Bayesian global optimization approach to the oil well placement problem with quantified uncertainties

The oil well placement problem is vital part of secondary oil production. Since the calculation of the net present value (NPV) of an investment depends on the solution of expensive partial differential equations that require tremendous computational resources, traditional methods are doomed to fail. The problem becomes exceedingly more difficult when we take into account the uncertainties in the oil price as well as in the ground permeability. In this study, we formulate the oil well placement problem as a global optimization problem that depends on the output of a finite volume solver for the two-phase immiscible flow (water-oil). Then, we employ the machinery of Bayesian global optimization (BGO) to solve it using a limited simulation budget. BGO uses Gaussian process regression (GPR) to represent our state of knowledge about the objective as captured by a finite number of simulations and adaptively selects novel simulations via the expected improvement (EI) criterion. Finally, we develop an extension of the EI criterion to the case of noisy objectives enabling us to solve the oil well placement problem while taking into account uncertainties in the oil price and the ground permeability. We demonstrate numerically the efficacy of the proposed methods and find valuable computational savings

XSEDE: eXtreme Science and Engineering Discovery Environment Third Quarter 2012 Report

The Extreme Science and Engineering Discovery Environment (XSEDE) is the most advanced, powerful, and robust collection of integrated digital resources and services in the world. It is an integrated cyberinfrastructure ecosystem with singular interfaces for allocations, support, and other key services that researchers can use to interactively share computing resources, data, and expertise.This a report of project activities and highlights from the third quarter of 2012.National Science Foundation, OCI-105357