Experimental and numerical study of cuttings transport in inclined drilling operations

Directional drilling has become popular in recent decades in both onshore and offshore operations due to reduced drilling costs and improved recovery. In a directional well drilling operation, drill cuttings tend to settle down at the lower side of the inclined annular section. If the generated cuttings are not removed from the hole section properly, it causes a cuttings bed formation in the annular section. Different drilling related problem such as poor rate of penetration, excessive torque and drag, increase differential sticking often associated with a poor hole cleaning which eventually lead to increase in non-productive time (NPT). Therefore, the success of inclined well drilling operation largely depends on effective cleaning of drill cuttings from the annular section. A variety of parameters, including the fluid rheology, mud velocity, cuttings size, drill pipe rotation and drill pipe inclination generally influence the cuttings transport performance. Optimization of these drilling parameters is crucial to ensure proper hole cleaning. In this study, a Computational Fluid Dynamics (CFD) model for the inclined well section is used to investigate the cuttings transport efficiency (CTE). An Eulerian-Eulerian multiphase flow model is proposed and validated with lab scale experimental data. The experiments were performed in a 6.16-m-long annular test section having an outer pipe diameter of 4.5-inch and an inner pipe diameter of 2.5-inch. The setup is equipped with Electrical Resistance Tomography (ERT) system to measure the instantaneous solid volume fraction and a visualization section. A non-Newtonian Heschel-Bulkley (HB) fluid was used as drilling mud and solid glass beads of 2.50 mm–3.00 mm were used as cuttings in the experiment. This study shows a good agreement in visualization of mechanistic three-layer model of cuttings transport in terms of ERT data from experiment and CFD simulation. The validated CFD model is used to perform 5- Factors factorial design and analysis of variance (ANOVA) study. ANOVA shows that the interaction effects of mud velocity-cuttings size, mud velocity-inclination are statistically significant. Finally, this study proposed a statistical model to estimate the CTE of an inclined well considering the two factor and three factor interactions among the variables. Also, the model shows that drill pipe rotation has negligible effect in improving cuttings transport efficiency in the inclined well. The proposed model also reveals that cuttings size and fluid velocity account for 78% contribution in the transport efficiency for an inclined well. Furthermore, an Artificial Neural Network (ANN) method is used to verify the contribution of lower order interaction in the statistical model. Though empirical model shows few lower order two factor interactions (fluid rheology -cuttings size, fluid rheology-inclination), and three factor interactions (velocity-cuttings size-inclination, fluid rheology-cuttings size-inclination); ANN model shows that lower order interaction are significant in model prediction and should not be ignored. The findings of this study can help in better understanding the interaction behaviour among drilling parameters and optimized cuttings transport efficiency in the inclined drilling operation for a wide variety drilling parameters.