Reservoir characterization is very important to the success of a history matching and production forecasting. Thus, numerical simulation becomes a powerful tool for the reservoir engineer in quantifying the impact of uncertainties in field development and management planning, calibrating a model with history data and forecasting field production, resulting in a reliable numerical model. History matching has been integrated into several areas, such as geology (geological characterization and petrophysical attributes), geophysics (4D seismic data), statistical approaches (Bayesian theory and Markov field), and computer science (evolutionary algorithms). Although most integrated history-matching studies use a unique objective function (OF), this is not enough. A history matching by simultaneous calibrations of different OF is necessary because all wells must have their OF near the acceptance range as well as maintain the consistency of generated geological models during reservoir characterization. The main goal of this work is to integrate history matching and reservoir characterization; applying a simultaneous calibration of different OF in a history matching procedure and keeping the geological consistency in an adjustment approach to reliably forecast production. We also integrate virtual wells and geostatistical methods into the reservoir characterization to ensure realistic geomodels without creating the geological discontinuities to match the reservoir numerical model. The proposed integrated calibration methodology consists of using a geostatistical method for modelling the spatial reservoir property distribution based on the well log data, running a numerical simulator and adjusting conditional realizations (models) based on geological modeling (variogram model, vertical proportion curve and regularized well log data) and reservoir uncertainties, using a simultaneous adjustment of different OF to evaluate the history matching process and virtual wells to perturb geological continuities such as channels and barriers. In conclusion, we present an effective methodology to preserve the consistency of geological models during history matching process. In addition, we simultaneously combine different OF to calibrate and validate the models with well production data. Reliable numerical and geological models are used in the forecasting production under uncertainties to validate the integrated procedure.

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