SIH26121: eRTMAC-NWIS (Nearby Wells Intelligence System): An AI-Powered Offset Well Knowle — SIH 2026 | Wynit

SIH26121: eRTMAC-NWIS (Nearby Wells Intelligence System): An AI-Powered Offset Well Knowle — SIH 2026 | Wynit

What they'll ask you

14

Generated from the brief's own wording and the competition's published rules, never from a guess about what this ministry prefers.

Sample PDF reports or datasets to train the OCR and NLP models?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Specific thresholds or definitions for what constitutes high risk?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Integration protocols for connecting to the existing eRTMAC system?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.

11 morethe ones a panel gets to if there is time

Has any part of this been shown at a previous event, hackathon or college project?
The guidelines are explicit: your solution must not have appeared in any previous event or programme, of any sort. A recycled project is what a team under time pressure reaches for.
The brief asks for map visualization interface. How would you build that?
Decoded from SIH26121 itself: Displays nearby wells on a geospatial map within a user-defined radius. The brief asks for it by name.
The brief asks for document extraction engine. How would you build that?
Decoded from SIH26121 itself: Uses OCR, NLP, and data analytics to extract and structure information from historical reports and PDFs. The brief asks for it by name.
The brief asks for searchable knowledge repository. How would you build that?
Decoded from SIH26121 itself: Provides a searchable database of past drilling events, lessons learned, and operational challenges. The brief asks for it by name.
The brief asks for risk prediction models. How would you build that?
Decoded from SIH26121 itself: Identifies potential drilling risks like mud losses or stuck pipe based on historical offset well behavior. The brief asks for it by name.
The brief asks for real-time alert system. How would you build that?
Decoded from SIH26121 itself: Generates real-time alerts and recommendations when current operations approach depths with known risks. The brief asks for it by name.
The brief asks for user dashboard. How would you build that?
Decoded from SIH26121 itself: Presents information through a user-friendly interface for field and office personnel. The brief asks for it by name.
Where does your data come from: a published source, one you collect, or one you generate?
No dataset is attached to this problem statement, so sourcing it is part of the work and nobody told you that.
Why not use what already exists? Name the closest thing to this that is already running.
A team that has not named the alternative themselves is answering this for the first time in the room.
Which single thing will you demonstrate end to end, start to finish, with nothing skipped?
Ours, not a rule: a narrow thing that fully works survives questioning better than a broad thing that half works. If nobody on the team can name it, that is the finding.
Show me this working: the evaluators will check whether your platform can successfully ingest unstructured historical reports using OCR and NLP, display spatial well data on a map, and accurately generate predictive risk alerts based on offset well data.
This is the evaluator read for your problem statement, decoded from the brief's own wording.

The government's original description (431 words)

• 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.