الفهرس | Only 14 pages are availabe for public view |
Abstract The goal of this research is to introduce new data-driven models used to predict Rate of penetration using different parameters such as (Depth, weight on bit(WOB), Revolution per minute(RPM), Torque(T), Standpipe pressure (SPP), Flow in pump(pumping flow rate(Q) ), Mud weight, Hours on bit (HOB), Revolutions on bit, Bit diameter, Total flow area(TFA), Pore pressure, Overburden pressure, and Pit volume). Data-driven models are built using eight different machine learning techniques, using 1771 raw actual field data collected from a vertical well. |