fluid in the wellbore, i.e., the flow path of the fluid in the wellbore, based on parameters such as the diameter, length of the wellbore, and the properties of the fluid; it analyzes the velocity changes of the fluid during the upward process, such as the velocity at different depths in the wellbore; and it calculates the pressure loss of the fluid flowing in the wellbore, i.e., the pressure reduction caused by factors such as friction and collision.

[0095] Step S1337: Based on the characteristics of the wellbore structure, analyze the differences in the flow state of the fluid at different depths in the wellbore, and calculate the impact of changes in the wellbore diameter on the fluid flow.

[0096] By considering wellbore structural characteristics, such as wellbore diameter variations and casing location and dimensions, this study analyzes the differences in fluid flow patterns at different wellbore depths. Since the wellbore diameter may vary at different depths, this affects fluid flow. For example, at larger diameters, the fluid velocity may be slower and the pressure loss smaller; conversely, at smaller diameters, the velocity may be faster and the pressure loss larger. Therefore, it is necessary to calculate the specific impact of wellbore diameter variations on fluid flow to more accurately simulate the fluid flow process within the wellbore.

[0097] Step S1338: Simulate the fluid production process at the wellhead, deduce the separation and metering process of the fluid after it flows out of the wellbore, and obtain the fluid production indicators of gas production rate and liquid production rate at this production stage.

[0098] The simulation of fluid production at the wellhead involves separating coalbed methane and water after the fluid flows out of the wellbore. The separated coalbed methane and water are then metered to determine the gas production rate and liquid production rate. By simulating the separation and metering process, fluid production indicators such as the gas production rate and liquid production rate for this production stage are obtained.

[0099] Step S1339: Integrate the seepage simulation results, wellbore flow simulation results, and production simulation results to form a complete fluid migration simulation result for this production stage. The fluid migration simulation result includes fluid flow information at each stage of the reservoir, wellbore, and wellhead.

[0100] The simulation results of fluid seepage in the reservoir obtained from the seepage simulation module, fluid flow in the wellbore obtained from the wellbore flow simulation module, and the simulation results of the wellhead production process are integrated. Following the sequence of fluid flow, the flow information in the reservoir, the flow information in the wellbore, and the production information at the wellhead are organically combined to form a complete fluid migration simulation result for this production stage. This fluid migration simulation result comprehensively reflects the fluid flow at each stage of the reservoir, wellbore, and wellhead.

[0101] Step S134: Using the production system response unit of the artificial intelligence production dynamic simulation model, combined with the production system response characteristics, simulate the feedback state of the production system after the adjustment of production operation parameters, including the changes in gas production rate and liquid production rate.

[0102] The production system response unit of the AI-powered production dynamic simulation model is activated, operating in accordance with the production system's response characteristics. These characteristics reflect the production system's reaction pattern when production operation parameters are adjusted. Based on input production operation parameter adjustment information, such as changes in gas production pressure and extraction rate, the production system response unit simulates the feedback state of the production system after these adjustments. The focus is on the changes in gas production rate and liquid production rate; specifically, how these rates change when production operation parameters change—whether they increase, decrease, or remain stable, and the magnitude and trend of these changes.

[0103] Step S135: The fluid transport simulation results are fused with the production system response simulation results to obtain the stage production dynamic simulation results for each production stage. The stage production dynamic simulation results include the change trajectory of key production indicators within the production stage.

[0104] The simulation results of fluid flow processes obtained from the fluid transport simulation unit and the simulation results of production system feedback states obtained from the production system response unit are fused. During the fusion process, data related to production indicators from the fluid transport simulation results, such as gas production rate and liquid production rate, are integrated and correlated with the changes in gas production rate and liquid production rate from the production system response simulation results. Through this fusion process, the stage-specific production dynamic simulation results for each production stage are obtained. These stage-specific production dynamic simulation results include the time-varying trajectories of key production indicators within that stage, such as gas production rate, liquid production rate, and reservoir pressure, thus demonstrating the production dynamics of the entire production stage.

[0105] Step S136: Analyze the connection between the simulation results of the production dynamics of adjacent production stages, so that the simulation results of the previous stage can be reasonably transitioned to the next stage, and the continuity of the production process can be maintained.

[0106] After obtaining the simulation results of the production dynamics for each production stage, the connection between the simulation results of adjacent production stages is analyzed. Adjacent production stages, such as the initial stage of production and the stable production period, or the stable production period and the production decline period, should exhibit continuous and reasonable changes in production dynamics. By comparing the production index data at the end of the previous stage with the production index data at the beginning of the next stage, any unreasonable jumps or abrupt changes are checked. If unreasonable connections are detected, the causes need to be analyzed and adjustments made, such as adjusting the initial parameters of the next stage or the model's extrapolation parameters, to ensure that the simulation results of the previous stage can transition naturally and reasonably to the next stage, thereby maintaining the continuity of the entire production process.

[0107] Step S1361: Obtain the stage production dynamic simulation results of two adjacent production stages, including the end-stage production index data of the previous stage and the initial stage production index data of the next stage.

[0108] When analyzing the connection between the simulation results of production dynamics in adjacent production stages, the simulation results of production dynamics in two adjacent production stages are first obtained. For example, the production index data at the end of the initial stage of the production process and the initial production index data at the beginning of the stable production stage are obtained, or the production index data at the end of the stable production stage and the initial production index data at the beginning of the production decline stage are obtained. The above data includes the specific values ​​of each production index at the end and beginning of the corresponding stage.

[0109] Step S1362: Compare the production indicator data at the end of the previous stage with the production indicator data at the beginning of the next stage, analyze the degree of difference between the two, and detect whether there is a step change in the production indicator data that exceeds the preset threshold.

[0110] Compare production indicator data at the end of the previous stage and the beginning of the next stage, and calculate the differences in values ​​for each production indicator. Analyze the degree of these differences to determine if there are any abrupt changes exceeding a preset threshold. The preset threshold is set based on production experience and the continuity requirements of the production process, and is used to measure whether changes in production indicators are within a reasonable range. If the change in a certain production indicator exceeds this threshold, it indicates a potential continuity problem.

[0111] Step S1363: Based on the mechanism of coalbed methane well production stage transition, obtain the normal transition law between two adjacent stages. When transitioning from the initial stage of drainage to the stable production period, the rate of change of gas production approaches zero, and the rate of change of liquid production approaches zero after being negative.

[0112] Based on the mechanism of production stage transition in coalbed methane wells, we summarize and obtain the normal transition law between two adjacent stages. Taking the transition from the initial drainage stage to the stable production stage as an example, according to the production mechanism, during this transition process, the gas production will gradually increase and tend to stabilize, so the rate of change of gas production will gradually approach zero; the liquid production may be relatively high in the initial drainage stage, but will gradually decrease as production progresses, so the rate of change of liquid production is negative and will gradually approach zero. The above normal transition law is the basis for judging whether the stage connection is reasonable.

