Technical Paper

Machine Learning and Learning From Machines

Written by: Ehsan Naeini and Kenton Prindle

Machine learning has been around for decades or, depending on your view, centuries. To consider the tools and underpinnings of machine learning, one would need to go back to the work of Bayes and Laplace, the derivation of least squares, and Markov chains, all of which form the basis and the probability construct used pervasively in machine learning. There has been a flood of progress between 1950 (with Alan Turing’s proposal of a learning machine) and early 2000 (with practical applications of deep learning in place and more recent advances such as AlexNet in 2012). Deep learning has demonstrated tremendous success in a variety of application domains in the past few years, and with some new modalities of applications, it continues to open new opportunities. The recent popularity and emergence of machine learning in the oil and gas industry is likely due to the abundance of unused or overlooked data and the economic need to extract additional information from the data currently used. Additionally, there is an unprecedented availability of computing power, easy-to-use coding libraries, and application programming interfaces, as well as recent and significant advances in various flavors of neural networks. In this paper, we will attempt to show how machine learning can assist geoscientists in performing routine tasks in a much shorter time frame. We assert that there is a great opportunity for geoscientists to learn from machines, use these techniques to quality check their work, and gain nuanced insights from their data. Another advantage is that these approaches lead to the optimization of workflows by providing more accurate training data sets thus driving continuous learning and enhancement of the model.