The melding of artificial and human intelligence in digital subsurface workflows: a historical perspective

Abstract

Artificial intelligence (AI) is not some elusive, mystical technology that humanity is chasing, especially in regard to its usage in digital subsurface workflows. Artificial intelligence has been complementing human intelligence since the 1960s, and AI was deeply integrated into our personal and professional lives long before the technological revolutions of the 21st century. However, we tend to not realize how intrinsic AI is to our lives already. We are constantly moving the goalposts for defining AI as it solves more and more problems. This is known as the AI effect, where people tend to only think of AI as ‘whatever hasn’t been done yet’ (Hofstadter, 1979). This article attempts to review the historical melding of human and artificial intelligence in digital subsurface workflows, with some extra focus on the field of geophysics. Over time, exploration and production activity has become increasingly automated, with no signs of slowing down. This has enabled domain experts to focus on more meaningful tasks such as research, domain concepts, and decision-making. The automation of data processing in the form of artificial intelligence and machine learning has already transformed the fields of geophysics, geology, and reservoir engineering in deeply powerful ways. Initially, human intelligence was only augmented by technology, but over time, more and more tasks traditionally performed by human minds were shifted on to computers. The offloading of these human tasks on to computers were implementations of artificial intelligence, and the added abilities to automatically learn and improve from experience without explicit programming were implementations of machine learning.