[0113] Step S1364: Based on the normal transition law, construct the stage transition evaluation standard. The stage transition evaluation standard defines the reasonable range of the transition of production indicators between adjacent stages. If the transition exceeds the reasonable range, it is judged as unreasonable.

[0114] Based on the normal transition law, a stage transition evaluation standard is constructed. This standard clearly defines the reasonable range for the transition of production indicators between adjacent stages, such as the reasonable range for the rate of change of gas production and the reasonable range for the rate of change of liquid production. If the difference in production indicator data between the end of the previous stage and the beginning of the next stage is within this reasonable range, the transition is considered reasonable; if it exceeds this reasonable range, it is deemed unreasonable.

[0115] Step S1365: If the difference between the production indicators at the end of the previous stage and the beginning of the next stage is within a reasonable range, then retain the simulation results of the current stage.

[0116] If, after comparison and analysis, the difference in production indicators between the end of the previous stage and the beginning of the next stage is found to be within a reasonable range as defined by the stage transition evaluation criteria, it indicates that the simulation results of the two stages are reasonably connected and no adjustments are needed; the simulation results of the current stage can be directly retained.

[0117] Step S1366: If the difference exceeds a reasonable range, then according to the stage transition diagnosis rules, determine whether the root cause of the transition problem is the deviation of the simulation results in the previous stage or the improper setting of the simulation parameters in the next stage.

[0118] When production indicators deviate from a reasonable range, analysis is conducted according to the phase transition diagnostic rules to determine the root cause of the transition problem. These rules are a set of judgment criteria based on production experience and simulation process summaries. By analyzing the characteristics of the discrepancies, the simulation process of the previous phase, and the parameter settings of the subsequent phase, it is determined whether the simulation results of the previous phase were biased and failed to accurately reflect the actual production status at the end of that phase, or whether the simulation parameters of the subsequent phase were improperly set, leading to a lack of reasonable transition between the initial production indicators and the end of the previous phase.

[0119] Step S1367: Adjust according to the cause. If it is a deviation in the simulation results of the previous stage, correct the inference parameters of the previous stage and re-simulate to make the final indicators fall within the reasonable range defined by the stage transition evaluation criteria.

[0120] Adjustments should be made based on the diagnosed causes. If the unreasonable transition is due to deviations in the simulation results of the previous stage, the extrapolation parameters of the previous stage need to be corrected. By adjusting the extrapolation parameters related to this stage, such as the fluid transport coefficient and the production system response coefficient, the production dynamics simulation of the previous stage should be repeated to ensure that the simulated final production indicators fall within the reasonable range defined by the stage transition evaluation criteria.

[0121] Step S1368: If the simulation parameters for the next stage are not set properly, adjust the model adaptation parameters for the next stage so that the difference between the initial indicators and the final indicators of the previous stage meets the stage transition evaluation criteria.

[0122] If the transition problem is caused by improper simulation parameter settings in the later stage, then the model adaptation parameters for the later stage should be adjusted. Model adaptation parameters include various parameters related to the production characteristics of this stage. By adjusting these parameters, the initial production indicators of the later stage can be matched with the production indicators at the end of the previous stage, and the difference between the two meets the requirements of the stage transition evaluation criteria.

[0123] Step S1369: Regenerate the adjusted simulation results of the production dynamics of adjacent stages, and verify the connection relationship again to ensure that it conforms to the stage transition law.

[0124] After adjusting the parameters, regenerate the adjusted simulation results for the adjacent production stages. Then, verify the connection relationship of the new simulation results again, checking whether the transition of production indicators conforms to the stage transition rules and stage connection evaluation criteria. If it conforms, the adjustment is effective; if it still does not conform, it is necessary to repeat the diagnosis and adjustment until the connection relationship is reasonable.

[0125] Step S13610: Repeat the above comparison, analysis, adjustment and verification process for all adjacent production stages to ensure that the entire life cycle production dynamic evolution path meets the continuity requirement and the changes in production indicators between stages meet the continuity requirement.

[0126] The above comparison, analysis, adjustment, and verification process is repeated for all adjacent production stages throughout the entire life cycle of a coalbed methane well. From the transition between the initial drainage phase and the stable production phase, to the transition between the stable production phase and the production decline phase, it is ensured that the transition of production indicators between each adjacent stage is reasonable and continuous. Through the above treatment, the dynamic evolution path of production throughout the entire life cycle meets the continuity requirement, and the changes in production indicators between each stage are smooth and natural, conforming to the actual production process.

[0127] Step S137: Based on the connected stage production dynamic simulation results, construct the production dynamic evolution path of the entire life cycle of the coalbed methane well. The production dynamic evolution path includes the continuous changes of production indicators at each stage and the characteristics of stage transition nodes.

[0128] Based on the adjusted and interconnected simulation results of production dynamics at each stage, a dynamic evolution path for the entire lifecycle of a coalbed methane well is constructed. This path, using time as its axis, connects the simulation results of each production stage, demonstrating the continuous changes in various production indicators throughout the entire lifecycle, from the initial drainage phase through the stable production period to the end of the production decline phase. Simultaneously, the characteristics of stage transition nodes are clearly marked in the evolution path, namely the key production indicator values ​​and trends when transitioning from one production stage to another, in order to comprehensively understand the overall evolution process of production dynamics.

[0129] Step S138: Through the trend prediction unit of the artificial intelligence production dynamic simulation model, extend the dynamic evolution path of the entire production life cycle and predict the trend of production indicator changes in subsequent production stages.

[0130] The trend prediction unit of the AI-powered production dynamics simulation model plays a crucial role in extending and extrapolating the established full lifecycle production dynamics evolution path. Based on existing patterns and trends in production indicators within the evolution path, and combined with general production patterns and relevant influencing factors in coalbed methane wells, the trend prediction unit predicts the trends in production indicators for subsequent production stages after the current stage. For example, during a production decline period, based on the current rate of decline and the remaining reservoir resources, it predicts the further trend in production over a future period.

[0131] Step S139: Integrate the simulation results, evolution paths and trend prediction results of each stage to form complete simulation data covering the entire life cycle of the production process. The complete simulation data includes information on the changes and spatial distribution of production indicators over time.

[0132] The simulation results of each production stage, the constructed full life-cycle production dynamic evolution path, and the prediction results of subsequent production stages obtained from the trend prediction unit are integrated. During the integration process, the above information is organized according to chronological order and data type to form complete simulation data covering the entire life-cycle production process of a coalbed methane well. This complete simulation data not only includes the changes in production indicators over time, such as gas production rate and liquid production rate at different time points, but also includes spatial distribution information of production indicators, such as pressure distribution and fluid saturation distribution in different areas of the reservoir.

