Total and Google Cloud have signed an agreement to jointly develop artificial intelligence (AI) solutions, aimed at subsurface data analysis, for oil and gas exploration and production.
The agreement focuses on the development of AI programmes that will make it possible to interpret subsurface images, notably from seismic studies (using computer vision technology) and automate the analysis of technical documents (using natural language processing technology). These programmes will allow Total’s geologists, geophysicists, reservoir and geo-information engineers to explore and assess oil and gas fields faster and more effectively.
Under this partnership, Total geoscientists will work side-by-side with Google Cloud’s machine learning experts within the same project team based in Google Cloud’s Advanced Solutions Lab in California.
“Total is convinced that applying artificial intelligence in the oil and gas industry is a promising avenue to be explored for optimising our performance, particularly in subsurface data interpretation. We are excited to work with Google Cloud towards this goal. This builds on the strategy being developed at Total, where AI is already used, for example, in predictive maintenance at facilities,” said Marie-Noëlle Semeria, Senior Vice President, Group CTO at Total.
“We believe that the combination of Total’s geoscience expertise and Google’s artificial intelligence skills will ensure the project’s success. Our ambition is to give our geoscience engineers an AI personal assistant in the next few years that will free them up to focus on high value-added tasks.” said Kevin McLachlan, Senior Vice President Exploration for Exploration & Production at Total.
“We are thrilled to welcome Total in our Advanced Solutions Labs for the development of AI solutions,” said Paul-Henri Ferrand, president of Global Customer Operations, Google Cloud.
“We are keen to engage our best AI engineers to work with Total’s geosciences’ experts.”
Total started applying artificial intelligence to characterise oil and gas fields using machine learning algorithms in the 1990s.
