ARTAM-WH: early predictive well health (WH) meta-monitoring tool
A well performing as expected is healthy. It is essential for efficient field operations to detect well health (WH) issues that may reduce the production efficiency and/or the overall recovery of an asset. The authors describe an early predictive WH meta-monitoring tool called ARTAM-WH, developed to assist maintaining stable operations (with high recovery). This is achieved by routinely checking the relevant individual WH parameters in context with the well’s operating environment. As alert complexity increases so does the risk of false alerts. Moreover, existing systems raise alarms/alerts after significant deviation without completing extensive cross-checking. An early predictive and WH meta-monitoring tool is highly desirable. This study fills the gap. ARTAM-WH is a new approach designed to provide early identification of existing or developing WH issues—precursors to WH problems if not addressed. ARTAM-WH uses in-situ monitoring systems—when available—or basic pressure, temperature and flow (gas, oil, water) data, coupled with static data, and conducts preliminary analysis and proper cross-checking and then notifies the engineers/operators. Differentiating this approach is that it is not looking at small data sets but at a large palate, including both static (well construct) and dynamic current performance versus performance expectations. ARTAM-WH provides supporting evidence of WH issues to the appropriate stakeholder. Notification includes the necessary and sufficient evidence derived from approximate reasoning algorithms combining multiple variables to identify possible issues and test the WH hypotheses. ARTAM-WH frees up engineers/operators to focus on higher priority activities such as developing solutions. This allows for handling those problems through normal planning rather than emergency fixes.