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USING THE KALMAN FILTER IN OIL RESERVOIR MANAGEMENT

Vasile Tudor

Abstract

Kalman filter is a set of mathematical equations that provide an efficient and recursive calculation of the means to estimate the state of a process, in a way that minimizes the average square error. The filter is very powerful in several aspects: supports estimations of past, present, and future states, and it can do so even when the precise nature of the shaped system is unknown. The concept of "closed loop" in Reservoir management is currently considered significant in the oil industry. The technique of updating the reservoir model in real-time or continuous is an essential component for applying any "closed loop" in the management process of the basic model of the reservoir. This technique should be able to quickly update the reservoir models assimilating the updates of the observations of production in its forecasts and association of uncertainty until the future optimization. Reservoir models have become an important and current part of the analysis for decision in oil and gas reservoir management. These decisions are based on the most current information available on the reservoir model and the uncertainty associated with the information. Based on a series of studies, the Ensemble Kalman Filter (EnKF) method has shown to be suitable for such applications compared to the traditional methods of historical matching (Evenson 1999, Gu and Oliver 2006, Chen 2006). Traditionally, validation of the reservoir models on the production date is performed through a process of historical matching (HM).

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DOI resolver: doi.org/10.5593/sgem2018/1.4/s06.125

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