Uncertainty quantification of coal seam gas production prediction using Polynomial Chaos
| Autor: | Diane Donovan, Stephen Tyson, Bevan Thompson, Fengde Zhou, Brodie A. J. Lawson, Suzanne Hurter, Thomas A. McCourt |
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| Rok vydání: | 2017 |
| Předmět: |
Mathematical optimization
Polynomial chaos business.industry Coal mining 010103 numerical & computational mathematics Solver 010502 geochemistry & geophysics Geotechnical Engineering and Engineering Geology 01 natural sciences Numerical integration Fuel Technology Surrogate model Applied mathematics Probability distribution 0101 mathematics Uncertainty quantification business Polynomial expansion 0105 earth and related environmental sciences Mathematics |
| Zdroj: | Journal of Petroleum Science and Engineering. 157:1148-1159 |
| ISSN: | 0920-4105 |
| DOI: | 10.1016/j.petrol.2017.08.012 |
| Popis: | A surrogate model approximates a computationally expensive solver. Polynomial Chaos is a method used 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. |
| Databáze: | OpenAIRE |
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