Gas reservoir dynamic early warning method and system based on sulfur water invasion coupling precursor identification

Abstract

The application discloses a gas reservoir dynamic early warning method and system based on sulfur water invasion coupling precursor identification, and relates to the field of geological engineering and production safety monitoring. The method comprises the following steps: collecting characteristic data of a gas reservoir well, mapping various types of data to form well characteristic information; combining the well characteristic information with the characteristic information of adjacent wells to correct and generate coupling sensitivity; generating a local coupling disturbance index based on the sulfur water difference between adjacent wells and the pressure and temperature difference; combining the coupling sensitivity data and the local disturbance data to generate a single-well anomaly index, forming cumulative evolution data; combining the single-well cumulative evolution data and the local disturbance data to generate a global coupling risk enhancement index; combining the single-well anomaly index to form a single-well risk index, mapping the risk index to a discrete risk level to form gas reservoir dynamic early warning information. Through multi-dimensional monitoring data fusion, hierarchical risk modeling of single wells and the whole, and graded early warning output, comprehensive identification, systematic evaluation and fine early warning of the sulfur water invasion coupling precursor of the gas reservoir are realized.

Description

Technical Field

[0001] This invention relates to the field of geological engineering and production safety monitoring, specifically to a dynamic early warning method and system for gas reservoirs based on the identification of precursors to sulfur-water intrusion coupling. Background Technology

[0002] During long-term development, gas reservoirs commonly face the dual effects of hydrogen sulfide intrusion and water intrusion. The combined effect of these factors can easily lead to a sharp decline in gas well productivity and wellbore safety risks. Hydrogen sulfide, a typical corrosive gas, causes material strength degradation in the wellbore and tubing, increasing the risk of wellbore damage and environmental leakage. Water intrusion, on the other hand, alters the pressure distribution and fluid transport pathways of the gas reservoir, leading to enhanced reservoir heterogeneity and unstable evolution of the gas production system. When sulfur and water erosion occur in a coupled manner, conventional monitoring methods often fail to identify early warning signs in a timely manner, resulting in delayed or even nonexistent early warnings.

[0003] Current technologies for dynamic monitoring and early warning of gas reservoirs mainly rely on single-parameter or simple multi-parameter threshold determination methods, typically analyzing only pressure, gas production, or water intrusion independently. These methods have significant limitations in practical applications. Firstly, they ignore the coupling relationship between multiple physical quantities such as hydrogen sulfide concentration, temperature, and water production, failing to accurately reflect the overall risk evolution of the gas reservoir. Secondly, they lack systematic modeling of the spatial correlation between wells, leading to delays in identifying regional risks. Especially under the combined effects of hydrogen sulfide and water intrusion, gas reservoirs often exhibit global and sudden dynamic instability processes; relying solely on anomaly monitoring of single wells or single parameters is insufficient to provide reliable early warnings for gas reservoir development.

[0004] In recent years, researchers have attempted to analyze gas reservoir risks using multi-source monitoring data and numerical simulation methods. However, most studies remain at the qualitative level, lacking effective feature mapping mechanisms and dynamic risk quantification indicators. Furthermore, existing methods lack a unified framework for handling coupling effects and historical evolution characteristics between gas wells, resulting in unstable risk identification results and hindering the construction of dynamic early warning systems. Therefore, how to comprehensively integrate multi-dimensional monitoring data, establish spatial relationships and temporal evolution mechanisms between wells, and develop refined precursor identification and dynamic early warning methods under the complex coupling background of sulfur and water intrusion has become a key technical problem urgently needing to be solved for the safe and efficient development of gas reservoirs. Summary of the Invention

[0005] Based on the shortcomings of the prior art described above, the purpose of this invention is to provide a method and system for dynamic early warning of gas reservoirs based on the identification of precursors of sulfur-water intrusion coupling, so as to solve the above-mentioned technical problems.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a gas reservoir dynamic early warning method based on sulfur-water intrusion coupling precursor identification, comprising:

[0007] Data on formation pressure, gas production, water production, hydrogen sulfide concentration, wellbore temperature, and water injection volume of gas reservoir wells are collected and mapped to a unified feature space to form well feature information.

[0008] The coupling sensitivity is corrected by combining the well feature information with the feature information of adjacent wells, and coupling sensitivity data is generated.

[0009] Based on the differences in sulfur water and pressure and temperature between adjacent wells, a local coupling disturbance index is generated to form local disturbance data;

[0010] A single-well anomaly index is generated by combining coupled sensitivity data and local disturbance data, and historical anomaly indices are cumulatively evolved to form cumulative evolution data.

[0011] By combining single-well cumulative evolution data with local disturbance data, a global coupled risk enhancement index is generated, forming gas reservoir precursor characteristic data;

[0012] By combining the global coupled risk enhancement index and the single-well anomaly index, a single-well risk index is formed. The risk index is then mapped to discrete risk levels to generate dynamic early warning information for gas reservoirs.

[0013] The present invention is further configured such that the well feature information formed by mapping the acquired gas reservoir data includes:

[0014] Data on reservoir pressure, gas production, water production, hydrogen sulfide concentration, wellbore temperature, and water injection volume are collected from the gas reservoir well. Among them, the reservoir pressure is collected by a pressure sensor, the gas production is collected by a flow meter, the water production is collected by a water meter, the hydrogen sulfide concentration is collected by a gas analyzer, the wellbore temperature is collected by a temperature sensor, and the water injection volume is collected by the water injection control system.