[0133] Step S1310: Standardize the complete simulation data, organize the simulation information according to a unified output format, and obtain preliminary production dynamic simulation results. The preliminary production dynamic simulation results include the production dynamic change information of the coalbed methane well from the initial stage of drainage to the production decline period.

[0134] The integrated, complete simulation data undergoes standardization. Since the complete simulation data comes from diverse sources and may have different formats, standardization involves organizing it according to a unified output format. Standardized data formats, indicator names, units, and other criteria are determined, and the simulation information is standardized, converted, and organized to ultimately obtain preliminary production dynamic simulation results. These preliminary production dynamic simulation results contain information on the dynamic changes in production throughout the entire production process of a coalbed methane well, from the initial stage of drainage to the decline in production.

[0135] Step S140: Introduce actual production boundary conditions of coalbed methane wells through a production constraint feedback mechanism, adaptively adjust the preliminary production dynamic simulation results, correct the deviation between the simulation results and the boundary conditions, and obtain the target production dynamic simulation results.

[0136] Step S141: Collect the actual production boundary conditions of coalbed methane wells, which include the upper limit of reservoir pressure, the range of gas production control, the equipment operating load limit and environmental emission requirements.

[0137] In the aforementioned application scenarios, to ensure that the dynamic simulation results of production more closely reflect actual production conditions, it is necessary to collect the actual production boundary conditions of coalbed methane wells. These boundary conditions are the limiting factors that must be followed during the production process. Among them, the upper limit of reservoir pressure specifies the value that the reservoir pressure cannot exceed to avoid damaging the reservoir; the gas production control range clarifies the upper and lower limits that the gas production can be adjusted to ensure the stability and economy of production; the equipment operating load limit takes into account the performance of the production equipment and specifies the load level that the equipment cannot exceed during operation; and the environmental emission requirements limit the concentration and amount of pollutants emitted during the production process to comply with environmental regulations.

[0138] Step S142: Input the actual production boundary conditions of the coalbed methane well into the constraint analysis unit of the production constraint feedback mechanism, analyze the constraint type and constraint range of each boundary condition, and output the restricted production indicators and their specific constraint requirements.

[0139] The collected actual production boundary conditions of coalbed methane wells are input into the constraint analysis unit of the production constraint feedback mechanism. The constraint analysis unit performs a detailed analysis of each boundary condition to determine its constraint type, such as whether it is an upper limit constraint, a lower limit constraint, or a range constraint. Simultaneously, it clarifies the specific constraint range of each boundary condition, such as the specific value of the upper limit of reservoir pressure and the upper and lower limits of the gas production control range. Based on these analyses, the restricted production indicators and the specific constraint requirements corresponding to each production indicator are output. For example, the constraint requirement for the gas production indicator is that it fluctuates within a certain numerical range.

[0140] Step S143: Compare the production indicators in the preliminary production dynamic simulation results with the analyzed boundary conditions, identify the simulation data that exceeds the constraint range, and record the corresponding production stage identifier and specific production indicators.

[0141] The production indicators in the preliminary dynamic simulation results are compared with the boundary conditions output by the constraint analysis unit. Each production indicator's simulation data at each production stage is checked to ensure it meets the corresponding constraint requirements. When the simulation data of a production indicator exceeds the constraint range specified by the analyzed boundary conditions, the simulation data is immediately identified, and its corresponding production stage identifier (i.e., which production stage the data belongs to, as well as the specific production indicator name and value) is accurately recorded.

[0142] Step S144: Based on the reservoir production coupling characteristics, and through the preset deviation analysis rules, determine that the simulation data exceeding the constraint range is due to the deviation of parameter settings or insufficient mining of coupling relationships during the simulation process, resulting in the index exceeding the limit.

[0143] Based on the reservoir production coupling characteristics, and using pre-defined deviation analysis rules, simulated data exceeding the constraints are analyzed to determine the reasons for the out-of-limit indicators. These pre-defined deviation analysis rules are a set of judgment criteria summarized from extensive production experience and data analysis. By comparing and analyzing the simulated data exceeding the constraints with relevant information in the reservoir production coupling characteristics, if the out-of-limit simulation data is found to be due to deviations between the settings of certain parameters during model deduction and actual conditions (e.g., unreasonable fluid migration coefficient settings), or insufficient exploration of the reservoir production coupling relationship, causing the model to fail to accurately reflect the complex relationship between reservoir conditions and production operations, thus leading to deviations in the simulation results, the analysis is conducted accordingly.

[0144] Step S1441: Extract the production stages and related production indicators corresponding to the simulation data that exceed the constraints, and identify the production stages and specific production indicators that exceed the limits.

[0145] When determining the causes of simulation data exceeding constraints, the first step is to extract the corresponding production stage and relevant production indicators. It is crucial to clearly identify which production stage these exceeding-limit data belong to, such as the initial stage of production or the period of declining production, and which specific production indicators, such as gas production or reservoir pressure, have exceeded the limits. These indicators should be clearly labeled for subsequent targeted analysis.

[0146] Step S1442: Retrieve the reservoir production coupling characteristics corresponding to the production stage, and extract the coupling information that is related to the over-limit production index according to the preset association rules, including the reservoir parameters that affect the corresponding index, the production operation parameters, and the interaction between the parameters.

[0147] The reservoir production coupling characteristics corresponding to the over-limit production stage are retrieved. These characteristics include the interaction relationships between reservoir parameters and production parameters. Based on preset association rules, coupling information related to the over-limit production index is extracted from the reservoir production coupling characteristics. This information includes reservoir parameters affecting the over-limit production index, such as permeability and porosity; relevant production operation parameters, such as gas production pressure and liquid production rate; and the interaction relationships between these reservoir parameters and production operation parameters, such as how changes in a certain reservoir parameter affect production operation parameters, and thus affect the over-limit production index.

[0148] Step S1443: Compare the simulation parameters related to this production stage in the AI ​​production dynamic simulation model with the benchmark values ​​in the standard parameter library to identify parameters with significant deviations.

[0149] The simulation parameters related to this stage of production exceeding limits in the AI-powered production dynamics simulation model were compared with benchmark values ​​in a standard parameter library. These benchmark values ​​are reasonable parameter ranges or values ​​determined based on extensive production practice and theoretical research. By comparing these parameters, those in the model that deviate significantly from the benchmark values ​​were identified. These unreasonable parameter settings may be one of the reasons for the simulation data exceeding limits.

[0150] Step S1444: Calculate the difference between the parameter settings in the AI ​​production dynamic simulation model and the parameter values ​​in actual production. When the difference exceeds the preset tolerance, it is determined that the parameter setting deviation may cause the production indicators to exceed the limit.

