In large, complex fields with hundreds of producing and injection wells, relying solely on periodic well tests often leads to missed warning signs and delayed responses. Subtle changes in well behavior—like increasing water cut, pressure fluctuations, or declining performance—can easily slip through the cracks, resulting in unplanned downtime or production loss.

To address this, the AI-Driven Well Surveillance solution brings together AI and reservoir physics to continuously analyze a wide range of operational data, from well test results and downhole pressures to petrophysical summaries and past interventions. By learning from historical well behavior, the solution can detect early indicators of problems such as liquid loading, sand ingress, or artificial lift inefficiencies—often before they’re visible through routine checks.

It builds a dynamic health profile for each well, prioritizing attention where the risk is highest and ensuring engineers have the insights they need to act quickly and effectively. This shift from reactive monitoring to predictive insight helps teams make smarter decisions, reduce operational disruptions, and keep wells running at their best.