[0015] Based on the collected monitoring data, a corresponding feature set is established, and nonlinear mapping processing is performed on different physical quantities to form unified well feature information;

[0016] Well feature information is aggregated in both time and space dimensions to form a well feature information set.

[0017] The present invention is further configured such that generating coupling sensitivity data includes:

[0018] Based on the set of well feature information, the well feature information is coupled and associated with the feature information of neighboring wells. A set of neighboring wells is established based on the spatial location relationship between wells, and spatial weights are formed according to the spatial distance attenuation relationship.

[0019] Adjacent well heterogeneity indices are generated based on differences in hydrogen sulfide concentration, water production, pressure, and temperature.

[0020] A coupling sensitivity correction factor is generated by combining the relationship between hydrogen sulfide and water production in the well and pressure and temperature conditions.

[0021] A preliminary coupling metric is formed based on unified well characteristic information, adjacent well heterogeneity index, spatial weight, and coupling sensitivity correction factor.

[0022] The initial coupling metric is modulated by the relationship between reservoir pressure and gas production to generate the final coupling sensitivity.

[0023] The present invention is further configured such that generating the local coupling perturbation index to form local perturbation data includes:

[0024] Based on the final coupling sensitivity, the differences in hydrogen sulfide concentration, water production, pressure and temperature between wells are coupled to form the neighboring well difference quantity.

[0025] Based on the differential values ​​of adjacent wells, a nonlinear interwoven structure is constructed to adjust the sulfur-water coupling characteristics and the pressure and temperature conditions of adjacent wells, thereby forming an intermediate perturbation expression.

[0026] The intermediate disturbance expression is modified based on the coupling sensitivity of adjacent wells to generate a local coupling disturbance index;

[0027] The local coupling disturbance indices of all well pairs are aggregated to form a local disturbance data matrix.

[0028] The present invention is further configured such that generating a single-well anomaly index and forming cumulative evolution data includes:

[0029] Based on coupling sensitivity data and local disturbance data, a single-well anomaly index is generated based on the difference relationship between the well and its neighboring wells.

[0030] Based on the single-well anomaly index, the historical anomaly index is cumulatively evolved to form cumulative evolution data that is updated over time.

[0031] Based on the cumulative evolution data, the cumulative evolution data of all wells are arranged by well number to generate a single-well anomaly evolution matrix.

[0032] The present invention is further configured such that combining single-well cumulative evolution data with local disturbance data includes:

[0033] The cumulative evolution data and local disturbance data between wells and adjacent wells are coupled to form the cumulative disturbance coupling amount between wells;

[0034] After the cumulative disturbance coupling between wells is formed, the coupling of adjacent wells of each well is summarized to generate the global interaction strength of the wells;

[0035] After the overall well interaction intensity is formed, it is amplified by combining time evolution factors to form a time evolution factor covering the entire well.

[0036] The present invention is further configured such that generating the global coupling risk enhancement index and forming gas reservoir precursor characteristic data includes:

[0037] By combining the global interaction intensity of the well with the time evolution factor, a preliminary global coupling risk index is generated;

[0038] Based on the preliminary global coupling risk index, the preliminary global coupling risk index is fused with local disturbance data to generate an enhanced global coupling risk index.

[0039] Based on the enhanced global coupling risk index, combined with cumulative evolution data and local disturbance data, a set of gas reservoir precursor characteristic data is formed.

[0040] The present invention is further configured such that the formation of the single-well risk index includes:

[0041] By combining single-well cumulative evolution data and global coupling risk enhancement indicators, a single-well coupling risk quantity is formed, which reflects the risk integration status of a single well under the background of global coupling.

[0042] After the risk quantity of a single well is formed, it is fused with the local disturbance data of adjacent wells, and a single-well risk index is generated through a higher-order coupling relationship.

[0043] The present invention is further configured such that the dynamic early warning information for gas reservoir formation includes:

[0044] The single-well risk index is converted into discrete risk levels according to a nonlinear mapping relationship, and each discrete level represents a different level of risk status of a single well.

[0045] All individual well discrete risk levels are combined into a dynamic early warning information set for gas reservoirs based on the geographical and geological layout between wells. This set provides the distribution of gas reservoir precursor risks across the entire well range.

[0046] This invention also provides a gas reservoir dynamic early warning system based on sulfur-water intrusion coupling precursor identification, the system comprising:

[0047] Well feature construction module: Collects data on formation pressure, gas production, water production, hydrogen sulfide concentration, wellbore temperature and water injection volume of gas reservoir wells, and maps various types of data to a unified feature space to form well feature information;

[0048] Coupling sensitivity generation module: Combines well feature information with neighboring well feature information to correct coupling sensitivity and generate coupling sensitivity data;

[0049] Local disturbance analysis module: Based on the differences in sulfur water and pressure and temperature between adjacent wells, a local coupled disturbance index is generated to form local disturbance data;

[0050] Single-well anomaly evolution module: Combines coupled sensitivity data and local disturbance data to generate single-well anomaly index, and performs cumulative evolution processing on historical anomaly index to form cumulative evolution data;

[0051] Global Risk Enhancement Module: Combines single-well cumulative evolution data with local disturbance data to generate a global coupled risk enhancement index, forming gas reservoir precursor characteristic data;

[0052] Single-well risk assessment and early warning module: Combines global coupled risk enhancement indicators and single-well anomaly index to form a single-well risk index, and maps the risk index to discrete risk levels to form dynamic early warning information for gas reservoirs.

