1887

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.

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/content/papers/10.3997/2214-4609.201406732
2001-04-30
2020-10-30
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201406732
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