Application of Particle Swarm Optimization for Parameter Estimation Integrating Production and Time Lapse Seismic Data

Abstract

Abstract The purpose of reservoir modeling is not only to build a model that is consistent with currently available data, but also to build one that gives a good prediction of its future behaviour. Updating a reservoir model to behave as closely as possible to the real reservoir is called history matching, and the estimation of reservoir properties using this method is known as parameter estimation and it is an inversion process. Here we apply one of the evolutionary algorithms (Particle Swarm Optimization – PSO) to estimate porosity and permeability using both production and 4D seismic data. PSO is a population-based stochastic optimization algorithm. It is known as a swarm intelligence algorithm because it was originally inspired by simulations of the social behaviour of a flock of birds. The method combines simplicity in implementation and high capability for distributed (parallel) computing. The results obtained on a 2D section of the Norne Field in the Norwegian Sea, demonstrate that this parameter estimation approach combines moderate computational requirements and better objective function values and exhibits good ability to handle history matching problems without exhaustive sampling of parameter space.