[0053] This invention provides a gas reservoir dynamic early warning method and system based on the identification of sulfur-water intrusion coupling precursors. The method collects data on reservoir well production pressure, gas production, water production, hydrogen sulfide concentration, wellbore temperature, and water injection volume, mapping these data to a unified feature space to form well feature information. It then combines the well feature information with neighboring well feature information to perform coupling sensitivity correction, generating coupling sensitivity data. Based on the differences in sulfur-water content, pressure, and temperature between neighboring wells, it generates a local coupling disturbance index, forming local disturbance data. Combining the coupling sensitivity data and local disturbance data, it generates a single-well anomaly index, and performs cumulative evolution processing on historical anomaly indices to form cumulative evolution data. Combining the single-well cumulative evolution data with local disturbance data generates a global coupling risk enhancement index, forming gas reservoir precursor feature data. Finally, combining the global coupling risk enhancement index and the single-well anomaly index forms a single-well risk index, which is mapped to discrete risk levels to form dynamic early warning information for the gas reservoir. The beneficial effects include:

[0054] 1. Comprehensiveness of precursor identification: By collecting multi-dimensional data such as formation pressure, gas production, water production, hydrogen sulfide concentration, wellbore temperature, and water injection volume, and mapping and fusing them within a unified feature space, this method can comprehensively reflect the multi-physics state of the wellbore and gas reservoir. Compared with traditional methods that rely on a single monitoring parameter, this method can more accurately capture subtle anomalies in the sulfur-water intrusion coupling process, thereby improving the reliability of precursor identification.

[0055] 2. Systematic Risk Assessment: By establishing a coupled sensitivity correction mechanism between wells and adjacent wells, and combining local disturbance indices with cumulative evolution data, a multi-level risk indicator system from single wells to the global picture is formed. This system can reveal the combined impact of sulfur-water intrusion on the dynamics of single wells and the entire gas reservoir, making risk assessment no longer limited to local well points, but possessing a systematic and holistic approach.

[0056] 3. Refined Early Warning Information: By mapping the risk index of a single well to discrete risk levels and forming a dynamic early warning information set across the entire well, the visualization and hierarchical management of gas reservoir precursor risks are realized. This method not only reveals the spatial distribution characteristics of risks but also provides a basis for taking differentiated control measures for different risk levels, thereby achieving refined and practical dynamic early warning for gas reservoirs.

[0057] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0059] Figure 1 A flowchart illustrating a gas reservoir dynamic early warning method based on sulfur-water intrusion coupling precursor identification, as an exemplary embodiment of the present invention;

[0060] Figure 2 This is a schematic diagram of the structure of a gas reservoir dynamic early warning system based on sulfur-water intrusion coupling precursor identification, which is an exemplary embodiment of the present invention. Detailed Implementation

[0061] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0062] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0063] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0064] Example 1

[0065] Gas reservoir dynamic early warning method based on sulfur-water intrusion coupled precursor identification, such as Figure 1 As shown, it includes:

[0066] Data on formation pressure, gas production, water production, hydrogen sulfide concentration, wellbore temperature, and water injection volume of gas reservoir wells are collected and mapped to a unified feature space to form well feature information.

[0067] The coupling sensitivity is corrected by combining the well feature information with the feature information of adjacent wells, and coupling sensitivity data is generated.

[0068] Based on the differences in sulfur water and pressure and temperature between adjacent wells, a local coupling disturbance index is generated to form local disturbance data;

[0069] A single-well anomaly index is generated by combining coupled sensitivity data and local disturbance data, and historical anomaly indices are cumulatively evolved to form cumulative evolution data.

[0070] By combining single-well cumulative evolution data with local disturbance data, a global coupled risk enhancement index is formed, generating gas reservoir precursor characteristic data;

[0071] By combining the global coupled risk enhancement index and the single-well anomaly index, a single-well risk index is formed. The risk index is then mapped to discrete risk levels to generate dynamic early warning information for gas reservoirs.

[0072] The present invention is further configured such that the well feature information formed by mapping the acquired gas reservoir data includes:

[0073] Data on reservoir well production pressure, gas production, water production, hydrogen sulfide concentration, wellbore temperature, and water injection volume are collected. Specifically, production pressure is collected by a pressure sensor, gas production by a flow meter, water production by a water meter, hydrogen sulfide concentration by a gas analyzer, wellbore temperature by a temperature sensor, and water injection volume by the water injection control system. In particular, comprehensive data collection is conducted on key operating parameters of the gas reservoir well, including: production pressure, gas production, water production, hydrogen sulfide concentration, wellbore temperature, and water injection volume. Production pressure is collected via a pressure sensor. The flow meter collects the gas production volume. Water meter collects water production The gas analyzer collects the hydrogen sulfide concentration. Temperature sensor collects wellbore temperature The water injection control system collects the water injection volume. Output the original dataset Complete and multi-dimensional data acquisition provides sufficient input, enabling dynamic early warning methods to accurately reflect changes in well conditions.

