Machine learning for subsurface energy and CCUS, Geothermal
The digital footprint is shaping the energy industry and the growth of renewables will squarely depend on the implementation of exponential technology. It can unleash its power from optimization to identification of issues and operational parameters. The subsurface facilities can be optimized using machine learning algorithms to improve their efficiency.
The Carbon Capture and Underground Storage (CCUS) is a complex mechanism which requires multi-prong approach to make it efficient. The data science provides tools and techniques to facilitate the understanding of underlying mechanism and operational efficiency.
Geothermal energy has magnitude of uncertainity and multitude of data which is captured at every stage of operation. From exploration to extraction, different geological characteristics to operational challenges, machine learning algorithms can be used to improve the process.
Our team is developing tools and techniques that will be game-changer and hasten the realization of technologies.