Neural-network backpropagation trained on sorption isotherm analysis (SIA) data to predict Langmuir volume (V_L) and Langmuir pressure (P_L) for CH₄ and CO₂ in coalbed methane — compressing a six-input network into a three-input predictor without losing accuracy.
In coalbed methane, gas is stored by adsorption on the coal matrix — not as free gas. The sorption isotherm governs how much gas the coal can hold at a given pressure, and its two defining parameters — Langmuir volume (V_L) and Langmuir pressure (P_L) — feed directly into original gas-in-place and sorption-scale recovery-factor calculations.
Obtaining V_L and P_L traditionally requires sorption isotherm analysis (SIA) on core samples, which is expensive and not always available on every well. The question this study addressed: can a neural network learn the mapping from the rest of the SIA measurements to V_L and P_L — and if so, which inputs actually matter?
The study used artificial neural network backpropagation (ANNBP) — a feedforward network with tansig/purelin activations — organised into three sequential stages that each serve a distinct purpose:
The key insight: a 6-input network is not the same as a 6-input model. Three of the six inputs carry most of the predictive signal — the remaining three add complexity without accuracy. The reduced network is cheaper to deploy and easier to interpret.
Pattern-recognition networks for both gases achieved R² close to unity. When the input set was reduced to the three most influential parameters, prediction quality held — composite R² = 0.965 across CH₄ and CO₂ combined.
| Stage | Gas | Inputs | R² |
|---|---|---|---|
| Pattern recognition | CH₄ | 6 | 0.949 |
| Pattern recognition | CO₂ | 6 | 0.961 |
| Prediction (reduced) | CH₄ + CO₂ | 3 | 0.965 |
For CH₄ the ranked drivers are density → moisture → temperature; for CO₂ they are moisture → temperature → density. The ordering differs between the two gases, but the set of three is the same — consistent with adsorption physics, where pore-surface availability (density), competition with water for adsorption sites (moisture), and kinetic energy (temperature) are the primary levers on sorption capacity.
The reduced three-input network delivers Langmuir parameters accurate enough to feed OGIP and sorption-scale recovery-factor calculations for saturated coal — without requiring the full SIA measurement suite on every sample. The same methodology extends naturally to under-saturated coal as a follow-on study.
This work was published at the IATMI Annual Scientific Conference 2014 and became the methodological precursor to later work predicting Langmuir parameters together with gas content and permeability directly from open-hole logs.