[0151] The difference between the current parameter settings in the AI-powered production dynamic simulation model and the parameter values ​​in actual production is calculated. The parameter values ​​in actual production are real data recorded during the actual production process. If the calculated difference exceeds the preset tolerance value, it indicates that the model parameter settings deviate significantly from the actual situation. In this case, it can be determined that the parameter setting deviation may be the cause of the production indicators exceeding the limits.

[0152] Step S1445: Based on the reservoir production coupling characteristics, check whether the set of standard coupling relationships related to the out-of-limit indicators is complete and identify the types of missing association relationships.

[0153] Based on the reservoir production coupling characteristics, the completeness of the standard coupling relationship set related to exceeding production limits is checked. The standard coupling relationship set contains all possible correlations between reservoir parameters and production parameters. If the set is found to be missing certain correlation types related to exceeding limits, it indicates insufficient coupling relationship mining. This may lead to the model failing to accurately reflect the relationship between reservoir conditions and production operations, thus causing production limits to be exceeded.

[0154] Step S1446: After supplementing the missing coupling relationships in the simulation environment, perform production indicator trend prediction and calculate the probability of the indicator value regressing within the constraint range.

[0155] In the simulation environment, previously identified missing coupling relationships are manually supplemented. Then, the supplemented coupling relationships are used to predict production indicator trends; that is, the trend of exceeding production limits is simulated after these relationships are supplemented, and the probability of the indicator value returning to the constraint range is calculated. If the probability is high, it indicates that insufficient coupling relationship mining is the main reason for the indicator exceeding the limit.

[0156] Step S1447: After correcting the biased extrapolation parameters in the simulation environment, perform production indicator trend prediction and calculate the probability that the indicator value regresses within the constraint range.

[0157] Similarly, in the simulation environment, the previously identified inference parameters with deviations are corrected to bring them closer to the benchmark values ​​in the standard parameter library or the parameter values ​​in actual production. Then, production indicator trend prediction is performed, and the probability that the out-of-limit production indicator values ​​will return to the constraint range after the parameter correction is calculated.

[0158] Step S1448: Compare the index regression probabilities corresponding to the two correction methods, and select the correction method with the higher index regression probability as the preferred solution.

[0159] Compare the regression probabilities of the indicators corresponding to two correction methods: supplementing the coupling correlation and modifying the inference parameters. The method that increases the probability of the over-production indicator returning to the constraint range is selected as the preferred correction scheme. If the regression probability is higher after supplementing the coupling correlation, it indicates that insufficient coupling correlation mining is the main reason; if the regression probability is higher after modifying the inference parameters, it indicates that parameter setting deviation is the main reason.

[0160] Step S1449: Based on the knowledge base of coalbed methane well production mechanism, calculate the probability weight of index exceeding the limit due to two reasons: parameter setting deviation and insufficient coupling relationship mining.

[0161] Based on a knowledge base of coalbed methane well production mechanisms, this knowledge base contains knowledge about the relationships between various production phenomena and their causes. It calculates the probability weights of two reasons leading to index exceedances: parameter setting deviations and insufficient mining of coupling relationships. These probability weights reflect the likelihood of each reason causing the index to exceed limits and are calculated based on production mechanisms and historical data.

[0162] Step S14410: Based on the combined results of the predicted correction effect and the probability weight calculation, determine whether the reason for the simulation data exceeding the constraint range is due to parameter setting deviation or insufficient mining of coupling relationship during the simulation process.

[0163] A comprehensive analysis and judgment are made by comprehensively considering the regression probabilities of the two correction methods obtained from the prediction of the correction effect, as well as the probability weights of the two causes calculated based on the production mechanism knowledge base. If the regression probability of supplementing the coupling relationship is high and its probability weight is also large, the final judgment is that the cause of the indicator exceeding the limit is insufficient mining of the coupling relationship; if the regression probability of the correction inference parameter is high and its probability weight is large, the judgment is that the cause is parameter setting deviation; if the probabilities and weights of the two are relatively close, it may be necessary to consider both causes simultaneously and make corresponding adjustments.

[0164] Step S145: Based on the deviation type determination result, perform the corresponding parameter adjustment operation. For deviations in parameter settings that cause exceeding limits, correct the corresponding fluid transport coefficient or production system response coefficient in the model.

[0165] Based on the determination of the deviation type, corresponding parameter adjustments are made. If the simulation data exceeding the constraints is determined to be due to parameter setting deviations, then the corresponding parameters in the model are corrected. For example, when the simulated fluid transport process deviates from the actual situation, causing production indicators such as gas production to exceed the constraints, the fluid transport coefficient in the model is corrected; when the simulated response of the production system to the adjustment of operating parameters is inaccurate, the production system response coefficient is corrected. Through these parameter adjustments, the model's extrapolation results are made closer to the actual production situation.

[0166] Step S146: For exceedances caused by insufficient coupling relationship mining, supplement the corresponding reservoir production coupling association information and strengthen the mutual influence relationship between related attributes.

[0167] If the exceedance of the index is due to insufficient exploration of coupling relationships, then it is necessary to supplement the corresponding reservoir production coupling correlation information. By further analyzing the relationships between data subsets such as reservoir geological conditions, production operation behavior, fluid properties, and wellbore structure, previously undetected or insufficiently considered coupling correlation information can be uncovered. Simultaneously, the mutual influence relationships between related attributes should be strengthened; for example, the characterization of the impact of reservoir permeability changes on gas production pressure regulation should be enhanced to make the reservoir production coupling characteristics more complete, thereby improving the accuracy of model extrapolation.

[0168] Step S147: Input the adjusted simulation parameters and the supplemented coupling correlation information into the artificial intelligence production dynamic simulation model, and re-perform the production dynamic simulation to obtain the corrected production dynamic simulation results.

[0169] The adjusted simulation parameters, such as the corrected fluid migration coefficient and production system response coefficient, along with the supplemented reservoir production coupling correlation information, are re-input into the AI-powered production dynamics simulation model. The model then uses these updated parameters and information to perform another production dynamics simulation. Starting from the initial drainage phase, through the stable production period, and into the production decline phase, the production dynamics of each stage are re-simulated, ultimately yielding the corrected production dynamics simulation results.

[0170] Step S148: Compare the corrected production dynamic simulation results with the actual production boundary conditions a second time to check whether there is still data that exceeds the constraint range.

[0171] After the revised production dynamics simulation results are generated, they are compared a second time with the actual production boundary conditions. Following the same method as the first comparison, each production indicator's simulation data at each production stage is checked to ensure it meets the boundary condition constraints. Special attention is paid to production indicators that previously exceeded limits, while other production indicators are also checked to ensure that the revised simulation results no longer contain data exceeding the constraints.

[0172] Step S149: When it is detected that there is still simulated data that exceeds the constraint range, start the iterative optimization process, and repeatedly perform deviation judgment, parameter adjustment and re-deduction operations until all production indicators meet the actual production boundary conditions.

