RFour Energy
RFour Energy is an independent reservoir-engineering and applied-machine-learning practice, built by engineers who cut their teeth in the field — the daily grind of subsurface analysis, development planning, and the pressure of production. We work the upstream subsurface, turning logs, production histories, and core into defensible answers about what is in the ground and how much of it will flow. Field first, intelligence later: the physical reservoir comes first and the algorithms are built to serve it — a model that cannot reproduce what is already known about a reservoir is not ready to forecast it.
What we do
We work across reservoir engineering, petrophysics, and applied machine learning for oil, gas, and geothermal. In practice that spans decline-curve and rate-transient analysis, material balance, nodal analysis, rock typing and formation evaluation — including the low-resistivity pay that quick-look logs miss — EOR screening, probabilistic volumetrics, reserves classification under PRMS, and gas- and oil-well surveillance. Each method is applied as a named, traceable technique rather than a black box.
How we work
Five steps, in order. Frame the operational question before reaching for an algorithm. Audit the data that is actually there — well tests, production histories, open-hole logs, the spreadsheets that hold the truth. Prototype a working model fast and test it against events we already remember, not just a train/test split. Document so the next engineer can pick it up without a phone call. And Revisit, because subsurface models age — the ones worth keeping are the ones we are willing to come back to and disagree with.
Selected results
A sample of applied machine-learning work, built with artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) architectures. Each is reported on held-out data, not in-sample fit alone.
| Result | Application |
|---|---|
| R² 0.997 | Petrophysics inference for cap-rock evaluation — open-hole logs to petrophysical properties, blind-tested across fields |
| NRMSE 0.032 | Unconventional reservoir properties from log data (coalbed methane) — multi-property inference |
| R² 0.965 | Sorption-parameter prediction (coalbed methane) |
We report performance with the right yardstick — R² paired with NRMSE — and, wherever possible, a blind test, because a single in-sample number is not evidence that a model generalises.
Working stack
Python and the standard ML stack (PyTorch, scikit-learn), ANN and ANFIS, physics-informed methods, and the classical reservoir-engineering toolkit — material balance, decline-curve analysis, reservoir simulation. On top sits an Excel-friendly ingestion layer, interactive web dashboards, and an agentic-AI interpretation layer. Tools are built standalone first, then integrated into the broader workflow.
Tools & writing
We build and maintain a suite of browser-based petroleum-engineering tools — selectively released from our private internal workflows — among them an integrated gas-and-oil well-surveillance system and an integrated production-reservoir system — and publish working method notes on the technical blog, organised into collections on production analysis, subsurface characterization, and reserves & reservoir performance.
How we report
Three standards run through everything: performance is stated with uncertainty (R²/NRMSE plus a blind test, never a single number); every analytic claim is traceable to a named method — Archie, Arps, Chan, Coleman-Turner, Waxman-Smits, PRMS — rather than an opaque model; and uncertainty and non-uniqueness are stated plainly, not hidden, such as treating water-drive gas-in-place as an upper bound rather than a single answer.
Get in touch
For project enquiries, reach us at info@rfourenergy.com, or start with the tools and the technical blog.