RFour Energy is where petroleum engineering meets machine learning for smarter subsurface and production decisions.
Forecasting, anomaly detection, physics-informed models — when analytical methods run thin.
Neural networks, ANFIS, physics-informed models — on real wells, not toy datasets.
Material balance, DCA, economics — auditable, transparent, defensible.
Production data, well tests, simulation — for the morning meeting, not the demo.
Deep field experience in reservoir engineering, production surveillance, and applied AI research — across multiple basin and reservoir types. The list below is what we've actually worked on long enough to break, fix, and explain to someone else.
Start with the operational question, not the algorithm. Most subsurface problems are misframed long before they're modelled — the time spent here saves the rest.
Inventory what's actually there: well tests, production histories, open-hole logs, simulation runs, the Excel tabs that somehow hold the truth. Surface what's signal, what's swamp.
Build a working model fast — tested against historical events we actually remember, not just train/test splits. If it can't reproduce a known outcome, it isn't ready.
Notebooks, an Excel-friendly tool, a serialized model with notes — whatever lets the next engineer pick it up without a phone call.
Subsurface models age. The ones we keep are the ones we're willing to come back to in six months and disagree with.
Stability-aware classifier — methodology informing GOWIS.
Open-hole logs → petrophysical properties. Blind-tested across fields.
Multi-property AI inference from open-hole logs.
Multi-gas AI prediction from laboratory sorption data.
Performance inference from rock + completion parameters.
Candidate screening, pilot, and field-wide deployment.
Integrated subsurface, drilling, and facility scope.
Map regeneration to localize undrained zones.
RFour Energy is forged by petroleum engineers who cut their teeth in the field—mastering the daily grind of subsurface analysis, complex development planning, and the relentless pressure of production. For us, the physical reservoir always came first; the algorithms were built to serve it.
We deploy Machine Learning at the exact point where classical physics gets noisy and conventional spreadsheets reach their limit. Our focus is precision: gas well surveillance, automated diagnostics, deep reservoir inference, and real-time well health classification. The tools showcased here are the refined outputs of our private internal workflows.
RFour Energy serves as the vault for what we architect, code, and occasionally release. We operate without the noise of traditional marketing. This isn't a brand built for the masses; it is a seal of technical integrity for the work we deliver.
Most of the AI on this site is just talk. This one is in production — built end-to-end on Cloudflare Workers + D1 + a frontier LLM, with tool calls grounded in live field surveillance data.
DCA, material balance, gas well diagnostics, nodal analysis, rate-transient analysis (RTA), rock typing intelligence, EOR screening, and a surveillance dashboard with autonomous AI monitoring — in production, open to anyone who finds them useful.
Technical notes on reservoir engineering, production surveillance, decline analysis, and applied machine learning in upstream oil & gas.
A Python, agentic AI, Excel, and dashboard framework for modern reservoir characterization. Six methods, one platform — Winland R35, FZI/HFU, PGS, Lorenz, J-function, and ML on logs.
Practitioner's guide to nodal analysis. Where IPR meets VLP, why the operating point is never decided at any single component, and how to translate seventy years of correlations into something that runs on your laptop.
Why P50 isn't enough — and how Monte Carlo Simulation transforms volumetric reserves estimation. Distributions, multi-zone aggregation, and tornado analysis.
When Arps over-extrapolates, plot 1/q vs Nₙ/q, fit a line, take the inverse slope. A rigorous DCA alternative.
OGIP and drive diagnosis from pressure decline. P/Z, Cole, Roach plots with three worked numerical examples.
Mature waterfloods need a different forecasting tool than rate-time decline. Practical guide with worked example.
Coning, channeling, or near-wellbore problem? Read WOR signatures correctly and the workover plan writes itself.
When to use exponential, hyperbolic, or harmonic. The b-factor as reservoir physics, not a fitting knob.
Gas wells don't fail because the reservoir runs dry — they fail because liquids win the upward race. Physics, equation, interpretation.
Systematic EOR candidate selection — eight recovery methods scored against reservoir parameters using Taber, NPC, and Green & Willhite criteria. CO₂, steam, polymer, ASP and more.
Reservoir question, ML approach, dataset you'd like a second pair of eyes on, or just a topic worth discussing — drop a note. Usually replies within a business day.
