The Effect of Including Tracer Data in the Ensemble-Kalman-Filter Approach

Tracers are widely used to increase the understanding of fluid flow; they can be used to label injection fluids, hence, well connections and fluid patterns can be established when the tracer appears in production wells. Tracer data contain valuable information but are often underexploited. This paper presents methodology for assimilation of tracer data for reservoir model updating using the ensemble Kalman filter (EnKF). The presented assimilation methodology is generally applicable for all types of tracers, but the example used for demonstration focuses on gas tracers. Contrary to water tracers, which can be either nonpartitioning or partition between (oil and water) phases, gas tracers always partition between the oil and gas phases. This oil/gas partitioning is accounted for in the presented tracer transport modeling. The EnKF has recently gained popularity as a method for history matching. The EnKF includes online update of parameters and the dynamical states. An ensemble of model representations is used to represent the model uncertainty. The value of tracer data in the EnKF approach is demonstrated on a North-Sea-based example. The permeability and fault transmissibility multiplier of a reservoir are estimated by EnKF. This example shows that tracer data can be used successfully in an EnKF-based automatic updating scheme. Potential misinterpretations of gas tracer data if their partitioning is neglected is highlighted by comparing results from simulation cases where partitioning is neglected to simulation results where partitioning is accounted for.