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Application of parallel neural networks in reservoir characterisation from well logsNormal access

Authors: A. Bhatt, H. B. Helle and B. Ursin
Event name: EAGE/SEG Research Workshop on Reservoir Rocks - Understanding reservoir rock and fluid property distributions - measurement, modelling and applications
Session: Reservoir Rocks - Understanding reservoir and fluid property distributions - measurement, modelling and applications
Publication date: 30 April 2001
DOI: 10.3997/2214-4609.201406732
Organisations: SEG, EAGE
Language: English
Info: Extended abstract, PDF ( 1.62Mb )
Price: € 20

Summary:
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.


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