Successful Development and Deployment of a Global ROP Optimization Machine Learning Model
Prepared with Petronas
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Location Offshore, deepwater, Malaysia Challenge Improve formation insight and increase drilling performance in a clay and sandstone environment known to cause slow rate of penetration (ROP). Solution Field trial real-time ROP agent in a side-track well section. Run stuck pipe hole cleaning agent to understand risk of increased cuttings in suspension and cave-ins. Results Rig…
Predictive machine learning agents were used by the real-time operation center to make informed decisions and guide the rig crew through hazardous, high dogleg and high inclination intervals.
National Oil Company (NOC) experienced a stuck pipe while drilling the main wellbore of a production well, and as a consequence needed to drill a costly side-track to reach the target. The side-track required casing to be run through two high dogleg-severity (DLS) and high inclination intervals, adding further risk of stuck pipe and cost increases.