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Permeability prediction using machine learning, exponential, multiplicative, and hybrid models

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The authors present a unified methodology for permeability prediction with nonlinear multiplicative, exponential, and hybrid multiplicative-exponential nonlinear models. Due to logarithmic transform of these models they may be used for prediction with both linear regression and various machine learning methods. It was demonstrated that enhancement of prediction accuracy is achieved with new two-level adjustable committee machines. The new prediction methodology was tested on data from sandstone and carbonate reservoirs with similar pattern of improvement of prediction accuracy due to using nonlinear prediction models and two-level committee machines.
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Keywords: AND; EXHAUSTIVE MODELS SEARCH; EXPONENTIAL; HYBRID NONLINEAR MODELS; MACHINE LEARNING; MULTIPLICATIVE; NEURAL NETWORKS; PERMEABILITY PREDICTION; TWOLEVEL COMMITTEE MACHINES

Document Type: Research Article

Publication date: 01 December 2017

This article was made available online on 18 November 2017 as a Fast Track article with title: "Permeability prediction using machine learning, exponential, multiplicative, and hybrid models".

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