Automated Well Management for Underground Gas Storage (UGS) Systems

Abstract This study introduces a digital well management system capable of automated model calibration and flow assurance risk detection in production and injection gas wells for an underground gas storage (UGS) facility. Steady-state well models leverage the latest measured data and reservoir simulation output for executing automated workflows that ensure model accuracy and continuous surveillance of flow instabilities in the system. The resulting set of outputs is integrated with interactive dashboards for user action, facilitating safe and cost efficient UGS operations. Data from digital field instrumentation is integrated into an SQL-based production data management system (PDMS), initiating automated workflows for well management. A valid well model that accurately mimics field conditions serves as the backbone for operational planning. The tailored workflows provide useful input to the UGS management, including: Autocalibration: By adjusting pre-defined parameters like choke discharge coefficient, friction factor, U-multiplier and productivity index to meet tolerance thresholds. This ensures realistic well performance analysis and bottleneck identification. Well Surveillance: Risk detection through pressure-temperature profile simulations, e.g., overpressure, sanding, hydrate, erosion, and liquid loading risk. Sensitivity Study: On-demand what-if scenario analysis of the impact of parameters like pressure, flowrate, and water-gas ratio (WGR) on well performance and stability. Bottomhole Pressure (BHP) Calculation: On-demand estimation of BHP based on user-defined measured data. Model Parameter Update: On-demand update of key calibration parameters or flow coefficients into the well model. This solution revolutionizes traditional analysis and field management methods with a model-based approach, delivering instant insights through interactive dashboards. Automation drastically reduces response analysis time, significantly accelerating decision-making processes. Predictive techniques for risk detection help operators anticipate and mitigate issues, reducing operational risks, and downtime. Consequently, stakeholders can respond quickly to market demands, thereby enhancing overall organizational effectiveness. This systematic approach thus enhances success, safety, efficiency, and productivity in the realm of UGS operations. This solution offers tailored workflows for UGS systems, facilitating multidisciplinary collaboration within a unified environment. The solution is enabled through the integration of domain technologies and the utilization of Web APIs to enable workflow results visualization and user interactions. The well models accurately mirror alternating production and injection operations in a digital environment, reflecting real-world dynamics. This approach marks a significant shift from the traditional reliance on user experience, to empowering users with streamlined processes and informed decision-making, thus enabling a smarter, more intelligent system, ensuring that UGS operations function more safely, efficiently, and optimally.