[0173] If, during the second comparison, simulated data for production indicators still exceeds the constraints, it indicates that the initial correction failed to fully resolve the issue. In this case, an iterative optimization process is initiated. This involves repeatedly performing deviation assessment, parameter adjustment, and re-simulation. Specifically, the causes of exceeding limits are analyzed again, corresponding parameter adjustments or supplementary coupling information are made, and then dynamic production simulation is performed again to obtain new simulation results, which are then compared with the boundary conditions. This process is repeated until the simulated data for all production indicators meet the actual production boundary conditions.

[0174] Step S1410: Perform data smoothing on the simulation results that finally meet the constraints, optimize the production index change curves and the continuity of stage transitions, and obtain the target production dynamic simulation results.

[0175] Once all production indicators in the simulation results meet the actual production boundary conditions, the final simulation results are smoothed. The production indicator change curves may exhibit some fluctuations or discontinuities; data smoothing adjusts these curves to make them smoother and more natural. Simultaneously, the continuity of production stage transitions is optimized to ensure a smoother transition from one production stage to another, avoiding abrupt changes. After these processes, the target production dynamic simulation results are obtained.

[0176] Step S150: Output the target production dynamic simulation results and the corresponding production parameter optimization directions.

[0177] For example, step S151: The target production dynamic simulation results are structured and organized according to production stage and production indicator type.

[0178] In the above application scenario, after obtaining the target production dynamic simulation results, they are first processed through structuring. The data in the simulation results are categorized and collected according to the production stages: the initial drainage phase, the stable production phase, and the production decline phase. Within each production stage, the data is further organized according to the type of production index, such as gas production rate, fluid production rate, reservoir pressure, and wellhead pressure. This structuring process makes the target production dynamic simulation results clearer and more convenient for subsequent analysis and application.

[0179] Step S152: Extract the changing trends of key production indicators from the target production dynamic simulation results. Key production indicators include gas production, liquid production, reservoir pressure, and wellhead pressure, and record the trend characteristics of each indicator.

[0180] The changing trends of key production indicators are extracted from the structured simulation results of the target production dynamics. These key indicators mainly include gas production, fluid production, reservoir pressure, and wellhead pressure. By analyzing the data of these indicators throughout the entire production cycle, their time-varying curves are plotted to demonstrate their changing trends. Simultaneously, the trend characteristics of each indicator are recorded in detail; for example, does gas production show a trend of first rising, then stabilizing, and then declining, or does it exhibit a sharp change at a certain stage?

[0181] Step S153: Compare the changing trends of key production indicators with the preset optimization target values, calculate the optimization potential value of each indicator in each production stage, and screen out the production stages and indicator combinations whose optimization potential value exceeds the set threshold and can be improved by adjusting production parameters.

[0182] The extracted trends of key production indicators are compared with preset optimization target values. These preset target values ​​are ideal values ​​or ranges for various production indicators set based on production needs and expectations. Through comparison, the optimization potential value of each key production indicator in each production stage is calculated. This potential value reflects the probability and extent to which the indicator can achieve its target value through optimization in the current production stage. Then, production stages with optimization potential values ​​exceeding a set threshold and their corresponding indicator combinations are selected, and these combinations can be improved by adjusting production parameters.

[0183] Step S154: Combining the reservoir production coupling characteristics, analyze the key production parameters that affect the production indicators within the optimization space, and determine the production parameters that have a significant impact on the target indicators.

[0184] By analyzing the reservoir production coupling characteristics, we identified key production parameters within the selected optimization space to determine the critical factors influencing these parameters. The reservoir production coupling characteristics reflect the interaction between reservoir parameters and production parameters. In-depth research into these parameters helps us determine which adjustments can significantly impact the target production indicators. For example, for the target indicator of gas production, parameters such as production pressure and extraction rate may be key influencing factors.

[0185] Step S155: Based on the optimization target of production indicators, determine the adjustment direction of each key production parameter. When the gas production is lower than the expected threshold and the gas production pressure is identified as a key influencing parameter, set the adjustment direction of the gas production pressure to increase.

[0186] Based on the optimization goals of production indicators, specific adjustment directions are determined for each key production parameter. If a production indicator is below a preset expected threshold, and a certain production parameter is identified as a key parameter that significantly affects that indicator, then the adjustment direction for that production parameter can be determined based on the relationship between the two. For example, when gas production is below the expected threshold, and gas production pressure is identified as a key influencing parameter, analysis shows that increasing gas production pressure can promote an increase in gas production; therefore, the adjustment direction for gas production pressure is set to increase.

[0187] Step S156: Based on the actual production boundary conditions of the coalbed methane well, determine the feasible range for adjusting each production parameter, ensuring that the adjustment direction is within the constraints of equipment load and environmental protection requirements.

[0188] After determining the adjustment direction of each key production parameter, and considering the actual production boundary conditions of the coalbed methane well, a feasible adjustment range for each parameter is determined. The equipment operating load limits in the actual production boundary conditions stipulate that production parameters cannot exceed the maximum capacity of the equipment, and environmental emission requirements also impose certain restrictions on the adjustment of production parameters. Therefore, when determining the adjustment range of production parameters, it is essential to ensure that the adjustment direction and magnitude are within these constraints to guarantee the safety, stability, and environmental protection of the production process.

[0189] Step S157: Integrate the optimization space, influencing parameters, adjustment directions and feasible range of key production indicators to form a corresponding description of the optimization direction of production parameters.

[0190] This process integrates information such as the optimization space of key production indicators (i.e., the optimizable indicators and their potential values ​​at each production stage, the key production parameters affecting these indicators, the adjustment directions for each key production parameter, and the determined feasible adjustment ranges. Organized according to a set logic and format, it forms a corresponding description of the production parameter optimization direction. This description indicates which production stages, which production indicators, which key production parameters can be adjusted, in which direction, and within what range, to achieve optimization of the production indicators.

[0191] Step S158: Standardize the format of the target production dynamic simulation results and the optimization direction of production parameters to generate output data containing simulation results and optimization directions. Send the output data to the coalbed methane well production control decision system and establish an update mechanism for the output results. When new coalbed methane well production-related data is received, repeat the above simulation process to update the target production dynamic simulation results and the optimization direction of production parameters.

[0192] The target production dynamic simulation results and production parameter optimization directions are standardized to conform to preset data format requirements, facilitating data storage, transmission, and use. After generating output data containing simulation results and optimization directions, it is sent to the coalbed methane well production control and decision-making system. Simultaneously, an output result update mechanism is established. When new coalbed methane well production-related data is received, such as new reservoir geological data or production operation data, the above simulation process is repeated to regenerate the target production dynamic simulation results and production parameter optimization directions, achieving dynamic updates of the output results and ensuring timely reflection of changes in the production process.

