Digital 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
Bold. Let us find out precisely how bold, in the order a panel will find out.
Proceed with caution. Precisely described and genuinely valuable, but with no field data your twin validates against itself — take it only with petroleum engineering support, and be explicit about which physics is modelled and which is assumed. Roughly 85–190 teams are expected to go here.
The receipts
Every red flag on this statement, in full. These are the four places it bites.
Exhibit A
No Baghewala field data is provided or public, so the twin is built and validated entirely against a simulator you wrote, which proves your assumptions rather than your optimisation
It gets worse
Coupled thermal reservoir simulation is specialist work and a simplified analytical model will be visibly inadequate to a reservoir engineer
Still reading?
Rod pump dynamics involve rod string elasticity and fluid inertia, and a static force model will mispredict exactly the rod-float condition the description cares most about
And the finisher
Oil India evaluators are production engineers who work on this field, so domain errors that a general judge would miss will be immediately apparent
The damage report
Every score this statement earned, and what each one actually costs you.
Feasibility
2/5You have picked a fight with physics, procurement, or both. One of them always wins.
This requires coupled thermal reservoir simulation, wellbore fluid modelling and rod pump dynamics validated against real field data from one specific Rajasthan field — none of which is public, and the petroleum engineering underpinning each layer is specialist knowledge rather than something learnable alongside the build.
Innovation scope
3/5Mildly interesting. The novelty will not carry the room; the build has to.
The physics of each layer is established petroleum engineering and the description names the parameters to optimise, so your room is in how you couple the three models and formulate the joint optimisation rather than in the modelling itself.
Clarity
4/5The ask is unambiguous, which quietly removes your favourite excuse.
The description names the field, the crude properties, the specific operational failures — rod floating, impact loading, high steam-oil ratio — and lists seven concrete things the solution should do, so the target is well defined even though the data is not provided.
Acceptance potential
2/5The numbers do not like you. Bring something the numbers cannot see.
The problem is genuine and precisely described, but with no field data the twin is calibrated against nothing and validated against itself, and Oil India judges are petroleum engineers who will probe the reservoir and rod-dynamics assumptions far more deeply than a general judge would.
Effort
MassiveA semester of work wearing a hackathon costume. Something is getting cut; decide what now, not in week five.
Three coupled physical models plus a joint optimiser plus a monitoring dashboard is a large systems build, and each physical layer requires domain study before any code can be written.
Demo-ability
MediumDemoable, if you rehearse it. Nobody rehearses it.
The optimiser adjusting pump settings as modelled viscosity rises tells a clear story, but every number in it comes from a simulation you built, so the demo largely demonstrates your own assumptions.
Data
None suppliedNo dataset comes with this one, so every accuracy figure you quote is a number about labels you invented.
Nothing is provided with the statement. You are sourcing, cleaning and labelling it yourself, and that work is invisible in the demo but very visible in the questions.
The demo they will have already seen
Somewhere around 85–190 teams are heading here, and the description is doing the choosing for most of them. They will read the same brief, reach the same architecture, and build a version of the same demo you are planning. Being correct is the floor. If your five minutes could be swapped with the team before you and nobody in the room would notice, you have not picked badly — you have built predictably, which costs exactly the same and hurts more.
What survives
The ground worth standing on when the questions start.
- The description hands you the reservoir characteristics — API gravity, temperature range, viscosity behaviour — so the physical parameters of your model come from the statement rather than invention
- Rod floating and impact loading are specific, well-documented failure modes with established detection signatures in dynamometer data
- Steam-oil ratio is a standard industry metric, so your optimisation objective is unambiguous and quantifiable
None of that means do not pick it. It means do not walk into that room having heard any of this for the first time from a judge.
The framing is a joke. The findings are not — they are the same analysis on the statement page, and every line above is attached to a score or a fact in the record. It is one opinion with its reasoning attached, so argue with it before you trust it.