Oil & gas asset evaluation in minutes, not weeks
AVP AI is a multi-agent platform that runs full due diligence on an asset: from geology and reserves estimation to reservoir simulation, surface facilities and a financial model with NPV / IRR. Six engineering agents work in a cascade under a unified methodology.
What asset evaluation covers
Six sequential stages with automatic data hand-off between agents. Every step is cross-checked against 2–3 independent sources.
Identification
Data collection on the license area: coordinates, licensee, status, drilling history. Source: state reserves register, open government registries.
Geology and reserves
Recoverable reserves C1+C2, lithology, oil-saturated thickness, recovery factor (RF). Cross-check with analogs. Method: state register + ML + volumetric.
Production profile
Reservoir simulation forecast: production peak, decline rate, water cut, gas-oil ratio. Calibrated against neighbors within 50–100 km.
Surface facilities
Well pads, pipelines, roads, power supply. Specifications and footage. CAPEX based on industry benchmarks.
Economics
CAPEX, OPEX, MET, EPT, reverse excise, profit tax. Full financial model and cash flow. Price and tax regime scenarios.
Business case
Buy / pass / develop decision, value drivers, investment memo, portfolio ranking. Decision-ready output.
The platform solves four common tasks
M&A teams, new-projects teams and portfolio teams use the same tool at different horizons.
M&A teams
Fast asset screening for tenders and auctions. A go / no-go call within a shift, not a month.
Corporate strategy
Portfolio review: where the hidden value sits, which assets are subsidized, where CAPEX and tax levers actually work.
Investment funds and traders
Independent valuation for deals and financing. A clean audit trail for every number.
Technical teams
A replacement for hand-built Excel models: the same methodology applied to hundreds of assets per week.
Cascade validation across four levels
No number is invented by the model. Every value runs through a cascade of checks: from the official source all the way down to the fallback classical formula.
- State reserves registerReserves approved by the State Reserves Commission (GKZ) — the highest-priority source.
- Neighbor analogsCross-check against 3–5 fields within a 50–100 km radius.
- ML modelTrained on 3,445 fields — forecast from geological parameters.
- Volumetric methodThe classical formula — a fallback when no other sources are available.
Classical evaluation vs AVP AI
The same task — evaluate an asset before the investment decision. Very different cost, speed and scalability.
Classical approach
- TIME14 daysper single iteration on one asset
- COST$5,000 — $20,000for external consulting
- SCALE1 asset per 1–3 monthslimited by team capacity
- METHODDifferent Excel modelsnot comparable across assets
- AUDITExpert "black box"hard to reproduce
AVP AI
- TIME10 minutesfull cycle, business case included
- COSTfrom $2per asset run
- SCALETens of assets per dayportfolio-wide screening
- METHODUnified methodologydirect comparison and ranking
- AUDITTransparent audit trailsource for every value
FAQ on asset evaluation
At minimum, coordinates and a license number are enough. The platform automatically pulls geological parameters from open sources, reserves from the state reserves register, analogs within a 50–100 km radius and the market context.
If you have proprietary seismic, drilling or well log data, you can upload it separately and it will improve the accuracy of the evaluation.
Roughly 10 minutes for a full cycle: geology, reservoir-simulation production profile, surface facilities, economics and business case. The classical consultant-led approach takes around 14 days per iteration.
Yes. AVP AI is built for bulk screening from the start — tens to hundreds of assets per week ranked by NPV / IRR under a single methodology. This is the typical workflow for M&A and corporate strategy teams.
Cascade validation: every value is checked first against the state reserves register, then against 3–5 neighboring analogs, then against an ML model trained on 3,445 fields, and as a fallback against the classical volumetric method. The result agrees with the state reserves register within 10%.
Currently the platform is cloud-based. Deployment inside a customer's secure perimeter is possible on a case-by-case basis.
We will run one of your assets — free of charge
Give us a license number or coordinates. We will pull the geology, estimate reserves, build a production profile and NPV. No NDA, no commitments — just a result you can benchmark against your own calculations.