[0193] In one exemplary embodiment, an artificial intelligence-based dynamic simulation system for coalbed methane well production is provided. This system can be a terminal, server, etc., and its internal structure diagram can be as follows: Figure 2 As shown, the AI-based dynamic simulation system for coalbed methane well production includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, near-field communication, or other technologies. When the computer program is executed by the processor, it implements an AI-based dynamic simulation method for coalbed methane well production. The display unit is used to generate a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device can be a touch layer covering the display screen, or a button, trackball, or touchpad set on the shell of the AI ​​production dynamic simulation system for coalbed methane wells, or an external keyboard, touchpad, or mouse, etc.

[0194] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A method for dynamic simulation of coalbed methane well production using artificial intelligence, characterized in that, The method includes: Receive coalbed methane well production-related data, which includes coalbed methane well reservoir geological data, production operation data, fluid property data and wellbore structure data, and all data carry corresponding production stage identifiers and spatial location identifiers; By analyzing the coalbed methane well production-related data through the reservoir production collaborative sensing network, the coupling relationship between reservoir geological conditions and production operation behavior is explored, and reservoir production coupling characteristics are generated. These reservoir production coupling characteristics include information on the interaction between reservoir parameter changes and production parameter adjustments. The reservoir production coupling characteristics are input into the artificial intelligence production dynamic simulation model. By combining the fluid migration law of coalbed methane wells with the response characteristics of the production system, the entire life cycle production process of coalbed methane wells is dynamically simulated to obtain preliminary production dynamic simulation results. By introducing actual production boundary conditions of coalbed methane wells through a production constraint feedback mechanism, the preliminary production dynamic simulation results are adaptively adjusted to correct the deviation between the simulation results and the boundary conditions, thereby obtaining the target production dynamic simulation results. Output the target production dynamic simulation results and the corresponding production parameter optimization directions; The process involves analyzing coalbed methane well production-related data through a reservoir production collaborative sensing network to uncover the coupling relationship between reservoir geological conditions and production operation behavior, generating reservoir production coupling characteristics, including: The coalbed methane well production-related data are divided into reservoir geology data subsets, production operation data subsets, fluid property data subsets, and wellbore structure data subsets according to data attributes. Each data subset retains complete production stage identifiers and spatial location identifiers. Through the attribute analysis layer of the reservoir production collaborative sensing network, the core attribute features of each data subset are extracted. The core attribute features of the reservoir geology data subset reflect the distribution characteristics of reservoir porosity and permeability. The core attribute features of the production operation data subset reflect the regulation characteristics of gas production pressure and liquid production. The core attribute features of the fluid property data subset reflect the physical characteristics of fluid viscosity and density. The core attribute features of the wellbore structure data subset reflect the structural characteristics of wellbore diameter and casing depth. Based on the extracted core attribute features, attribute association chains are constructed within each data subset. These attribute association chains reflect the mutual influence between different attributes within the same data subset. The attribute association chains of the reservoir geology data subset reflect the relationship between porosity and permeability, while the attribute association chains of the production operation data subset reflect the relationship between gas production pressure and liquid production. By using the cross-set association mining unit of the reservoir production collaborative sensing network, we can analyze the attribute interaction relationship between the reservoir geological data subset and the production operation data subset, identify the impact path of reservoir parameter changes on production operation parameters, and quantify the impact of permeability changes on the gas production pressure regulation effect. The attribute matching relationship between the fluid property data subset and the reservoir geology data subset is analyzed, the matching degree between fluid viscosity, density and reservoir pore structure is calculated, and the reservoir's ability to contain and transport fluids is evaluated based on the matching degree. Analyze the attribute matching relationship between the wellbore structure data subset and the production operation data subset, determine the matching degree between wellbore diameter, casing depth and production pressure control, and determine the effective control range of production operation parameters under different wellbore structures; The influence paths, matching states, and adaptation degrees obtained from cross-set association mining are integrated to form preliminary reservoir production association features. These preliminary reservoir production association features include unidirectional influence and bidirectional adaptation information between attributes of different data subsets. Through the coupling reinforcement layer of the reservoir production collaborative sensing network, the preliminary correlation features of reservoir production are interactively reinforced, the representation weight of the bidirectional interaction information between reservoir conditions and production operations is enhanced, and the feedback of production parameter adjustment caused by changes in reservoir parameters and the reaction information of production operations on reservoir state are strengthened. The similarity between the enhanced association features and the coupling relationship instances in the historical production data of coalbed methane wells is calculated. When the similarity is lower than the preset threshold, the coupling relationship details of the current association features and the changes in coupling relationships under different production stages are supplemented based on the coupling relationship instances in the historical data. The improved correlation features are subjected to unified characterization processing, and different types of coupled correlation information are integrated into a unified feature expression to generate reservoir production coupling features that contain the interaction information between reservoir parameter changes and production parameter adjustments.

2. The artificial intelligence-based dynamic simulation method for coalbed methane well production according to claim 1, characterized in that, The process involves inputting the reservoir production coupling characteristics into an artificial intelligence production dynamic simulation model, combining the fluid migration patterns of coalbed methane wells with the response characteristics of the production system, to dynamically simulate the entire life cycle production process of coalbed methane wells, obtaining preliminary production dynamic simulation results, including: The reservoir production coupling characteristics are divided according to the coalbed methane well production stages, forming stage coupling characteristics corresponding to different production stages. The production stages include the initial drainage period, the stable production period, and the production decline period. The coupling characteristics of each stage are input into the stage adaptation layer of the AI ​​production dynamic simulation model. The simulation parameters of the model are adjusted according to the core characteristics of different production stages, and the simulation parameters of the model are configured according to the production characteristics of each stage. The fluid migration simulation unit of the AI-powered dynamic production model, combined with the fluid migration law of coalbed methane wells, simulates the fluid flow process at each production stage, simulating the fluid seepage in the reservoir, the flow in the wellbore, and the production process. The production system response unit of the artificial intelligence production dynamic simulation model, combined with the production system response characteristics, simulates the feedback state of the production system after the adjustment of production operation parameters, including the changes in gas production rate and liquid production rate. By fusing the fluid transport simulation results with the production system response simulation results, the stage production dynamic simulation results for each production stage are obtained. The stage production dynamic simulation results include the change trajectory of key production indicators within that production stage. Analyze the connection between the simulation results of the production dynamics of adjacent production stages to ensure that the simulation results of the previous stage can be reasonably transitioned to the next stage and maintain the continuity of the production process. Based on the stage production dynamic simulation results after connection, a production dynamic evolution path for the entire life cycle of a coalbed methane well is constructed. The production dynamic evolution path includes the continuous changes of production indicators at each stage and the characteristics of stage transition nodes. By using the trend prediction unit of the AI-powered production dynamics simulation model, the dynamic evolution path of the entire production lifecycle is extended and simulated, and the changing trends of production indicators in subsequent production stages are predicted. The simulation results, evolution paths, and trend prediction results of each stage are integrated to form complete simulation data covering the entire life cycle of the production process. The complete simulation data includes information on the changes and spatial distribution of production indicators over time. The complete simulation data is standardized and the simulation information is organized according to a unified output format to obtain preliminary production dynamic simulation results. The preliminary production dynamic simulation results include the production dynamic changes of coalbed methane wells from the initial stage of drainage to the production decline period.

