AbstractMatching historical performance of a reservoir is a time-consuming exercise with uncertain outcome. The current practice of manual matching is subjective and goodness of the match owes largely to the experiences of the team members involved in a study and to the quality and quantity of various input and observed data.This paper describes approaches taken to speed up the history matching effort during the course of replicating multiyear performance of two West African reservoirs. The study entails validation of production data, and matching of single-well and full-field performances with two automated-history-matching (AHM) approaches based on the Gauss-Newton algorithm. These finite-difference results obtained with AHM are contrasted with those obtained from the traditional manual approach.Results show that both AHM approaches shorten length of a study, and preserve objectivity of a history match. The use of grid coarsening results in impressive speed gain that can quickly provide clues whether adjustments of certain parameters are justified for some wells. That multiple parameter adjustments can be made within specified bounds with a few iterations in a 24-hour run is a profound improvement in our speed of learning of key reservoir flow behavior. This speed, in turn, guides the study in a path that leads to rapid conclusion in a fraction of time taken in a traditional study.