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Eyad Abdullah Alshaye

King Fahd University of Petroleum & Minerals (KFUPM)

SCHOLARLY PAPERS

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Scholarly Papers (2)

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1.

Tree-Based Machine Learning Models for Predicting the Maximum Depth of Corrosion Defects Based on Historical In-Line Inspection Data

Number of pages: 28 Posted: 05 Feb 2025
Eyad Abdullah Alshaye, Atif AlZahrani, Abduljabar Al-Sayoud and Md Shafiullah
King Fahd University of Petroleum & Minerals (KFUPM), King Fahd University of Petroleum & Minerals (KFUPM), King Fahd University of Petroleum & Minerals (KFUPM) and King Fahd University of Petroleum & Minerals (KFUPM)
Downloads 84 (783,685)

Abstract:

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In-line inspection, machine learning, Supervised learning, Pipeline corrosion, Maximum depth prediction, Statistical analysis, Sustainability

2.

Supervised Machine Learning Models for Predicting the Maximum Depth of Corrosion Defects Based on Historical In-Line Inspection Data

Number of pages: 24 Posted: 27 Dec 2024
Eyad Abdullah Alshaye, Atif AlZahrani, Abduljabar Al-Sayoud and Md Shafiullah
King Fahd University of Petroleum & Minerals (KFUPM), King Fahd University of Petroleum & Minerals (KFUPM), King Fahd University of Petroleum & Minerals (KFUPM) and King Fahd University of Petroleum & Minerals (KFUPM)
Downloads 71 (867,774)

Abstract:

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In-line inspection, Machine Learning, supervised learning, Pipeline corrosion, Maximum depth prediction, Oil and Gas Industry.