[0074] Based on the collected monitoring data, a corresponding feature set is established, and nonlinear mapping processing is performed on different physical quantities to form unified well characteristic information; specifically, multi-source acquired data... Mapping to a unified feature space eliminates dimensional differences and enhances high-risk features. The mapping employs nonlinear combination functions, including exponential, power, and composite denominator relationships, avoiding linear weighting or simple summation. The unified formula for calculating well feature information is as follows: ,in, For well Unified feature information, , , , , , , , , This is a non-linear mapping exponent, with a value range of [0.1, 3.0]. Amplify the effects of pressure and gas production while retaining water production characteristics. Suppress minor disturbances while considering sulfur concentration, temperature, and water injection volume. Emphasizing the coupling characteristics of pressure and hydrogen sulfide, the sensitivity to anomalies is enhanced by nonlinear suppression of gas production and water production.

[0075] Well feature information is aggregated in both time and space dimensions to form a well feature information set. Specifically, each well is mapped with features. Formation of well feature information set .

[0076] The present invention is further configured such that generating coupling sensitivity data includes:

[0077] Based on the well feature information set, the well feature information is coupled and associated with the feature information of neighboring wells. A neighboring well set is established based on the spatial location relationship between wells, and spatial weights are formed according to the spatial distance attenuation relationship. Specifically, each well is weighted according to its spatial coordinates. Define the set of adjacent wells Only distances within the threshold are included. The well inside: , Construct spatial weights using a scalable exponential decay method: ,distance Characterizing the spatial correlation strength between wells, the neighboring well set is determined by a threshold. Limiting the scope of spatial influence, spatial weight Near-wellbore areas are assigned high weights, and distant-wellbore areas are assigned low weights. Parameters Controlling spatial scale The decay power exponent controls the decay steepness, making spatial propagation adjustable;

[0078] Heterogeneity indices for adjacent wells are generated based on differences in hydrogen sulfide concentration, water production, pressure, and temperature; specifically, heterogeneity indices are constructed based on differences in raw quantities between wells. Amplify the effects of hydrogen sulfide and water production difference, and suppress the effects of pressure and temperature difference. ,in, , , , It is a power exponent of heterogeneity. Amplify the sulfur-water differential coupling effect. Nonlinear suppression is applied to high pressure or temperature difference conditions to avoid false high disturbance values. The heterogeneity index reflects the heterogeneity of adjacent wells in sulfur water intrusion behavior.

[0079] A coupling sensitivity correction factor is generated by combining the relationship between hydrogen sulfide and water production in the well, as well as pressure and temperature conditions; specifically, a correction factor is generated for each well. To amplify the sulfur-water coupling characteristics within the well: ,in, Nonlinear amplification of high-sulfur, high-water wells To prevent abnormal amplification of high voltage, Additional corrections are established by using injection volume and well temperature to suppress / enhance coupling. The in-well coupling sensitivity correction factor is dynamically adjusted according to changes in in-well sulfur-water coupling. , To adjust parameters; enhance the contribution of in-well sulfur-water coupling to coupling sensitivity, and ensure that the correction factor dynamically adapts to different well conditions;

[0080] A preliminary coupling metric is formed based on unified well characteristic information, adjacent well heterogeneity indices, spatial weights, and coupling sensitivity correction factors; specifically, unified characteristic information... Characteristic information of adjacent wells Heterogeneity index Spatial weights In-well coupling sensitivity correction factor Fusion: ,in, Capturing the power-law amplification effect of feature differences, Highly heterogeneous damping, external power By controlling the nonlinear sensitivity and integrating spatial and in-well factors, a preliminary characterization of spatial-local coupled sensitivity can be achieved. This is the characteristic difference amplification index, with a value range of [0.5, 2.0]. The heterogeneity damping index has a value range of [0.5, 2.5]. This is the external sensitivity index, with a value range of [0.5, 3.0]. To provide a preliminary coupling measurement, a nonlinear amplification and suppression mechanism is provided to improve the coupling sensitivity's ability to identify abnormal wells and ensure that the coupling characteristics between adjacent wells are accurately reflected under conditions of spatial and physical property heterogeneity.

[0081] The initial coupling metric is modulated by the relationship between reservoir pressure and gas production to generate the final coupling sensitivity. Specifically, pressure-gas production modulation is introduced to suppress spurious coupling and generate the final coupling sensitivity. Forming a coupling sensitivity matrix ,in, Magnify the contribution of high-pressure wells to coupling sensitivity. Suppressing spurious coupling caused by large differences in gas production, power exponent , , To achieve nonlinear adjustment, The pressure coupling index has a value range of [0.1, 2.0]. The gas production inhibition index has a value range of [0.1, 3.0]. This is the modulation attenuation index.

