Machine learning, artificial intelligence (AI), and subsurface characterization: applications, possibilities and risks - Berton, Fábio (Latin American Journal of Energy Research, 2023)
Software and plugins based on machine learning and artificial intelligence (AI) principles has been adapted to the processing and interpretation of subsurface data. In front of what might become a technological revolution, it is necessary to discuss the probable impacts of the new technologies. In subsurface studies in the oil and gas industry, AI has proven to be useful dealing with large volumes of geological data with homogeneous patterns, sparing the human user of repetitive tasks. This characteristic makes these software useful to increase efficiency and work safety, but the way they programmed now, they are far from being capable of dealing with the frequent geological complexity that might represent risks or opportunities in subsurface. Not even the best AI-based software are able to resolve the limitations that are inherent to subsurface data, such as lack of resolution, or lack of representativity. They also cannot generate plausible solutions to complex and specific geological conditions. The new AI-based technological solutions must be seen as tools to facilitate the work life of subsurface professionals. As any other tool, their existence have specific purposes that do not encompass the whole complexity of geological systems. The geological interpretation derived from machine learning and AI-based programs must be evaluated as geostatistical approximations, not as the representation of reality. Geoscientists will remain being necessary to apply AI-based tools correctly, and to filter the information provided by them.