Factors affecting drilling incidents: Prediction of suck pipe by XGBoost model

Abstract

The unproductive time is very high due to drill string jamming. So the main objective of this research is to determine the influences of the parameters in the accidents of stuck pipes using the construction of XGBoost models. To develop the model, drilling parameters are taken from daily drilling reports of the construction of 30 wells in the West Qurna field, Iraq. The data includes well drilling reports from 2013 to 2020. The results show that the factors such as Measured depth (MD), Rate of penetration (ROP), 10-sec gel (GEL1), Plastic Viscosity (PV), Mud weight (MW), Yield point (YP) contribute positively to the model predictions. In contrast, the factors such as Flow rate (FR), Rotation per minute (RPM), Filtrate (API/HPHT-FILTR and Bottom hole assembly (BHA) length has a negative contribution to the final model predictions. This research concluded that pipe sticking in the borehole is primarily due to inclination, penetration rate, and flow rate. This study is useful in the drilling of any field.

Keywords

• Machine learning
• Drilling
• Stuck pipe
• Sensitivity analysis
• XGBoost model