Overview
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 LTIMindtree to modernize its approach. The client needed an automated solution for anomaly detection in unconventional wells.
Market Trends
The global liquefied natural gas (LNG) market is undergoing a rapid transformation, driven by rising demand for cleaner energy sources, increased investments in infrastructure, and a strong push for operational efficiency. As countries seek to reduce carbon emissions and transition away from coal and oil, LNG has emerged as a strategic fuel for both domestic consumption and international export. In Africa, large-scale LNG projects are reshaping regional energy landscapes, attracting billions in investment and fostering economic growth. Companies are prioritizing digitalization, data-driven decision-making, and compliance with international standards to remain competitive and future-ready in a dynamic market environment.
Need for Change
Oil and gas operations are becoming increasingly complex, with expanding asset networks, legacy systems, and disparate data sources creating significant challenges in maintaining operational efficiency. Surface operations, in particular, generate massive volumes of equipment, maintenance, and production data, often trapped in silos and inconsistent formats. This fragmentation limited collaboration, slowed analytics, and raised compliance risks across critical workflows.
To overcome these constraints, the client aimed to establish a unified Surface Data Platform that could centralize operational datasets, enable predictive maintenance, and support Digital Twin models. The initiative aimed to standardize data, improve reliability, and empower field teams with real-time, actionable insights. This laid a strong foundation for oil and gas data modernization.
LTIMindtree’s Solution
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:
Well Performance Outlier Detection Model
A robust model was implemented to detect performance anomalies across wells, enabling early identification of wells requiring immediate operational attention.
Scalable Data Analytics Infrastructure
The system architecture was designed to support seamless scaling for growing number of new wells drilled in the asset.
Anomaly Classification Engine
Rule-based and pattern-recognition mechanisms were defined and implemented to classify anomalies into:
- Short-Term Events: Sudden shut-ins and U-tubing gas occurrences.
- Long-Term Events: Bottlenecking, well plugging, and gradual performance degradation.
Integration with Bad Actor
detection using real time data providing real-time visibility into well behaviour and enabling faster, more informed decision-making.
Tech Stack and Architecture
Benefits
Conclusion
LTIMindtree’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.
Discover how AI-based solutions can transform oil and gas production operations and optimize workflows. Reach out to our experts today.
Contact eugene.comms@ltimindtree.com to know more.
