Comprehensive Review on Wellbore Cleaning: Analyzing Factors, Simulation Models, and Emerging Technologies for Enhanced Drilling Efficiency | ||||
| Journal of Petroleum and Mining Engineering | ||||
| Volume 26, Issue 1 - Serial Number 10, July 2024, Page 7-36 PDF (2.13 MB) | ||||
| Document Type: Review Article | ||||
| DOI: 10.21608/jpme.2024.253199.1183 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Natalie Boyou1; Amin Sharifi Haddad1; Roozbeh Rafati1; Amany Ayman Aboulrous  2; ِAhmed Alsabagh3
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| 1School of Engineering, Aberdeen University, Scotland, UK | ||||
| 2Production Department, Egyptian Petroleum Research Institute, Cairo, Egypt | ||||
| 3Petroleum Application Department, Egyptian Petroleum Research Institute, Cairo, Egypt | ||||
| Abstract | ||||
| Inadequate wellbore cleaning is a recurring problem as removing cuttings from thousands of meters from the downhole to the surface is challenging during any drilling process. High concentrations of cuttings or any drilling fluid residues left at the bottom or challenging sections of the well could cause formation damage, impede well completion, and damage completion tools. To avoid expensive workover services such as fishing and clean-up procedures, this work comprehensively reviews past studies related to hole cleaning. Therefore, we aim to systematically provide data on recent hole-cleaning flow loop designs, and discuss the main findings of studies based on important factors affecting hole cleaning and simulation modeling. Interrelated impacts of rheological properties of drilling fluids, flow rate, hole inclination, hole eccentricity, drill pipe rotation, and cutting size in hole cleaning processes are discussed. Furthermore, this study presents an overview of various analytical and numerical modeling approaches that are used for hole-cleaning analyses. Also, the role of artificial intelligence (AI) and machine learning (ML) in predicting the hole cleaning process, was discussed. Finally, the challenges that currently exist in the hole-cleaning process are highlighted to provide insights for further investigations in this area. | ||||
| Keywords | ||||
| Hole cleaning; Drilling; Rheological properties; Artificial Intelligence | ||||
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