Uncertainty quantification of coal seam gas production prediction using Polynomial Chaos

TL;DR

This paper demonstrates how Polynomial Chaos can efficiently create surrogate models for coal seam gas production prediction, enabling accurate uncertainty quantification and sensitivity analysis with less computational effort.

Contribution

It applies Polynomial Chaos to a commercial solver for coal seam gas estimation, achieving accurate results with fewer data and respecting geophysical properties.

Findings

Polynomial Chaos surrogate models match complex solver accuracy

Achieved low error with small training datasets

Enabled efficient uncertainty quantification and sensitivity analysis

Abstract

A surrogate model approximates a computationally expensive solver. Polynomial Chaos is a method to construct surrogate models by summing combinations of carefully chosen polynomials. The polynomials are chosen to respect the probability distributions of the uncertain input variables (parameters); this allows for both uncertainty quantification and global sensitivity analysis. In this paper we apply these techniques to a commercial solver for the estimation of peak gas rate and cumulative gas extraction from a coal seam gas well. The polynomial expansion is shown to honour the underlying geophysics with low error when compared to a much more complex and computationally slower commercial solver. We make use of advanced numerical integration techniques to achieve this accuracy using relatively small amounts of training data.