FUZZINESS, INCOMPLETENESS AND RANDOMNESS - CLASSIFICATION OF UNCERTAINTY IN RESERVOIR APPRAISAL

The rapid progress in computer technology in recent years has enabled the development of increasingly complex simulators, which can handle l arge amounts of data. It is often assumed that this automatically lead s to more accurate static and dynamic reservoir models. In reality, ho wever, there is still much evidence that the predicted performance of a reservoir often differs vastly from the actual production behaviour. These deviations are an indication of the failure to understand the p rocesses involved and to recognize the uncertainty inherent in the def inition of important reservoir characteristics. In this paper, a class ification scheme is proposed, in which uncertainty is expressed as fuz ziness, incompleteness and randomness. Each of these elements is descr ibed in detail and illustrated within the context of reservoir apprais al, although the approach san be applied to the wider aspects of petro leum geoscience. It is believed that adopting this classification sche me will enable the geoscientist to build a more extensive picture of u ncertainty in reservoir appraisal. It will also be invaluable as a too l with which to inform management of the existing uncertainty, using a consistent language, thus providing guidance in the decision-making p rocess.