An AI-enabled digital twin software that integrates reservoir and surface data to help engineers optimize steam injection and pump operations for heavy oil wells.
Oil India Limited · Software
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Decomposed from what the description asks for. Nothing added.
Data Ingestion Pipeline
Collects historical production, steam injection, and pump operating data.
Reservoir Thermal Predictor
Predicts reservoir heating, cooling, and production performance over time.
CSS Cycle Optimizer
Recommends optimal steam volume, injection pressure, soak time, and production cut-off.
SRP Operations Controller
Adjusts stroke length and SPM based on current well conditions.
Rod Failure Detector
Detects rod floating and impact loading to prevent equipment damage.
Both columns are read off the brief's own wording. Nothing here is inferred from the ministry's name.
The jury will check whether your digital twin successfully integrates reservoir behavior with surface pump operations, and if it provides actionable predictions for CSS cycles and rod failure detection.
A jury can still ask about these. Decide them deliberately rather than by accident.
Generated from the brief's own wording and the competition's published rules, never from a guess about what this ministry prefers.
Exact format and schema of the historical field data?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Real-time data streaming protocols to be used?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Specific numerical targets for reduction in Steam-Oil Ratio?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
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 data ingestion pipeline. How would you build that?
Decoded from SIH26120 itself: Collects historical production, steam injection, and pump operating data. The brief asks for it by name.
The brief asks for reservoir thermal predictor. How would you build that?
Decoded from SIH26120 itself: Predicts reservoir heating, cooling, and production performance over time. The brief asks for it by name.
The brief asks for css cycle optimizer. How would you build that?
Decoded from SIH26120 itself: Recommends optimal steam volume, injection pressure, soak time, and production cut-off. The brief asks for it by name.
The brief asks for srp operations controller. How would you build that?
Decoded from SIH26120 itself: Adjusts stroke length and SPM based on current well conditions. The brief asks for it by name.
The brief asks for rod failure detector. How would you build that?
Decoded from SIH26120 itself: Detects rod floating and impact loading to prevent equipment damage. 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 jury will check whether your digital twin successfully integrates reservoir behavior with surface pump operations, and if it provides actionable predictions for CSS cycles and rod failure detection.
This is the evaluator read for your problem statement, decoded from the brief's own wording.
Each one is quoted from a gap in the brief, not a guess about your team.
No dataset provided
The description depends on real data, and the organisers have not attached a dataset link.
Needs data you may not get
The problem requires deep reservoir data, well completion details, and fluid properties from the Baghewala Field which are not publicly available.
No measurable target
The text asks to improve pump efficiency and reduce energy consumption without specifying exact percentage targets.
What the organisers attached, and what the brief assumes you can get.
Same organisation, same year. Reading two of theirs tells you more about what they care about than reading one.