3. The artificial intelligence-based dynamic simulation method for coalbed methane well production according to claim 1, characterized in that, The process of introducing actual production boundary conditions for coalbed methane wells through a production constraint feedback mechanism to adaptively adjust the preliminary production dynamic simulation results, correct deviations in the simulation process, and obtain the target production dynamic simulation results includes: Collect actual production boundary conditions of coalbed methane wells, including reservoir pressure upper limit, gas production control range, equipment operating load limit and environmental emission requirements; The actual production boundary conditions of the coalbed methane well are input into the constraint analysis unit of the production constraint feedback mechanism to analyze the constraint type and constraint range of each boundary condition, and output the restricted production indicators and their specific constraint requirements. By comparing the production indicators in the preliminary production dynamic simulation results with the analyzed boundary conditions, simulation data that exceeds the constraint range is identified, and the corresponding production stage identifier and specific production indicators are recorded. Based on the reservoir production coupling characteristics, and through the preset deviation analysis rules, it is determined that the simulation data that exceeds the constraint range is due to the deviation of parameter settings or insufficient mining of coupling relationship during the simulation process, resulting in the index exceeding the limit. Based on the deviation type determination result, the corresponding parameter adjustment operation is executed. For the excess caused by parameter setting deviation, the corresponding fluid transport coefficient or production system response coefficient in the model is corrected. For the exceedance caused by insufficient coupling relationship mining, supplement the corresponding reservoir production coupling correlation information and strengthen the mutual influence relationship between related attributes; The adjusted simulation parameters and the supplemented coupling correlation information are input into the artificial intelligence production dynamic simulation model, and the production dynamic simulation is performed again to obtain the corrected production dynamic simulation results. The revised production dynamics simulation results are compared with the actual production boundary conditions to check whether there is still data that exceeds the constraint range. When simulated data that still exceeds the constraints is detected, the iterative optimization process is initiated, and deviation judgment, parameter adjustment and re-deduction operations are performed repeatedly until all production indicators meet the actual production boundary conditions. The simulation results that finally meet the constraints are smoothed to optimize the continuity of production index change curves and stage transitions, thus obtaining the target production dynamic simulation results.

4. The artificial intelligence-based dynamic simulation method for coalbed methane well production according to claim 1, characterized in that, The cross-set association mining unit of the reservoir production collaborative sensing network analyzes the attribute interaction relationships between the reservoir geological data subset and the production operation data subset, and identifies the impact path of reservoir parameter changes on production operation parameters, including: Key reservoir parameters affecting production operations are extracted from the core attribute features of a subset of reservoir geological data. These key reservoir parameters include permeability, porosity, and gas saturation. Key production parameters affected by reservoir parameters are extracted from the core attribute features of the production operation data subset. These key production parameters include gas production pressure, liquid production rate, and extraction rate. Construct a correlation matrix between key reservoir parameters and key production parameters. The elements in the matrix represent the potential correlation between the corresponding reservoir parameters and production parameters. By using the association verification module of the cross-set association mining unit, combined with the coalbed methane well production mechanism, the rationality of potential associations in the association matrix is ​​verified, and false associations that do not conform to the production mechanism are eliminated. Strength analysis was performed on the verified reasonable correlations to determine the influence strength of each reservoir parameter on the corresponding production parameter, and to quantify the difference between the influence of permeability change on gas production pressure and the influence of porosity change on gas production pressure. Based on the results of the influence intensity analysis, a direct influence path from reservoir parameters to production parameters is constructed, and a direct influence relationship between increased permeability and the expansion of the adjustable range of gas production pressure is established. The study analyzes the pathways through which reservoir parameters indirectly affect production parameters via intermediate parameters, and identifies the indirect influence of porosity on reservoir gas saturation, thereby affecting liquid production. By integrating direct and indirect impact paths, a complete network of impact paths of reservoir parameter changes on production operation parameters is formed, which includes the impact order and correlation strength of each path. The influence path network is simplified, and the simplified influence path network is output to show the main influence paths of reservoir parameter changes on production operation parameters.

5. The artificial intelligence-based dynamic simulation method for coalbed methane well production according to claim 2, characterized in that, The fluid migration simulation unit, which uses an artificial intelligence-based dynamic model to simulate fluid migration patterns in coalbed methane wells, simulates the fluid flow process at each production stage, including seepage in the reservoir, flow within the wellbore, and production processes. The stage coupling characteristics corresponding to the production stage are input into the fluid transport simulation unit to extract the feature information related to fluid transport, including the relevant features of reservoir porosity, permeability, fluid viscosity, density and production pressure. Based on the extracted relevant features, the initial conditions for fluid migration in this production stage are determined. The initial conditions include the initial reservoir pressure, initial gas saturation, and initial fluid distribution state. Based on the fluid migration law of coalbed methane wells, the basic assumptions of fluid migration are set. The assumptions include reasonable assumptions that the fluid flow is isothermal and the reservoir medium is a homogeneous and isotropic medium, which are consistent with actual production. The fluid transport simulation unit uses the seepage simulation module to calculate the fluid seepage process in the reservoir based on Darcy's law, and to deduce the flow trajectory and flow rate of the fluid from the reservoir pores to the wellbore. The phase change process of fluid in reservoir is simulated, and the fluid property characteristics are combined to deduce the process of coalbed methane desorption from adsorbed state to free state and the mixing flow of free fluid with water. The wellbore flow simulation module of the fluid transport simulation unit simulates the flow process of fluid after it enters the wellbore, and deduces the upward trajectory, velocity change and pressure loss of the fluid in the wellbore. Based on the structural characteristics of the wellbore, the differences in the flow state of fluid at different depths of the wellbore are analyzed, and the influence of the change in wellbore diameter on fluid flow is calculated. The process of fluid production at the wellhead is simulated, and the separation and metering process of fluid after it flows out of the wellbore is deduced to obtain the fluid production indicators of gas production rate and liquid production rate at this production stage. The seepage simulation results, wellbore flow simulation results, and production simulation results are integrated to form a complete fluid migration simulation result for this production stage. The fluid migration simulation result includes fluid flow information at each stage of the reservoir, wellbore, and wellhead.