[0082] The present invention is further configured such that generating the local coupling perturbation index to form local perturbation data includes:

[0083] Based on the final coupling sensitivity, the differences in hydrogen sulfide concentration, water production, pressure, and temperature between wells are coupled to form the neighboring well difference; specifically, for each pair of wells Calculate the absolute differences in hydrogen sulfide concentration, water production, pressure, and temperature to form a set of differences between adjacent wells: , , , , , The difference in sulfur-water coupling between adjacent wells is the main driving force of the disturbance index. , It serves as a regulating factor, providing pressure and temperature damping in subsequent nonlinear interleaving to prevent excessive amplification under high pressure and high temperature. , , , To accurately characterize the local differences between adjacent well pairs;

[0084] A nonlinear interwoven structure is constructed based on the differential values ​​of adjacent wells to adjust the sulfur-water coupling characteristics and the pressure and temperature conditions of adjacent wells, forming an intermediate perturbation expression. Specifically, the differential values ​​of adjacent wells are introduced into the nonlinear interwoven structure to form an intermediate perturbation expression. ,in, The difference between sulfur and water is amplified exponentially, and their effects are coupled to demonstrate the sensitivity of sulfur-water coupling to local disturbances. Adjust the nonlinearity of the contributions from the sulfur gradient and the water production gradient, respectively. Control the overall external amplification index. Introducing pressure and temperature difference damping helps prevent excessive disturbances caused by high pressure or high temperature differences. By controlling the pressure and temperature damping strength respectively, a complex nonlinear response is formed, ensuring that the local disturbance index is sensitive to sulfur-water coupling and robust to pressure-temperature differences.

[0085] The intermediate perturbation expression is modified based on the coupling sensitivity of adjacent wells to generate a local coupling perturbation index; specifically, in the intermediate perturbation expression... Introducing adjacent well coupling sensitivity based on Correction, generating the final local coupling perturbation index ,in, Amplify the contribution of highly sensitive wells to the perturbation index, ensuring coupling sensitivity and greater significance for local perturbations. Introducing dual damping to avoid false high disturbances in high-pressure, high-temperature wells, external power Controlling the overall decay level ensures that the index remains numerically stable even under high sensitivity conditions. For sensitivity amplification index, , The pressure and temperature damping index is used to integrate the coupling sensitivity of adjacent wells and local differences to achieve complex nonlinear regulation, highlighting the local disturbance index of highly sensitive wells;

[0086] The local coupling disturbance indices of all well pairs are aggregated to form a local disturbance data matrix. Specifically, this matrix is ​​used for all well pairs in the gas reservoir. Convergence Local Disturbance Index This forms a local perturbation matrix. Each element The matrix characterizes the coupling strength of well pairs under sulfur water intrusion. It provides a global perspective on the distribution pattern of local disturbances.

[0087] The present invention is further configured such that generating a single-well anomaly index and forming cumulative evolution data includes:

[0088] Based on coupling sensitivity data and local disturbance data, a single-well anomaly index is generated according to the difference relationship between the well and its neighboring wells; specifically, for each well... and its adjacent well collection By combining local disturbance intensity, coupling sensitivity, and acquisition time difference, a single-well anomaly index is generated. ,in, For local coupling perturbation index Power-law amplification is applied to highlight high perturbations and their contribution to anomalies. Controlling the degree of nonlinear enhancement ensures that local perturbation differences elicit a nonlinear response. Introducing Coupling Sensitivity The impact of further nonlinear enhancement on the contribution of adjacent wells to the anomaly index. Adjusting the power of coupling sensitivity, Controlling the overall nonlinear superposition effect, To account for the data acquisition time difference, control the nonlinear amplification of the disturbance and sensitivity on the anomaly index. The time difference between well and data acquisition is exponentially decayed to suppress abnormal errors caused by asynchronous acquisition. As a time decay factor, it ensures that the abnormal index decays naturally over time;

[0089] Based on the single-well anomaly index, historical anomaly indices are cumulatively evolved to form cumulative evolution data updated over time; specifically, the current anomaly index of a single well is... Compared with historical cumulative data By combining exponential decay and power superposition methods, cumulative evolution data is generated: ,in, Exponential decay is applied to historical outlier data. This is a historical attenuation coefficient, used to control the rate of decay of the impact of historical anomalies, preserving historical trends while avoiding excessive accumulation. The current abnormal index is superimposed by a power. The nonlinear cumulative exponent controls the nonlinear enhancement, making the latest anomaly contribute significantly to the cumulative evolution. It combines historical decay to form a dynamic fusion, balancing historical and current anomalies. The dynamic fusion of historical and current anomalies ensures that the cumulative evolution data reflects both long-term trends and sensitively captures the latest anomaly changes. The combination of nonlinear power and exponential decay enhances the model's ability to respond to sudden anomalies while maintaining numerical stability.

[0090] Based on cumulative evolution data, the cumulative evolution data of all wells are arranged by well number to generate a single-well anomaly evolution matrix. Specifically, all wells... Cumulative evolution data Arranged by well number, forming a single-well anomaly evolution matrix. .

[0091] The present invention is further configured such that combining single-well cumulative evolution data with local disturbance data includes:

[0092] The cumulative evolution data and local disturbance data between wells and adjacent wells are coupled to form the inter-well cumulative disturbance coupling quantity; specifically, the well... and neighboring wells Single-well cumulative evolution data , With local disturbance index Coupling, generating well-to-well coupling quantity ,in, The cumulative anomalies of adjacent wells are amplified exponentially and summed to highlight the contribution of high-cumulative anomalies to well-pair coupling. , Adjusting the degree of nonlinear enhancement, Indicates the local disturbance index Introducing coupling regulation, Amplify the differences in local disturbances. Adjusting the overall nonlinear effects;

