An Artificial Neural Network Model for Predicting Initial Water Saturation of Petroleum Reservoirs
Anthony Ogbaegbe Chikwe
Department of Petroleum Engineering, Federal University of Technology Owerri, Nigeria.
Onyebuchi Ivan Nwanwe *
Department of Petroleum Engineering, Federal University of Technology Owerri, Nigeria.
Jude Emeka Odo
Department of Petroleum Engineering, Federal University of Technology Owerri, Nigeria.
Aliene Chibuike Patrick
Department of Petroleum Engineering, Federal University of Technology Owerri, Nigeria.
Ifeanyichukwu Michael Onyejekwe
Department of Petroleum Engineering, Federal University of Technology Owerri, Nigeria.
Christian Emelu Okalla
Department of Petroleum Engineering, Federal University of Technology Owerri, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
Initial Water saturation is the water saturation of a reservoir before production commences. It enables the reservoir engineer to properly estimate the correct volume of Oil or gas reserves and to produce without water. And over the years over estimation or under estimation had caused major changes in the decision making of oil companies. New techniques are developed as technology advances to measure water saturation. These are the most widely used techniques for determining water saturation, nevertheless. Measurements obtained directly from a sealed core, which are more expensive, or calculations made using the Archie equation on sample well logs, which are less expensive. In this Project, Artificial Neural Network (ANN) model is the sole purpose of the modelling. The datasets are gathered, processed, trained, tested and validated.
Keywords: Artificial neural network, reservoir modeling, water saturation, petro-physical calculations
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References
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