6. The artificial intelligence-based dynamic simulation method for coalbed methane well production according to claim 3, characterized in that, Based on the reservoir production coupling characteristics, and through preset deviation analysis rules, the simulation data exceeding the constraint range is determined to be due to parameter setting deviations or insufficient exploration of coupling relationships during the simulation process, resulting in the exceeding of index limits. This includes: Extract the production stages and related production indicators corresponding to the simulation data that exceeds the constraints, and identify the production stages and specific production indicators that exceed the limits. Retrieve the reservoir production coupling characteristics corresponding to the production stage, and extract the coupling information related to the over-limit production indicators according to the preset association rules, including the reservoir parameters that affect the corresponding indicators, production operation parameters, and the interaction between the parameters. The simulation parameters related to this production stage in the AI ​​production dynamic simulation model are compared with the benchmark values ​​in the standard parameter library to identify parameters with significant deviations. The difference between the parameter settings in the AI ​​production dynamic simulation model and the parameter values ​​in actual production is calculated. When the difference exceeds the preset tolerance, it is determined that the parameter setting deviation may lead to the production index exceeding the limit. Based on the reservoir production coupling characteristics, check whether the set of standard coupling relationships related to the out-of-limit indicators is complete and identify the types of missing association relationships; After supplementing the missing coupling relationships in the simulation environment, perform production indicator trend prediction and calculate the probability of indicator values ​​regressing within the constraint range. After correcting the biased extrapolation parameters in the simulation environment, the production indicator trend prediction is performed, and the probability of the indicator value returning to the constraint range is calculated. Compare the index regression probabilities corresponding to the two correction methods, and select the correction method with the higher index regression probability as the preferred option. Based on the knowledge base of coalbed methane well production mechanism, the probability weight of index exceeding the limit is calculated due to two reasons: parameter setting deviation and insufficient exploration of coupling relationship. Based on the combined results of the predicted correction effect and the probability weight calculation, it was ultimately determined whether the simulation data exceeding the constraint range was caused by parameter setting deviations during the simulation process or by insufficient mining of coupling relationships.

7. The artificial intelligence-based dynamic simulation method for coalbed methane well production according to claim 1, characterized in that, The coupling enhancement layer of the reservoir production collaborative sensing network performs interactive enhancement processing on the preliminary correlation features of reservoir production, strengthens the representation weight of the bidirectional interaction information between reservoir conditions and production operations, and enhances the feedback of production parameter adjustments caused by changes in reservoir parameters and the reaction information of production operations on reservoir state, including: The preliminary correlation features of reservoir production are input into the feature decomposition module of the coupled reinforcement layer to obtain the unidirectional influence features of reservoir on production, the unidirectional influence features of production on reservoir, and the bidirectional adaptation features of the two. The unidirectional influence of reservoir on production is enhanced, the influence of reservoir parameter changes on production operation parameters is amplified, and the influence of permeability and porosity parameter changes on gas production pressure and liquid production rate is enhanced. The characteristics of the unidirectional impact of production on the reservoir are enhanced, highlighting the characterization of the reaction of production operation parameter adjustment on reservoir state, and enhancing the characterization of the degree of influence of gas production pressure adjustment on reservoir pressure distribution and gas saturation. By using the bidirectional interaction module of the coupled reinforcement layer, an interactive feedback loop between the reservoir and production is constructed to simulate the cyclical process in which changes in reservoir parameters trigger adjustments in production parameters, and then the adjustments in production parameters have a feedback effect on reservoir parameters. Based on the interactive feedback loop, a two-way interaction enhancement feature is generated, which includes the change trajectory of each parameter and the intensity of mutual influence during the cyclic interaction process. The enhanced unidirectional influence feature is fused with the bidirectional action enhancement feature to form a preliminary enhanced coupling feature, which simultaneously reflects unidirectional influence and bidirectional interaction information. The consistency of the initial enhanced coupling features with the bidirectional interaction instances in the historical data of coalbed methane well production dynamics is verified, and the feature fusion weights are adjusted based on the verification results. Based on the verification results, the fusion weights of the unidirectional influence feature and the bidirectional effect enhancement feature were adjusted, and the adjusted fusion feature was further optimized in detail to supplement the difference information of bidirectional effect under different production conditions, so that the feature has the ability to adapt to multiple production scenarios. The output is a correlation feature after interactive enhancement, which highlights the bidirectional interaction between reservoir conditions and production operations.

8. The artificial intelligence-based dynamic simulation method for coalbed methane well production according to claim 2, characterized in that, The analysis of the connection between the simulation results of adjacent production stages, ensuring a reasonable transition from the simulation results of the previous stage to the next and maintaining the continuity of the production process, includes: Obtain the stage production dynamic simulation results of two adjacent production stages, including the end-stage production index data of the previous stage and the initial stage production index data of the next stage. Compare the production indicator data at the end of the previous stage with the production indicator data at the beginning of the next stage, analyze the degree of difference between the two, and detect whether there are step changes in the production indicator data that exceed the preset threshold. Based on the mechanism of coalbed methane well production stage transition, the normal transition law between two adjacent stages was obtained. When transitioning from the initial stage of drainage to the stable production stage, the rate of change of gas production approaches zero, and the rate of change of liquid production approaches zero after being negative. Based on the normal transition law, a stage transition evaluation standard is constructed. The stage transition evaluation standard defines a reasonable range for the transition of production indicators between adjacent stages. If the transition exceeds the reasonable range, it is judged as unreasonable. If the difference between the production indicators at the end of the previous stage and the beginning of the next stage is within a reasonable range, then the simulation results of the current stage are retained. If the difference exceeds a reasonable range, then according to the stage transition diagnosis rules, determine whether the root cause of the transition problem is the deviation of the simulation results in the previous stage or the improper setting of the simulation parameters in the next stage. Adjustments are made based on the cause. If the deviation is due to a deviation in the simulation results of the previous stage, the simulation parameters of the previous stage are corrected and the simulation is repeated to ensure that the final indicators fall within the reasonable range defined by the stage transition evaluation criteria. If the simulation parameters for the next stage are not set properly, adjust the model adaptation parameters for the next stage so that the difference between the initial indicators and the final indicators of the previous stage meets the stage transition evaluation criteria. The adjusted simulation results of production dynamics between adjacent stages were regenerated to verify the connection relationship again, which conformed to the stage transition law. Repeat the above comparison, analysis, adjustment and verification process for all adjacent production stages to ensure that the dynamic evolution path of the entire production life cycle meets the continuity requirement, and that the changes in production indicators between stages meet the continuity requirement.

9. An artificial intelligence-based dynamic simulation system for coalbed methane well production, characterized in that, include: processor; A machine-readable storage medium for storing machine-executable instructions of the processor; The processor is configured to execute the artificial intelligence production dynamic simulation method for coalbed methane wells according to any one of claims 1 to 8 by executing the machine-executable instructions.

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