Table 2 Summary of Feature Engineering for the three machine learning models.
From: A supervised machine learning model to select a cost-effective directional drilling tool
|  | Model number → | Model-1 | Model-2 | Model-3 |
|---|---|---|---|---|
Model output → | Bit ROP | Average ROP | Tripping/Casing running speed | |
Input data category ↓ | Feature used ↓ | |||
Directional survey | MD | √ |  |  |
Inclination | √ | √ | √ | |
DLS | √ | √ | √ | |
Drilling equipment & tools | Rig |  | √ | √ |
DD_Tech | √ | √ | √ | |
Lithology | Field | √ |  | √ |
Formation | √ |  | √ | |
Hole configurations | Hole size | √ | √ | √ |
Drilling parameters | WOB | √ |  |  |
Bit RPM | √ |  |  | |
Flow rate | √ |  |  | |
Bit ROP |  | √ | √ | |
Mud records | Mud type | √ | √ | √ |
Mud weight | √ |  |  | |
Bit record | Bit type | √ | √ | √ |
Bit condition | √ |  |  | |
Tripping data | Tripping type/Direction |  |  | √ |
