Scholarly record
USING MACHINE LEARNING TECHNIQUES TO FORECAST CUTTINGS REMOVAL IN HOLE CLEANING
Abstract
This study addresses the significant challenge of hole cleaning in drilling operations, which is essential for preventing stuck pipe incidents-a major cause of non-productive time and additional costs in drilling. This research aims to develop and validate machine learning models that enhance the prediction and optimization of cuttings removal during drilling. Utilizing a dataset derived from historical drilling operations, we employed regression analysis and neural network models to forecast the presence and height of slurry beds. The models were trained on variables such as borehole dimensions, drilling fluid characteristics, and operational parameters. Our results demonstrate that these models effectively predict conditions that could lead to stuck pipes, allowing for preemptive adjustments to drilling operations. This capability could significantly reduce unplanned downtime and associated costs. The primary contribution of this study lies in its innovative use of machine learning to transform predictive maintenance in drilling operations, offering substantial improvements in efficiency and safety. These advancements represent a crucial step forward in drilling technology, with the potential to mitigate risks and enhance operational decision-making across the industry.
Publication Impact Profile
- Captures
- Mendeley - Readers: 2
Publication details
References10
Muqeem M.A, Weekse A.E, Al-Hajji A.A. Stuck pipe best practices: a challenging approach to reducing stuck pipe costs // Saudi Arabia section technical symposium and exhibition. � Al-Khobar, Saudi Arabia, 2012. DOI: 10.2118/160845-MS
Abdelhalim Kh., Al Zaabi M., Al Ali S., Abdel Karim I., Jadallah H., Al Beshr �., Shigeru E. A Challenging Drilling Campaign Unleashed a Unique Fast Drill String Recovery Methodology Eliminating Utilizing Wireline, CT and Overcome Highly Deviated String Accessibility // Middle East Drilling Technology Conference and Exhibition. � Abu Dhabi, UAE, 2021. DOI: 10.2118/202153-MS
Aljubran M. J., Looni Q. D., Ramadhan, A. A., Neacsu M., Mora C. T., Emam A. A., Alomoush J., Ali A. M. Completion Planning and Execution of the World's Longest ICD Partially Cemented Production System in an Offshore Horizontal Well // Abu Dhabi International Petroleum Exhibition & Conference. � Abu Dhabi, UAE, 2016. DOI: 10.2118/182937-MS
Mitchell J. Trouble-Free Drilling, third edition. � Houston: Drillbert Engineering, 2004. � P. 230.
Egenti N.B. Understanding Drill-cuttings Transportation in Deviated and Horizontal Wells // Nigeria Annual International Conference and Exhibition. � Lagos, Nigeria, 2014. DOI: 10.2118/172835-ms
Malekzadeh N., Mohammadsalehi M. Hole Cleaning Optimization in Horizontal Wells: A New Method to Compensate Negative Hole Inclination Effects // Brasil Offshore. � Macae?, Brazil, 2011. DOI: 10.2118/143676MS. https://doi.org/10.2118/143676-ms
Busahmin B., Saed N.H, Alusta G. Analysis of Hole Cleaning for a Vertical Well // Open Access Library Journal, 2017. � Vol. 4. � P.1-10. https://doi.org/oalib.1103579. DOI: 10.4236/oalib.1103579
Biletskiy, M.T.; Ratov, B.T.; Syzdykov, A.K.; Delikesheva, D.N. Express method for measuring the drilling muds rheological parameters // International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM 2019 | Conference paper. DOI: 10.5593/sgem2019/1.2/S06.109
Mendez, Michael, Ahmed, Ramadan, Karami, Hamidreza, Nasser, Mustafa, Hussein, Ibnelwaleed A., Garcia, Sergio, and Andres Gonzalez. "Applications of Machine Learning Methods to Predict Hole Cleaning in Horizontal and Highly Deviated Wells." SPE Drill & Compl 38 (2023): 606�617. DOI: 10.2118/212912-PA
Soofi A. A., Awan A. Classification techniques in machine learning: applications and issues // J. Basic Appl. Sci, 2017. � Vol. 13. � P. 459-465. DOI: 10.6000/1927-5129.2017.13.76.
View or Download full articleAccess options
SWS access login
Login as SWS Scientific CommitteeLogin as SWS Scientific PartnerLogin as SWS AuthorAuthors and approved SWS contributors will read and export their own linked papers after identity matching by SWS profile, email and SGEM GlobalID.
For librarian assistance: [email protected]
Purchase Instant Access
- Article can be downloaded after successful payment.
- Article may be used according to SWS library access terms.
- Article cannot be redistributed.