[0093] After the accumulated disturbance coupling between wells is formed, the coupling amounts of adjacent wells of each well are summarized to generate the global well interaction strength; specifically, for each well... Multiply the coupling values ​​of all adjacent wells together to form the global well interaction strength. The multiplication structure replaces simple summation, ensuring that any highly coupled well has a significant amplification effect on the global interaction strength. The nonlinear enhancement parameter controls the nonlinear enhancement and enhances the risk accumulation effect;

[0094] After the overall well interaction intensity is formed, it is amplified by incorporating time evolution factors to form a time evolution factor covering the entire well. Specifically, the time evolution factor is introduced to dynamically amplify the cumulative anomalies in the well. in, For the current time, As the baseline time scale, Risk amplifies over time. To amplify the time index and adjust the cumulative time effect, Suppressing wells with large accumulated anomalies. To adjust the constant and control the adjustment range, the exponent and power are combined to ensure that the time evolution both enhances the overall trend and avoids excessive amplification of anomalies. The time evolution factor dynamically reflects the trend of risk accumulation over time and, combined with accumulated anomalies, provides a precursor amplification mechanism.

[0095] The present invention is further configured such that generating the global coupling risk enhancement index and forming gas reservoir precursor characteristic data includes:

[0096] By combining the global interaction strength of the well with the time evolution factor, a preliminary global coupling risk indicator is generated; specifically, this is combined with the interaction strength... With time evolution factor Constructing preliminary global coupling risk indicators: ,in, This serves as a preliminary global coupling risk indicator, comprehensively reflecting the global coupling risk of the well. , This is the nonlinear amplification factor. , The nonlinear fusion of control interaction and time amplification effect ensures that the exponential structure guarantees the multiplicative amplification effect of the two influencing factors on the risk index, rather than a simple superposition.

[0097] Based on the preliminary global coupling risk indicators, these indicators are fused with local disturbance data to generate a global coupling risk enhancement indicator; specifically, all preliminary global coupling risk indicators are... Multiplication across the entire well range generates a global coupling risk enhancement index. The multiplicative structure ensures that any single high-risk index has a cumulative amplifying effect on the overall risk. Adjusting the nonlinear enhancement intensity strengthens the global coupling risk response;

[0098] Based on the enhanced global coupling risk index, combined with cumulative evolution data and local disturbance data, a set of gas reservoir precursor characteristic data is formed. Specifically, the enhanced global coupling risk index... Local perturbation matrix and single-well cumulative evolution data Combined data to form a set of precursory gas reservoir features: , The collection integrates global coupling risks, well-to-well local disturbances, and single-unit cumulative anomalies.

[0099] The present invention is further configured such that the formation of the single-well risk index includes:

[0100] By combining single-well cumulative evolution data and global coupling risk enhancement indicators, a single-well coupling risk quantity is formed, reflecting the risk integration status of a single well under the background of global coupling; specifically, single-well cumulative evolution data... Enhanced risk indicators of global coupling Combined, construct single-well coupled risk quantities ,in, This is a historical cumulative nonlinear exponent. It is a global risk projection index. This is the external magnification index of the global projection. Applying a power-law amplification to the historical cumulative anomalies of a single well, the power exponent To control the nonlinear contribution of historical anomalies to the integration of coupled risks, a constant is added to ensure that when... The time term is not zero. The overall coupling risk of the gas reservoir is introduced into the single-well level through power-law mapping, removing the baseline effect by subtracting one term, and the power exponent. With external power By controlling the curvature of the global risk projection and the intensity of the overall nonlinear response, the multiplicative structure ensures that historical cumulative anomalies and global coupled risks are multiplicatively coupled at the single-well level, which can amplify the risk expression when high historical anomalies and high global coupling coexist.

[0101] After the single-well coupled risk quantity is formed, it is fused with the local disturbance data of adjacent wells. A single-well risk index is generated through higher-order coupling relationships. Specifically, the single-well coupled risk quantity... Local disturbance data from adjacent wells A single-well risk index is generated through high-order product coupling and fusion. ,in, This is a single-well coupling risk amplification index. For the amplification index of disturbance from adjacent wells, each item Corresponding well and neighboring wells Higher-order coupling contribution, power Amplifying the response of single-well coupling risk under the coupling effect of adjacent wells, power This amplifies the sensitivity of local disturbances from neighboring wells to this coupling, replacing the summation structure with a product structure. This ensures that when any neighboring well provides significant coupling or disturbance, the product will be affected. This produces an exponential or power-law amplification effect, preventing high-risk local aggregations from being averaged out, through the product of multiples. Reflection well The cumulative coupling risk exposure in its neighborhood network can reflect the combined effect of multiple neighboring wells. The high-order product coupling structure highlights the neighborhood clustering risk, which makes it easier to identify local high-risk wells caused by the combined influence of multiple neighboring wells.

[0102] The present invention is further configured such that the dynamic early warning information for gas reservoir formation includes:

[0103] The single-well risk index is converted into discrete risk levels according to a nonlinear mapping relationship, with each discrete level representing a different level of risk status for the single well; specifically, the single-well risk index... Perform nonlinear normalized mapping and quantize into discrete levels To avoid numerical instability due to division by zero or extremely small values, a zero-prevention constant is used in the mapping. The mapping is defined as follows: ,in, This represents the maximum value of the risk index for a single well in the entire well network at the current moment. Round down. For the maximum number of grades, the fraction Normalize the single-well risk index to To avoid incomparability due to differences in absolute magnitude, the range is used. To prevent the denominator from being zero or the value from degenerating, the exponentiation... The normalized value is nonlinearly amplified or compressed to control the degree of widening or compression between high-risk and medium-to-low-risk levels, multiplied by Round down to obtain the integer level. If you want the level to start from 1, you can increment the result by one; nonlinear mapping and grading realize the reproducible conversion from continuous risk quantity to operable early warning level, which makes it easy to incorporate numerical results into procedural early warning thresholds and operation and maintenance decisions.

