WellScout AI screens orphaned and retiring oil & gas wells to identify the best candidates for geothermal power, grid-scale energy storage, and direct lithium extraction — before the plug-and-abandon order.
WellScout AI evaluates orphaned and retiring wells across three complementary revenue streams, letting operators extract maximum value from legacy assets before plug-and-abandon.
Repurpose casing heat from retired oil wells to generate continuous, baseload electricity via modular Organic Rankine Cycle (ORC) hardware deployable in under six months.
Leverage wellbore geometry and subsurface thermal mass to deploy compressed-air and thermal energy storage solutions, turning idle infrastructure into dispatchable grid assets.
Identify produced-water streams from existing wells with battery-grade lithium brine concentrations, enabling DLE deployment with no new drilling required.
WellScout replaces speculative greenfield exploration with data-driven conversion of known assets.
Operational in under 6 months versus the 5–7 year timeline of traditional geothermal or greenfield resource development.
Converts Asset Retirement Obligations into lease revenue. Operators replace a $150,000 plugging liability with a long-term income stream.
Progresses from single-well diesel displacement to multi-well Virtual Power Plants to a global SaaS and hardware licensing model.
Every evaluated well is screened across all three verticals simultaneously — thermal, storage, and lithium — maximizing value-per-asset.
WellScout is built on PINNs — a class of neural network that doesn't just learn from data, it also obeys the fundamental equations governing heat, fluid flow, and pressure underground. The result: accurate subsurface predictions even when well data is sparse.
Imagine you're trying to find hidden treasure underground — heat, lithium, or storage potential — without drilling everywhere. A regular AI is like a student who memorized past test answers: it guesses based only on the measurements it has seen before. If the data is patchy, the guesses get sloppy.
A Physics-Informed Neural Network is a student who also understands why the answers are correct. It has the laws of physics baked in — how heat travels through rock, how fluids move underground, how pressure changes with depth. So even when measurements are missing, it fills in the gaps the same way a physicist would: by following the rules the Earth itself obeys. It literally cannot predict something that breaks the laws of physics.
Most orphaned wells have incomplete records. PINNs use physics to interpolate between sparse measurements rather than guessing blind — critical for screening wells with decades-old or partial logs.
Heat conduction PDEs constrain the thermal gradient predictions, ensuring estimated bottom-hole temperatures are physically plausible — not just statistically convenient.
Navier-Stokes and Darcy flow equations guide lithium brine concentration and energy storage reservoir modeling, catching physically impossible predictions before they mislead an investment decision.
Because the model cannot violate thermodynamics or fluid mechanics, it self-corrects overfit predictions — surfacing only candidates that are physically viable, not just statistically correlated.
WellScout AI pairs a data intelligence layer with modular field hardware, covering the full lifecycle from screening to deployment.
A staged rollout from on-site diesel displacement to global licensing across all three verticals.
Field diesel displacement at active drilling sites. Single-well pilots validate thermal, storage, and lithium economics with real operators and real infrastructure.
Multi-well aggregation feeding local grids. Thermal, storage, and lithium output streams combined into unified energy and resource offtake agreements.
International SaaS and manufacturing licensing of the WellScout AI platform and Geo-Pod hardware across all three verticals worldwide.
Request access to the investor data room or open one of the WellScout screeners to evaluate wells across thermal, energy storage, and lithium verticals.