Advanced CFD–DEM Analysis of Cuttings Transport Efficiency in Oil Well Drilling

EasyChair Preprint no. 13414

Abstract

Efficient hole cleaning in oil and gas drilling operations is crucial for maintaining drilling efficiency and preventing operational issues. This study employs a fully coupled Computational Fluid Dynamics (CFD) and Discrete Element Method (DEM) approach to investigate the effect of mud hydrodynamics on cuttings transport. The CFD-DEM model integrates the Eulerian approach for fluid dynamics with the Lagrangian approach for particle dynamics, considering the two-way interaction between fluid and solid phases. Key parameters such as mud rheology, flow rate, cutting size, drill string rotation, and inclination angle are analyzed to understand their impact on cuttings transport efficiency.

The results indicate that increasing mud viscosity improves cuttings suspension but also increases pressure drop, necessitating higher pump power. Higher flow rates enhance cutings transport, but benefits diminish at extreme turbulence levels due to re-suspension. Smaller cuttings are more effectively transported compared to larger ones. Drill pipe rotation significantly affects cuttings transport at lower velocities, particularly in deviated wells, but has minimal impact at higher velocities. The inclination angle of the wellbore also plays a crucial role, with horizontal wells exhibiting lower transport efficiency due to gravitational settling.

This study provides valuable insights for optimizing drilling fluid properties and operational parameters to improve hole cleaning efficiency. The findings highlight the importance of a comprehensive understanding of fluid-particle interactions and the need for careful consideration of multiple factors in drilling operations.

Keyphrases: CFD-DEM Simulation, cuttings transport, Mud Hydrodynamics, oil well drilling

@Booklet{EasyChair:13414,
  author = {Kurez Oroy},
  title = {Advanced CFD–DEM Analysis of Cuttings Transport Efficiency in Oil Well Drilling},
  howpublished = {EasyChair Preprint no. 13414},

  year = {EasyChair, 2024}}