Determination of the Rate of Penetration by Robust Machine Learning Algorithms Based on Drilling Parameters

Seyed Vahid Alavi Nezhad Khalil Abad; Omid Hazbeh; Meysam Rajabi; Somayeh Tabasi; Sahar Lajmorak; Hamzeh Ghorbani; Ahmed E Radwan; Mohammad Mudabbir

doi:10.1021/acsomega.3c02364

Determination of the Rate of Penetration by Robust Machine Learning Algorithms Based on Drilling Parameters

ACS Omega. 2023 Nov 28;8(49):46390-46398. doi: 10.1021/acsomega.3c02364. eCollection 2023 Dec 12.

Authors

Seyed Vahid Alavi Nezhad Khalil Abad¹, Omid Hazbeh², Meysam Rajabi³, Somayeh Tabasi⁴, Sahar Lajmorak⁵, Hamzeh Ghorbani^{6

7}, Ahmed E Radwan⁸, Mohammad Mudabbir⁷

Affiliations

¹ Department of Civil Engineering, Birjand University of Technology, Birjand 97198 66981, Iran.
² Faculty of Earth Sciences, Shahid Chamran University, Ahwaz 6135743136, Iran.
³ Department of Mining Engineering, Birjand University of Technology, Birjand 97198 66981, Iran.
⁴ Faculty of Industry and Mining (Khash), University of Sistan and Baluchestan, Zahedan 1489684511, Iran.
⁵ Department of Earth Sciences, Institute for Advanced Studies in Basic Sciences (IASBS), 444 Prof. Yousef Sobouti Blvd., Zanjan 45137-66731, Iran.
⁶ Young Researchers and Elite Club, Ahvaz Branch, Islamic Azad University, Ahvaz 1477893855, Iran.
⁷ Doctoral School of Materials Science and TechnologiesObuda University, Budapest 1034, Hungary.
⁸ Faculty of Geography and Geology, Institute of Geological Sciences, Jagiellonian University, Gronostajowa 3a, Kraków 30-387, Poland.

Abstract

Underground resources, particularly hydrocarbons, are critical assets that promote economic development on a global scale. Drilling activities are necessary for the extraction and recovery of subsurface energy resources, and the rate of penetration (ROP) is one of the most important drilling parameters. This study forecasts the ROP using drilling data from three Iranian wells and hybrid LSSVM-GA/PSO algorithms. These algorithms were chosen due to their ability to reduce noise and increase accuracy despite the high level of noise present in the data. The study results revealed that the LSSVM-PSO method has an accuracy of roughly 97% and is more precise than the LSSVM-GA technique. The LSSVM-PSO algorithm also demonstrated improved accuracy in test data, with RMSE = 1.92 and R² = 0.9516. Furthermore, it was observed that the accuracy of the LSSVM-PSO model improves and degrades after the 50th iteration, whereas the accuracy of the LSSVM-GA algorithm remains constant after the 10th iteration. Notably, these algorithms are advantageous in decreasing data noise for drilling data.