  With recent progress in algorithms and the availability of massive amounts of
computation power, application of machine learning techniques is becoming a hot
topic in the oil and gas industry. One of the most promising aspects to apply
machine learning to the upstream field is the rock facies classification in
reservoir characterization, which is crucial in determining the net pay
thickness of reservoirs, thus a definitive factor in drilling decision making
process. For complex machine learning tasks like facies classification, feature
engineering is often critical. This paper shows the inclusion of
physics-motivated feature interaction in feature augmentation can further
improve the capability of machine learning in rock facies classification. We
demonstrate this approach with the SEG 2016 machine learning contest dataset
and the top winning algorithms. The improvement is roboust and can be $\sim5\%$
better than current existing best F-1 score, where F-1 is an evaluation metric
used to quantify average prediction accuracy.
