Uncertainty quantification of coal seam gas production prediction using Polynomial Chaos
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.
Keywords
Cite
@article{arxiv.1702.04781,
title = {Uncertainty quantification of coal seam gas production prediction using Polynomial Chaos},
author = {Thomas A. McCourt and Brodie Lawson and Fengde Zhou and Bevan Thompson and Stephen Tyson and Diane Donovan},
journal= {arXiv preprint arXiv:1702.04781},
year = {2017}
}