Multi-objective optimization for reservoir modelling and seismic data matching: proof of concept and field application

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We present a new method to generate reservoir models by combining geostatistical simulation and optimization of multiple objective functions; including seismic data matching (i.e. a reservoir model seismic matching loop). Our method is used to estimate static reservoir models by simultaneously integrating several datasets including well logs, geologic information and various seismic attributes. The key advantage of our proposed method is that we can define multiple objective functions for a variety of data types and constraints, and simultaneously minimize the data misfits. Using our optimization method, the resulting models converge towards Pareto fronts, which represent the sets of best compromise model solutions for the defined objectives. We test our new approach on a 3D object-oriented reservoir model, where variogram-based simulation techniques typically fail to produce realistic models. Our results indicate that improved reservoir facies and porosity models and flow-unit connectivity can be obtained with this new multi-objective optimization approach.