Oil India Limited
Browse and strategize across all 4 official problem statements submitted by Oil India Limited for Smart India Hackathon 2026.
Problem Statements (4)
Match My TeamDigital Twin for Well-to-Surface Optimization of Cyclic Steam Stimulation (CSS) and Sucker Rod Pump (SRP) Operations for Heavy Oil Wells of Baghewala Field.
Oil India Limited's (OIL) Baghewala Field in Rajasthan produces heavy crude oil (17°-19° API) from Jodhpur Sandstone reservoirs where high oil viscosity and asphaltenes require Cyclic Steam Stimulation (CSS / 'Huff and Puff'), but unoptimized steam injection volumes and soaking times cause high Steam-to-Oil Ratios (SOR), sand ingress, and premature reservoir cooling. Build an integrated Reservoir-to-Surface Digital Twin for Cyclic Steam Stimulation (CSS) Optimization combining thermal reservoir physics, wellbore heat loss modeling, and AI predictive control to maximize heavy oil recovery while minimizing boiler fuel gas consumption.
eRTMAC-NWIS (Nearby Wells Intelligence System): An AI-Powered Offset Well Knowledge and Decision Support Platform for Drilling Operations
Drilling engineers at Oil India Limited (OIL) encounter unexpected drilling hazards (formation kick, lost circulation, stuck pipe, shale swelling) because offset well data (drilling logs, mud weights, lithology, casing seats) is locked in legacy well archives and scattered daily drilling reports (DDRs). Build 'eRTMAC-NWIS' (Nearby Wells Intelligence System)—an AI-powered offset well correlation and real-time drilling risk forecasting platform for OIL that spatializes historical borehole logs and predicts formation pore pressures to prevent blowouts.
Intelligent Data Capture & Schedule-Linking Layer for Infrastructure Project Management: Real-Time Actual Progress Tracking (Planning-to-Execution Bridge)
Oil India Limited's massive pipeline, well-pad, and processing plant infrastructure projects suffer from delayed milestone tracking because site engineers log physical site updates in informal emails and chat groups with no automated link to Primavera P6 / MS Project master schedules. Build an Intelligent Data Capture and Schedule-Linking Layer for OIL that automatically extracts physical construction milestones from site photos, drone orthomosaics, and daily progress reports (DPRs) to dynamically update enterprise Primavera P6 Gantt charts.
AI/NLP Engine to Detect Serious Injury & Fatality (SIF) Precursors in OIL's Unsafe-Act/Unsafe-Condition and Near-Miss Reports
Upstream oil & gas drilling and refinery operations at Oil India Limited (OIL) record thousands of daily near-miss reports, hazard observations, and maintenance logs in unstructured text, where critical Serious Injury & Fatality (SIF) precursors (high-pressure gas releases, suspended crane loads, confined space entry lapses) remain buried until a fatal accident occurs. Build an AI/NLP Engine to Detect Serious Injury & Fatality (SIF) Precursors in Safety Logs for OIL that uses domain-specific NLP to automatically identify high-energy hazard precursors and prioritize critical preventive interventions.