Peer-reviewed articles 17,970 +


Title: USING MACHINE LEARNING TECHNIQUES TO FORECAST CUTTINGS REMOVAL IN HOLE CLEANING
USING MACHINE LEARNING TECHNIQUES TO FORECAST CUTTINGS REMOVAL IN HOLE CLEANING

PlumX Article Metrics by Elsevier
Dinara N. Delikesheva; Aizada B. Sharaouva
10.5593/sgem2024/1.1/s06.84
10.5593/sgem2024/1.1
1314-2704
978-619-7603-67-5
English
24
1.1
•    Prof. DSc. Oleksandr Trofymchuk, UKRAINE 
•    Prof. Dr. hab. oec. Baiba Rivza, LATVIA
6.Oil and Gas Exploration
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.
[1] 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. https://doi.org/10.2118/160845-MS
[2] Abdelhalim Kh., Al Zaabi M., Al Ali S., Abdel Karim I., Jadallah H., Al Beshr А., 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: https://doi.org/10.2118/202153-MS
[3] 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: https://doi.org/10.2118/182937-MS
[4] Mitchell J. Trouble-Free Drilling, third edition. – Houston: Drillbert Engineering, 2004. – P. 230.
[5] Egenti N.B. Understanding Drill-cuttings Transportation in Deviated and Horizontal Wells // Nigeria Annual International Conference and Exhibition. – Lagos, Nigeria, 2014.
[6] Malekzadeh N., Mohammadsalehi M. Hole Cleaning Optimization in Horizontal Wells: A New Method to Compensate Negative Hole Inclination Effects // Brasil Offshore. – Macae?, Brazil, 2011. https://doi.org/10.2118/143676MS.
[7] 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.
[8] 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
[9] 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: https://doi.org/10.2118/212912-PA
[10] Soofi A. A., Awan A. Classification techniques in machine learning: applications and issues // J. Basic Appl. Sci, 2017. – Vol. 13. – P. 459-465. https://doi.org/10.6000/1927-5129.2017.13.76.
[11] Kalyan Das. Jiming Jiang. J. N. K. Rao. "Mean squared error of empirical predictor." Ann. Statist. 32 (2) 818 - 840, April 2004. https://doi.org/10.1214/009053604000000201
The article was prepared within the framework of project No. AP13068658 “Developing torque and drag, hydraulic models to prevent stuck pipe by monitoring drilling parameters in real time” as part of the ongoing competition for grant funding for fundamental and applied research of young scientists in scientific and (or) scientific technical projects for 2022–2024 Ministry of Science and Higher Education of the Republic of Kazakhstan.
conference
https://doi.org/10.5593/sgem2024/1.1/s06.84
Download PDF
Proceedings of 24th International Multidisciplinary Scientific GeoConference SGEM 2024
24th International Multidisciplinary Scientific GeoConference SGEM 2024, 1 - 7 July, 2024
Proceedings Paper
STEF92 Technology
International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM
SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci and Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts and Letters; Acad Fine Arts Zagreb Croatia; Croatian Acad Sci and Arts; Acad Sci Moldova; Montenegrin Acad Sci and Arts; Georgian Acad Sci; Acad Fine Arts and Design Bratislava; Russian Acad Arts; Turkish Acad Sci.
677-684
1 - 7 July, 2024
website
9912
hole cleaning, cuttings removal, regression analysis, machine learning, pipe sticking

24th SGEM International Conference on Earth & Planetary Sciences


International GeoConference SGEM2024
28 June - 8 July, 2024 / Albena, Bulgaria

Read More		   

SGEM Vienna GREEN "Green Science for Green Life"


Extended Scientific Sessions SGEM Vienna GREEN
25 - 29 November, 2024 / Vienna, Austria

Read More
   

A scientific platform for Art-Inspired Scientists!


The Magical World Where Science meets Art
Vienna, Austria

Read More