Uncertainty quantification of coal seam gas production prediction using Polynomial Chaos
For the coal seam gas industry, this provides a computationally efficient method for uncertainty quantification and sensitivity analysis of production predictions.
The paper applies Polynomial Chaos surrogate modeling to a commercial coal seam gas well solver, achieving low error in predicting peak gas rate and cumulative gas extraction while using relatively small training data.
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.