eRTMAC-NWIS (Nearby Wells Intelligence System): An AI-Powered Offset Well Knowledge and Decision Support Platform for Drilling Operations

• Background Oil India Limited has a digital real-time monitoring system (eRTMAC) that provides real-time drilling data, mud logging information, and wellsite analytics across operational areas. However, drilling decisions, particularly in geologically complex formations, require not only real-time data from the active well but also insights from nearby and historical wells drilled in the same reservoir or formation. Historical drilling knowledge currently resides across numerous well completion reports, drilling reports, PDF documents, and individual experience, making retrieval time-consuming and dependent on individual experience & memory. This often results in delays in decision-making and missed opportunities to proactively mitigate drilling risks. • Problem Description Currently, drilling teams do not have a unified platform that can: i. Display nearby wells on a geospatial map relative to the active well. ii. Provide instant access to historical drilling experiences and operational events from offset wells. iii. Correlate drilling parameters, reservoir characteristics, mud losses, kicks, stuck pipe incidents, casing programs, cementing practices, and formation-specific risks across wells. iv. Generate proactive alerts when current drilling operations approach depths or formations where similar challenges were encountered in nearby wells. As a result, engineers often spend significant time manually searching through historical reports and databases, limiting the ability to make fast, informed, and data-driven operational decisions. • Expected Outcome / Solution Develop an AI/ML-enabled Nearby Wells Intelligence System (NWIS) that acts as a standalone decision-support platform alongside eRTMAC that has institutional memory. The solution should: i. Use AI, NLP, OCR, and data analytics to automatically extract and structure information from historical drilling reports and well documents. ii. Provide an interactive map-based visualization of nearby wells within a user-defined radius. iii. Create a searchable knowledge repository of drilling events, lessons learned, operational challenges, and mitigation measures. iv. Correlate geological, drilling, and reservoir data across wells based on depth and formation. v. Develop predictive analytics models that can identify potential drilling risks such as mud losses, stuck pipe, overpressure zones, torque spikes, or cementing issues based on historical offset-well behaviour. vi. Generate real-time alerts and recommendations to assist drilling engineers in proactive decision-making. vii. Present information through a user-friendly dashboard for field and office-based personnel. • Relevant Data Availability (if any) Potential data sources available within OIL may include: i. Well Completion Reports (WCRs) ii. Daily Drilling Reports (DDRs) iii. Drilling and mud logging databases iv. Historical well parameters and drilling records v. Reservoir and geological data vi. eRTMAC data streams vii. Well trajectory and survey data viii. Casing, cementing, and mud program records ix. Historical operational event records including mud losses, kicks, stuck pipe incidents, fishing operations, and NPT events.