The main objective of the present work is to numerically determine how sensor information may aid in reducing the ill-posedness associated with permeability estimation via 4-D seismic history matching. These sensors are assumed to provide timely information of pressures, concentrations and fluid velocities at given locations in a reliable fashion. This information is incorporated into an objective function that additionally includes production and seismic components that are mismatched between observed and predicted data. In order to efficiently perform large-scale permeability estimation, a coupled multilevel, stochastic and learning search methodology is proposed. At a given resolution level, the parameter space is globally explored and sampled by the simultaneous perturbation stochastic approximation (SPSA) algorithm. The estimation and sampling performed by SPSA is further enhanced by a neural learning engine that estimates sensitivities in the vicinity of the most promising optimal solutions. Preliminary results shed light on future research avenues for optimizing the frequency and localization of 4-D seismic surveys when sensor data is available.

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