A Learning Computational Engine for History Matching

The main objective of the present work is to propose and evaluate a learning computational engine for history matching, which is based on a hybrid multilevel search methodology. According to this methodology, the parameter space is globally explored and sampled by the simultaneous perturbation stochastic approximation (SPSA) algorithm at a given resolution level. This estimation is followed by further analysis by using a neural learning engine for evaluating the sensitiveness of the objective function with respect to variations of each individual model parameter in the vicinity of the promising optimal solution explored by the SPSA algorithm.<br>The proposed methodology is used to numerically determine how additional sources of information may aid in reducing the ill-posedness associated with permeability estimation via conventional history matching procedures. The additional sources of information considered in this work are related to pressures, concentrations and fluid velocities at given locations in a reliable fashion, which in practical scenarios might be estimated from high resolution seismic surveys, or directly obtained as in situ measurements provided by sensors. This additional information is incorporated, along with production data, into a multi-objective function that is mismatched between the observed and the predicted data. The preliminary results presented in this work shed light on future research avenues for optimizing the use of additional sources of information such as seismic or sensor data in history matching procedures. <br>