Data Management Transformation to Drive Subsurface Autonomy

The digital transformation of subsurface processes, powered by AI and ML shall enable a deeper understanding of reservoir dynamics and efficient subsurface decisions. Subsurface autonomy has significant potential to transform the way upstream companies with limited human intervention in conducting reservoir characterisation, field development planning, well planning, and production management. To realise the full potential of AI in the subsurface domain, it requires a paradigm shift in current data management practices. This paper explores key components in transforming data management to enable AI-ready subsurface data. This addresses key challenges to facilitate AI-assisted subsurface interpretation, data-driven reservoir modelling, and the creation of subsurface digital twins. The paper highlights the emergence of industry standards such as Open Subsurface Data Universe (OSDU) in liberating data from proprietary, multivendor petrotechnical applications, ensuring seamless integration across disciplines for better decision-making. Finally, we discuss strategic actions that upstream companies must take to prepare their data to power AI and subsurface autonomy, enabling a more agile, efficient, and sustainable future for the energy industry.