Digitalization Lead – Deepwater Technology, Shell
Machine Learning in Oil & Gas - Moving from Hype to Reality
There is much hype around all topics related to digitalization and IR 4.0 and their underlying technologies. At the same time, industries have already seen some real-life applications and benefits as well as some real disruption to their value chains, making it more urgent than ever for oil & gas companies to accelerate their digital transformations. Despite the hype and the promise of machine learning, adoption outside of the tech sector is still at an early, often experimental stage with few firms having deployed it at scale.
This presentation will discuss some of the successes and challenges of moving machine learning in Oil & Gas from hype to reality. There have been plenty of promising successful deployments across robotics and autonomous vehicles, computer vision, virtual agents, and machine learning, with the latter including deep learning and underpinning many recent advances in the other AI technologies. However, the fact that it requires big data that must be trained on what’s often sparse, incomplete, and messy data, its tendency to cut across functional, geographic, and organizational silos, and its dependence on having a digital foundation and an upskilled workforce pose formidable challenges to scaling digital and machine learning in particular.