Uncertainty quantification is an increasingly important aspect of many areas of computational science, where the challenge is to make reliable predictions about the performance of complex physical systems in the absence of complete or reliable data. Predicting flows of oil and water through oil reservoirs is an example of a complex system where accuracy in prediction is needed primarily for financial reasons. Simulation of fluid flow in oil reservoirs is usually carried out using large commercially written finite difference simulators solving conservation equations describing the multi-phase flow through the porous reservoir rocks. This paper examines a Bayesian Framework for uncertainty quantification in porous media flows that uses a stochastic sampling algorithm to generate models that match observed data. Machine learning algorithms are used to speed up the identification of regions in parameter space where good matches to observed data can be found. © 2006 Elsevier Inc. All rights reserved.
|Number of pages||16|
|Journal||Journal of Computational Physics|
|Publication status||Published - 1 Sept 2006|
- Artificial neural networks
- Genetic algorithm
- Stochastic sampling