Fractured reservoir history matching improved based on artificial intelligent

Abstract In this paper, a new robust approach based on Least Square Support Vector Machine (LSSVM) as a proxy model is used for an automatic fractured reservoir history matching. The proxy model is made to model the history match objective function (mismatch values) based on the history data of the field. This model is then used to minimize the objective function through Particle Swarm Optimization (PSO) and Imperialist Competitive Algorithm (ICA). In automatic history matching, sensitive analysis is often performed on full simulation model. In this work, to get new range of the uncertain parameters (matching parameters) in which the objective function has a minimum value, sensitivity analysis is also performed on the proxy model. By applying the modified ranges to the optimization methods, optimization of the objective function will be faster and outputs of the optimization methods (matching parameters) are produced in less time and with high precision. This procedure leads to matching of history of the field in which a set of reservoir parameters is used. The final sets of parameters are then applied for the full simulation model to validate the technique. The obtained results show that the present procedure in this work is effective for history matching process due to its robust dependability and fast convergence speed. Due to high speed and need for small data sets, LSSVM is the best tool to build a proxy model. Also the comparison of PSO and ICA shows that PSO is less time-consuming and more effective.

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