[0104] A dynamic early warning information set for gas reservoirs is formed by categorizing all discrete risk levels of individual wells according to their inter-well geographical and geological layout. This set provides the distribution of gas reservoir precursor risks across the entire well range. Specifically, it includes the discrete risk levels of all wells. Organized by well location or well number, forming a dynamic early warning information set for gas reservoirs. This set can be either formed into a vector by well number or mapped to spatial coordinates to form a two-dimensional risk distribution layer, used to monitor the spatiotemporal distribution of gas reservoir precursor risks, and further... It can be linked with geological attributes or production control parameters for risk visualization or manual assessment, transforming continuous risk quantities into a set of actionable early warning levels, facilitating real-time monitoring, alarm rule triggering, and the integration of operation and maintenance response processes.

[0105] Example 2

[0106] Please see Figure 2 This exemplary gas reservoir dynamic early warning system based on sulfur-water intrusion coupling precursor identification includes:

[0107] Well feature construction module: Collects data on formation pressure, gas production, water production, hydrogen sulfide concentration, wellbore temperature and water injection volume of gas reservoir wells, and maps various types of data to a unified feature space to form well feature information;

[0108] Coupling sensitivity generation module: Combines well feature information with neighboring well feature information to correct coupling sensitivity and generate coupling sensitivity data;

[0109] Local disturbance analysis module: Based on the differences in sulfur water and pressure and temperature between adjacent wells, a local coupled disturbance index is generated to form local disturbance data;

[0110] Single-well anomaly evolution module: Combines coupled sensitivity data and local disturbance data to generate single-well anomaly index, and performs cumulative evolution processing on historical anomaly index to form cumulative evolution data;

[0111] Global Risk Enhancement Module: Combines single-well cumulative evolution data with local disturbance data to generate a global coupled risk enhancement index, forming gas reservoir precursor characteristic data;

[0112] Single-well risk assessment and early warning module: Combines global coupled risk enhancement indicators and single-well anomaly index to form a single-well risk index, and maps the risk index to discrete risk levels to form dynamic early warning information for gas reservoirs.

[0113] It should be noted that the gas reservoir dynamic early warning system based on sulfur-water intrusion coupling precursor identification provided in the above embodiments and the gas reservoir dynamic early warning method based on sulfur-water intrusion coupling precursor identification provided in the above embodiments belong to the same concept. The specific operation methods of each module and unit have been described in detail in the method embodiments and will not be repeated here. In practical applications, the gas reservoir dynamic early warning system based on sulfur-water intrusion coupling precursor identification provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the system can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0114] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A dynamic early warning method for gas reservoirs based on the identification of precursors to sulfur-water intrusion coupling, characterized in that, include: Data on formation pressure, gas production, water production, hydrogen sulfide concentration, wellbore temperature, and water injection volume of gas reservoir wells are collected and mapped to a unified feature space to form well feature information. The coupling sensitivity is corrected by combining the well feature information with the feature information of adjacent wells, and coupling sensitivity data is generated. Based on the differences in sulfur water and pressure and temperature between adjacent wells, a local coupling disturbance index is generated to form local disturbance data; A single-well anomaly index is generated by combining coupled sensitivity data and local disturbance data, and historical anomaly indices are cumulatively evolved to form cumulative evolution data. By combining single-well cumulative evolution data with local disturbance data, a global coupled risk enhancement index is generated, forming gas reservoir precursor characteristic data; By combining the global coupled risk enhancement index and the single-well anomaly index, a single-well risk index is formed. The risk index is then mapped to discrete risk levels to generate dynamic early warning information for gas reservoirs.

2. The gas reservoir dynamic early warning method based on sulfur-water intrusion coupling precursor identification according to claim 1, characterized in that, The well feature information obtained by mapping gas reservoir data includes: Data on reservoir pressure, gas production, water production, hydrogen sulfide concentration, wellbore temperature, and water injection volume are collected from the gas reservoir well. Among them, the reservoir pressure is collected by a pressure sensor, the gas production is collected by a flow meter, the water production is collected by a water meter, the hydrogen sulfide concentration is collected by a gas analyzer, the wellbore temperature is collected by a temperature sensor, and the water injection volume is collected by the water injection control system. Based on the collected monitoring data, a corresponding feature set is established, and nonlinear mapping processing is performed on different physical quantities to form unified well feature information; Well feature information is aggregated in both time and space dimensions to form a well feature information set.

