Enhancing Reservoir Management Through Automated Pressure Transient Analysis and Data-Driven Insights

Abstract This study focuses on the automation of pressure transient transient analysis (PTA) as part of a broader digital solution to optimize reservoir management and field operations. The objective is to automate the data interpretation workflow, enabling real-time identification of well performance issues, such as pressure build-ups and shut-ins. This approach integrates data-driven tools to streamline analysis, enhance decision-making, and support proactive well intervention strategies across multi-well assets, ultimately improving operational efficiency and reducing costs. The project integrates advanced data automation techniques, leveraging large datasets from Permanent Downhole Gauges (PDHGs). By automating PTA interpretation, the system reduces manual analysis and accelerates decision-making. During the pilot, automation facilitated automatic shut-in detection, marking a significant step forward in reservoir monitoring. The workflow employs real-time data processing and collaboration among multidisciplinary teams of engineers, data scientists, and field specialists. The system identifies performance trends and anomalies, flags potential issues, and optimizes resource allocation, all through a user-friendly interface integrated with asset management platforms. The pilot phase delivered significant operational improvements, including a 70% reduction in engineering time and over 2 million USD in annual cost savings. The automated PTA interpretation provided real-time insights, enabling timely shut-in identification and enhanced well intervention planning. Early-stage automation demonstrated the potential for future enhancements, such as more sophisticated predictive models based on asset production data. This approach streamlined workflow, minimized delays, and enabled proactive decision-making, contributing to better reservoir performance management and operational efficiency. This project showcases the power of automated analysis as part of a digital solution that integrates data-driven decision-making into asset management. The pilot demonstrated the potential of automatic shut-in detection and analysis and identified pathways for future improvements, including more refined predictive models using production data, ultimately optimizing reservoir management strategies across diverse fields and assets.