Oil reservoir production forecasting with uncertainty estimation using genetic algorithms

Abstract

A genetic algorithm is applied to the problem of conditioning the petrophysical rock properties of a reservoir model on historic production data. This is a difficult optimization problem where each evaluation of the objective function implies a flow simulation of the whole reservoir. Due to the high computing cost of this function, it is imperative to make use of an efficient optimization method to find a near optimal solution using as few iterations as possible. We have applied a genetic algorithm to this problem. Ten independent runs are used to give a prediction with an uncertainty estimate for the total future oil production using two different production strategies.