Applied Artificial Intelligence in the Subsurface

Abstract

In this paper we present an example of improved approaches for how to interact with data and leverage artificial intelligence for the subsurface. Currently, subsurface workflows typically rely on a lot of time-consuming manual input and analysis, but the promise of artificial intelligence is that, once properly trained, an AI can take care of the more routine tasks, leaving the domain expert free to work on more complex and creative parts of the job. Artificial intelligence work on subsurface datasets in recent years has typically taken the form of research and proof of concept type work, with a lot of one-off solutions showing up in the literature using new and innovative ideas (e.g. Hussein et al, 2021; Misra et al, 2019). Oftentimes this work requires a good degree of data science knowledge and programming skills on the part of the scientist, putting many of the approaches outlined in these and a multitude of other papers out of reach for many subsurface experts in the Oil and Gas industry. In order for Artificial Intelligence to become applied as part of regular workflows in the subsurface, the industry needs tools built to help subsurface experts access AI techniques in a more practical, targeted way. We present herein a practical guide to help in developing applied artificial Intelligence tools to roll out within your organization or to the industry more broadly.