Output details
15 - General Engineering
Heriot-Watt University (joint submission with University of Edinburgh)
An iterative stochastic ensemble method for parameter estimation of subsurface flow models
This paper introduces a new parameter estimation technique to improve the predictability of subsurface oil reservoirs. The proposed method is simple to formulate and bridges the fields of stochastic optimization, ensemble methods and standard regularization techniques. The simplicity of the method enabled further extensions by novel regularization techniques based on machine learning methods (DOI: 10.1016/j.cma.2013.02.012) and extensions to multi-optimum cases (DOI: 10.1016/j.jhydrol.2013.03.037). This paper is a result of a multi-disciplinary, international collaboration (UT-Austin, KAUST). This work was funded through the KAUST UT-Austin Academic Excellence Alliance Program (UT-Austin office of sponsored projects OSP-200702891, $3,001,332).
