Geosteering is the process of optimal placement of a well based on the results of real-time downhole measurements rather than pre-defined targets in space. Therefore, the process can be improved by either improving the measurements or by advances in placement decision optimization. The measurement tools have been extensively developed for the last two decades and are continuously improving. However, there has been much less focus on improving well placement through improved decision optimization. In this paper, we introduce a formal classification for decision optimization policies for geosteering under geological uncertainty. We distinguish myopic, naïve and farsighted optimization policies, which differ by whether future decisions and learning are considered for optimization. The paper describes the three approaches and classifies the methods used in earlier publications. Moreover, we present a synthetic example, which demonstrates that different policies would result in different decision recommendations and, hence, different well placements.


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