One of the most challenging tasks in the oil industry is the production of reliable reservoir forecast models. Due to different sources of uncertainties in the numerical models and inputs, reservoir simulations are often only crude approximations of the reality. This problem is mitigated by conditioning the model with data through data assimilation, a process known in the oil industry as history matching. Several recent advances are being used to improve history matching reliability, notably the use of time-lapse data and advanced data assimilation techniques. One of the most promising data assimilation techniques employed in the industry is the ensemble Kalman filter (EnKF) because of its ability to deal with non-linear models at reasonable computational cost. In this paper we study the use of crosswell seismic data as an alternative to 4D seismic surveys in areas where it is not possible to re-shoot seismic. A synthetic reservoir model is used in a history matching study designed better estimate porosity and permeability distributions and improve the quality of the model to predict future field performance.

This study is divided in three parts: First, the use of production data only is evaluated (baseline for benchmark). Second, the benefits of using production and 4D seismic data are assessed. Finally, a new conceptual idea is proposed to obtain time-lapse information for history matching. The use of crosswell time-lapse seismic tomography to map velocities in the interwell region is demonstrated as a potential tool to ensure survey reproducibility and low acquisition cost when compared with full-scale surface surveys. Our numerical simulations show that the proposed method provides promising history matching results leading to similar estimation error reductions when compared with conventional history matched surface seismic data.

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