Unconventional wells present unique monitoring challenges, especially when manual processes limit visibility and responsiveness. Faced with time-consuming surveillance, scattered data systems, and delayed anomaly detection, a leading energy company partnered with LTM to modernize its approach. The client needed an automated solution for anomaly detection in unconventional wells.
We equipped the client with an automated anomaly detection solution for unconventional wells by developing a heuristic Z-score model, powered by Azure AI/ML and Seeq. The solution was deployed to monitor over 1,000 gas-lift wells in the Permian Basin, enabling proactive well management and operational efficiency:
A robust model was implemented to detect performance anomalies across wells, enabling early identification of wells requiring immediate operational attention.
The system architecture was designed to support seamless scaling for growing number of new wells drilled in the asset.
Rule-based and pattern-recognition mechanisms were defined and implemented to classify anomalies into:
detection using real time data providing real-time visibility into well behaviour and enabling faster, more informed decision-making.
LTM’s AI/ML-powered anomaly detection solution—built on Azure and integrated with Seeq—helped the client take control of well performance monitoring across 1,000+ gas-lift wells in the Permian Basin. Automation of anomaly detection and classification eliminated time-consuming manual surveillance and reduced operational overhead. Moreover, its scalable design and real-time dashboards gave teams powerful visibility to make informed decisions and maintain consistent well output. Beyond immediate efficiency gains, this solution is a strong example of how AI is transforming the future of oil and gas, making operations more responsive, intelligent, and scalable.
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