This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 196675, “Visual Recognition of Drill-Cuttings Lithologies Using Convolutional Neural Networks To Aid Reservoir Characterization,” by Muhammad Kathrada, SPE, and Benjamin Jacob Adillah, Petronas, prepared for the 2019 SPE Reservoir Characterization and Simulation Conference and Exhibition, Abu Dhabi, 17-19 September. The paper has not been peer reviewed.

Drill cuttings and core images often present classification problems. Development of an unbiased objective system that can overcome the various issues creating these difficulties is an important goal. Advances during the past decade in using convolutional neural networks (CNNs) for visual recognition of discriminately different objects means that now object recognition can be achieved to a significant extent. The benefit of such a system would be improvement of reservoir understanding by having all available images classified in a consistent manner, thus keeping characterization consistent as well.

Keywords:
deep learning, machine learning, upstream oil & gas, transfer function, relu layer, artificial intelligence, drill-cutting image, alexnet, activation map, validation
Subjects:
Information Management and Systems, Neural networks
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2019. Society of Petroleum Engineers
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