Uncertainty quantification for porous media flows

Authors:
Mike Christie;Vasily Demyanov;Demet Erbas
Affiliations:
Institute of Petroleum Engineering, Heriot-Watt University, Riccarton, Edinburgh, Scotland, UK;Institute of Petroleum Engineering, Heriot-Watt University, Riccarton, Edinburgh, Scotland, UK;Institute of Petroleum Engineering, Heriot-Watt University, Riccarton, Edinburgh, Scotland, UK
Venue:
Journal of Computational Physics - Special issue: Uncertainty quantification in simulation science
Year:
2006

Citing 8
Cited 4

Practical neural network recipes in C++

Practical neural network recipes in C++
An introduction to genetic algorithms

An introduction to genetic algorithms
Prediction and the quantification of uncertainty

Physica D - Special issue originating from the 18th Annual International Conference of the Center for Nonlinear Studies, Los Alamos, NM, May 11&mdash ;15, 1998
Evolutionary Design by Computers with CDrom

Evolutionary Design by Computers with CDrom
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
Baysian Nonparametrics via Neural Networks

Baysian Nonparametrics via Neural Networks
Inverse Problem Theory and Methods for Model Parameter Estimation

Inverse Problem Theory and Methods for Model Parameter Estimation

Numerical analysis of the Burgers' equation in the presence of uncertainty

Journal of Computational Physics
An exponential integrator for advection-dominated reactive transport in heterogeneous porous media

Journal of Computational Physics
Hierarchical benchmark case study for history matching, uncertainty quantification and reservoir characterisation

Computers & Geosciences
Bayesian estimates of parameter variability in the k-ε turbulence model

Journal of Computational Physics

Quantified Score

Hi-index	0.01

Visualization

Abstract

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.