Tackling an Inverse Problem from the Petroleum Industry with a Genetic Algorithm for Sampling

Abstract. When direct measurement of model parameters is not possible, these need to be inferred indirectly from calibration data. To solve this inverse problem, an algorithm that preferentially samples all regions of the parameter space that fit data well is needed. In this paper, we apply a real-parameter Genetic Algorithm (GA) to sample the parameter space for the inverse problem of calibrating a petroleum reservoir model. This results in several important insights into this nonlinear inverse problem.

• Type: Conference
• Year: 2004
• Where: GECCO
• Authors: Pedro J. Ballester, Jonathan N. Carter
• Keywords: GECCO 2004, Inverse Problem, Nonlinear Inverse Problem, Parameter Space