3. The gas reservoir dynamic early warning method based on sulfur-water intrusion coupling precursor identification according to claim 2, characterized in that, Generating coupling sensitivity data includes: Based on the set of well feature information, the well feature information is coupled and associated with the feature information of neighboring wells. A set of neighboring wells is established based on the spatial location relationship between wells, and spatial weights are formed according to the spatial distance attenuation relationship. Adjacent well heterogeneity indices are generated based on differences in hydrogen sulfide concentration, water production, pressure, and temperature. A coupling sensitivity correction factor is generated by combining the relationship between hydrogen sulfide and water production in the well and pressure and temperature conditions. A preliminary coupling metric is formed based on unified well characteristic information, adjacent well heterogeneity index, spatial weight, and coupling sensitivity correction factor. The initial coupling metric is modulated by the relationship between reservoir pressure and gas production to generate the final coupling sensitivity.

4. The gas reservoir dynamic early warning method based on sulfur-water intrusion coupling precursor identification according to claim 1, characterized in that, The local coupling perturbation index is generated, forming local perturbation data including: Based on the final coupling sensitivity, the differences in hydrogen sulfide concentration, water production, pressure and temperature between wells are coupled to form the neighboring well difference quantity. Based on the differences between adjacent wells, a nonlinear interwoven structure is constructed, and the sulfur-water coupling characteristics and the pressure and temperature conditions of adjacent wells are adjusted to form an intermediate perturbation expression. The intermediate disturbance expression is modified based on the coupling sensitivity of adjacent wells to generate a local coupling disturbance index; The local coupling disturbance indices of all well pairs are aggregated to form a local disturbance data matrix.

5. The gas reservoir dynamic early warning method based on sulfur-water intrusion coupling precursor identification according to claim 4, characterized in that, The generation of single-well anomaly indices and the formation of cumulative evolution data include: Based on coupling sensitivity data and local disturbance data, a single-well anomaly index is generated based on the difference relationship between the well and its neighboring wells. Based on the single-well anomaly index, the historical anomaly index is cumulatively evolved to form cumulative evolution data that is updated over time. Based on the cumulative evolution data, the cumulative evolution data of all wells are arranged by well number to generate a single-well anomaly evolution matrix.

6. The gas reservoir dynamic early warning method based on sulfur-water intrusion coupling precursor identification according to claim 1, characterized in that, Combining single-well cumulative evolution data with local disturbance data includes: The cumulative evolution data and local disturbance data between wells and adjacent wells are coupled to form the cumulative disturbance coupling amount between wells; After the cumulative disturbance coupling between wells is formed, the coupling of adjacent wells of each well is summarized to generate the global interaction strength of the wells; After the overall well interaction intensity is formed, it is amplified by combining time evolution factors to form a time evolution factor covering the entire well.

7. The gas reservoir dynamic early warning method based on sulfur-water intrusion coupling precursor identification according to claim 6, characterized in that, Generate a global coupling risk enhancement index and form gas reservoir precursor characteristic data including: By combining the global interaction intensity of the well with the time evolution factor, a preliminary global coupling risk index is generated; Based on the preliminary global coupling risk index, the preliminary global coupling risk index is fused with local disturbance data to generate an enhanced global coupling risk index. Based on the enhanced global coupling risk index, combined with cumulative evolution data and local disturbance data, a set of gas reservoir precursor characteristic data is formed.

8. The gas reservoir dynamic early warning method based on sulfur-water intrusion coupling precursor identification according to claim 1, characterized in that, The formation of a single-well risk index includes: By combining single-well cumulative evolution data and global coupling risk enhancement indicators, a single-well coupling risk quantity is formed, which reflects the risk integration status of a single well under the background of global coupling. After the risk quantity of a single well is formed, it is fused with the local disturbance data of adjacent wells, and a single-well risk index is generated through a higher-order coupling relationship.

9. The gas reservoir dynamic early warning method based on sulfur-water intrusion coupling precursor identification according to claim 8, characterized in that, The formation of dynamic early warning information for gas reservoirs includes: The single-well risk index is converted into discrete risk levels according to a nonlinear mapping relationship, and each discrete level represents a different level of risk status of a single well. All individual well discrete risk levels are combined into a dynamic early warning information set for gas reservoirs based on the geographical and geological layout between wells. This set provides the distribution of gas reservoir precursor risks across the entire well range.

10. A gas reservoir dynamic early warning system based on sulfur-water intrusion coupling precursor identification, used to implement the gas reservoir dynamic early warning method based on sulfur-water intrusion coupling precursor identification as described in any one of claims 1-9, characterized in that, include: Well feature construction module: Collects data on formation pressure, gas production, water production, hydrogen sulfide concentration, wellbore temperature and water injection volume of gas reservoir wells, and maps various types of data to a unified feature space to form well feature information; Coupling sensitivity generation module: Combines well feature information with neighboring well feature information to correct coupling sensitivity and generate coupling sensitivity data; Local disturbance analysis module: Based on the differences in sulfur water and pressure and temperature between adjacent wells, a local coupled disturbance index is generated to form local disturbance data; Single-well anomaly evolution module: Combines coupled sensitivity data and local disturbance data to generate single-well anomaly index, and performs cumulative evolution processing on historical anomaly index to form cumulative evolution data; Global Risk Enhancement Module: Combines single-well cumulative evolution data with local disturbance data to generate a global coupled risk enhancement index, forming gas reservoir precursor characteristic data; Single-well risk assessment and early warning module: Combines global coupled risk enhancement indicators and single-well anomaly index to form a single-well risk index, and maps the risk index to discrete risk levels to form dynamic early warning information for gas reservoirs.

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