From Insight to Foresight: Knowing How to Apply Artificial Intelligence in the Oil & Gas Industry

Abstract

Abstract We are in an era where digital technologies are developing at exponential rates and transforming industries wholesale. The confluence of machine learning advances, accelerated growth in acquired data, on-demand CPU and GPU driven computing such as cloud infrastructure, and other advances in automation and robotics are causing an industrial revolution that some term as the "Fourth Industrial Revolution". Given that all these transformative technologies are now available and rapidly reinventing other industries, why is the rate of adoption in the oil and gas industry so slow? How can we best utilize these advances to stop drowning in data and instead transform this data into information and knowledge in order to enable secure and intelligent automation in oilfield operations? The oil and gas industry has attempted, at times successfully, a multitude of big data and analytical techniques to further describe and analyze the systems’ or system of systems’ subsurface interactions. While the proofs of concepts have shown promise, structural difficulties embedded in the design of 20th century systems hinder the implementation of the methods and procedures now part and parcel of the 21st century, driven forth because of the Fourth Industrial Revolution. Unfortunately, 20th century procedures are not able to incorporate 21st century driven processes and methods of conducting business. We outline some of the structural challenges facing the oil and gas industry and describe a few of the solutions that have been developed to help companies in the industry. These include applications from the subsurface in geophysics, completions design, and production. Overcoming data silos in traditional data infrastructure requires a novel approach to cloud infrastructure that respects user access, data privacy, and data residency requirements of companies. Assessing data for quality and for reasonable diversity and variation in order to answer questions posed by oil & gas companies can be quite profound. This critical step prevents companies from spending lots of non-productive time and money trying to develop and tune machine learning algorithms to produce answers that are simply not available in the data. Further, getting data to be in a form suitable to apply artificial intelligence can be quite involved. We illustrate the above challenges by several subsurface examples and then describe the implementation of novel solutions. What we will show is that the oil and gas digital highway presently has data traffic jams preventing it from moving at the speed of light. Removing these traffic jams offers decision-makers the opportunity to move from insight to foresight – looking out in front instead of the rearview mirror to drive change.