Improved torque and drag modeling using traditional and machine learning methods




Oyedere, Mayowa Olugbenga

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During the drilling process, the drillstring inadvertently comes in contact with the wellbore generating frictional losses in the rotating moment (torque) and axial force (drag). These losses reduce the rotational power available at the drill bit, thus making adequate torque and drag modeling a critical piece in the drilling puzzle. The simplifying assumptions of the widely used soft-string model for torque and drag modeling make it less accurate for new complex well designs, creating the need for the use of the more robust stiff-string model. This first part of this dissertation focuses on a new approach to developing a stiff-string model that can be easily implemented for well planning. The stiff-string model addresses the pitfalls of the soft-string model by using cubic splines for its well-path trajectory. It solves the three coupled, non-linear ordinary differential equations that describe the motion of the drillstring at each survey point to account for the shear forces and bending stiffness. The stiff-string model is then applied to design four horizontal wells. Drilling Optimization has consistently generated research interest over the years because of the cost-saving benefits of improving drilling efficiency. Rate of penetration (ROP) and torque-on-bit (TOB) predictions have become critical to the successful drilling optimization efforts. The second part of this dissertation focuses on the prediction and optimization of TOB using five regression-based machine learning algorithms. TOB was modeled as a function of rotary speed (RPM), weight-on-bit (WOB), flow rate, pump pressure, and unconfined compressive strength (UCS). Three direct search optimization algorithms—Nelder Mead, differential evolution, and particle swarm optimization (PSO)—were used to optimize TOB. The final part of this dissertation introduces a novel approach to ROP and TOB prediction by modeling it as a classification problem with two regions (low and high ROP and TOB respectively) based on a user-defined threshold. Five different classification algorithms were implemented and compared using the area under curve (AUC) classification metric. Finally, a probability gradient tool was developed to help inform the drilling engineer on the best combination of WOB and RPM to yield the desired drilling performance


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