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Application of parallel neural networks in reservoir characterisation from well logs
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, EAGE/SEG Research Workshop on Reservoir Rocks - Understanding reservoir rock and fluid property distributions - measurement, modelling and applications, Apr 2001, cp-55-00023
- ISBN: 978-94-6282-123-1
Abstract
A new class of neural networks for quantitative analysis of reservoir properties from well logs is demonstrated in several practical applications. The parallel neural network consists of a number of identical networks (experts) trained on identical or overlapping patterns. We demonstrate that the new artificial neoral network approach is a pragmatic and accurate alternative for converting well data to common reservoir parameters such as porosity, permeability, fluid saturation and for identification of lithofacies. Application to measurement white drilling is feasible.