Model calibration of a real petroleum reservoir using a parallel real-coded genetic algorithm

An application of a Real-coded Genetic Algorithm (GA) to the model calibration of a real petroleum reservoir is presented. In order to shorten the computation time, the possible solutions generated by the GA are evaluated in parallel on a group of computers. This required the GA to be adapted to a multi-processor structure, so that the scalability of the computation is maximised. The best solutions of each run enter the ensemble of calibrated models, which is finally analysed using a clustering algorithm. The aim is to identify the optimal regions contained in the ensemble and thus to reveal the distinct types of reservoir models consistent with the historic production data, as a way to assess the uncertainty in the Reservoir Characterisation due to the limited reliability of optimisation algorithms. The developed methodology is applied to the characterisation of a real petroleum reservoir. Results show a large improvement with respect to previous studies on that reservoir in terms of the quality and diversity of the obtained calibrated models. Our main conclusion is that, even with regularisation, many distinct calibrated models are possible, which highlights the importance of applying optimisation methods capable of identifying all such solutions.