Abstract
Reservoir characterization is usually a long and costly process. Engineers, geologists and geophysicists use many different methodologies to characterize the hydrocarbon reservoirs. The conventional reservoir characterization studies normally involve a series of data collection and interpretation processes that can be cost prohibitive. This chapter reviews a collection of methods developed by the author over several years in order to generate a better understanding of reservoir characteristics only using the available data. These methodologies were developed using artificial neural networks and dates back to early 1990s. In this collection of studies, the types of data used for reservoir characterization are wireline logs, core data, and magnetic resonance logs. The outcomes of the reservoir characterization studies using the tools being introduced here are effective porosity, fluid saturation and formation permeability. Based on the amount and the quality of the available data one or more of the aforementioned reservoir properties can be characterized.
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Mohaghegh, S. (2002). Taking One Step Forward in Reservoir Characterization Using Artificial Neural Networks. In: Wong, P., Aminzadeh, F., Nikravesh, M. (eds) Soft Computing for Reservoir Characterization and Modeling. Studies in Fuzziness and Soft Computing, vol 80. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1807-9_7
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