Integrated Physical Modeling with Live Data Base to Develop Exception Base Surveillance Engine for Deep Tight Gas Fields

Abstract This paper details the transformative journey of the Oman Shell Well Reservoir and Facility Management (WRFM) team in designing and deploying an advanced, automated, and engineering-based monitoring system. The initiative was driven by the need to enhance operational efficiency, ensure safety, and optimize resource utilization across oil and gas facilities. The team achieved this by developing an integrated digital solution that seamlessly connects real-time field data with an Integrated Visual Management (IVM) platform. By embedding engineering logic and physics-based models into the IVM system, the solution enables Exception-Based Surveillance (EBS) alarms to be triggered automatically. This innovation has revolutionized the monitoring process, reducing reliance on manual interventions and enabling faster, more accurate decision-making. The primary objective of the project was to streamline the tracking of safety and business-critical performance indicators by automating the surveillance of wells and surface facilities. This automation not only minimized the need for manual data monitoring but also established clear roles and responsibilities for various teams, including WRFM and Operations. By leveraging engineering calculations rooted in physics models and data-driven logic, the team developed a robust system capable of detecting critical issues such as well exceeding erosional velocity, tubing leaks, and other operational anomalies. To enhance usability, the system incorporates diagnostic plots for intuitive visualization and a consolidated event view that includes features such as user acknowledgment and automated email notifications. These functionalities ensure that issues are promptly identified, communicated, and addressed. The EBS engine, a core component of the system, delivers direct value by generating live, accurate, and instantaneous data-driven alarms. These alarms, when acted upon in a timely manner by the field and subsurface teams, have yielded significant operational and financial benefits. Key outcomes include: 100% Well Integrity Compliance: The system ensures that all wells adhere to stringent integrity standards, mitigating potential hazards and safeguarding operational safety. This proactive approach has significantly reduced the risk of well failures and environmental incidents.Reduction in Deferment reaction time: Since its implementation in May 2024, the deferment reaction time have successfully reduced 15%. This reduction has contributed to uninterrupted production, maximizing revenue generation and minimizing operational disruptions.Reduction in Daily Well Review Process time: The automation of the daily well review process has led to a 20% improvement in Full-Time Equivalent (FTE) efficiency. This optimization has allowed for better resource allocation, reduced operational costs, and enhanced productivity. The integration of the IVM platform as a physical modeling tool for system performance, combined with real-time data from surface facilities and wells, represents a groundbreaking achievement in the oil and gas industry. This first-of-its-kind implementation involved hardwiring an operating envelope database with the IVM prediction engine, creating a robust, real-time, and fully automated data surveillance system that is linked to a physical gas field model. The system's ability to predict and monitor operational performance in real-time has set a new benchmark for efficiency and reliability in the sector. Beyond the immediate operational benefits, this innovation has broader implications for the industry. It demonstrates the potential of digital transformation to enhance safety, reduce costs, and improve decision-making processes. The success of this project underscores the importance of collaboration between engineering, data science, and operations teams in driving technological advancements. Furthermore, it highlights the value of leveraging real-time data and predictive analytics to address complex challenges in oil and gas operations. Through development of this automated monitoring system, a significant milestone is achieved in the evolution of well and facility management. By combining advanced engineering models, real-time data integration, and user-friendly visualization tools, the team has created a solution that not only addresses current operational challenges but also paves the way for future innovations in the industry. This project serves as a testament to the power of digital transformation in achieving operational excellence and driving sustainable growth.