Title: 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.