Machine learning agent significantly reduces drilling time on ultra-deepwater exploration well

Location Gabon, West Africa Ultra deepwater Challenge Show that real-time rate of penetration (ROP) optimization can signifi cantly contribute to improving ROP, decreasing drilling time and reducing costs, even when an auto driller is used. Solution The agent was used by RTO engineers to provide the rig crew with real-time drilling parameter recommendations for optimizing…

Unsupervised machine learning: A well planning tool for the future

OMEA2022-78423: Peter Batruny, PETRONAS Carigali Sdn Bhd, Tim Robinson, Exebenus

Abstracts: In recent years, the industry has sought insights from abundant data generated by drilling operations. One of the key focus areas is the rate of penetration (ROP) which impacts costs directly, and emissions indirectly. Previous work has succeeded in predicting and optimizing ROP, however was limited to specific fields and small-scale applications. This limitation stems from unobserved information between different fields or operations that often impacts model usability. This paper provides a new way of well planning by leveraging the power of unsupervised machine learning to deliver higher drilling efficiency, lower costs, and less uncertainty.