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Ahmed Alsaihati
Researcher at King Fahd University of Petroleum and Minerals
Publications - 17
Citations - 160
Ahmed Alsaihati is an academic researcher from King Fahd University of Petroleum and Minerals. The author has contributed to research in topics: Drilling & Engineering. The author has an hindex of 3, co-authored 13 publications receiving 36 citations.
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Use of Machine Learning and Data Analytics to Detect Downhole Abnormalities While Drilling Horizontal Wells, With Real Case Study
TL;DR: The development and evaluation of an intelligent system that could help the drilling crew to detect downhole abnormalities in real-time, react, and take corrective action to mitigate the problem promptly is detailed.
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Rock Strength Prediction in Real-Time While Drilling Employing Random Forest and Functional Network Techniques
TL;DR: The developed PCA-based RF and FN models provide an accurate UCS estimation in real-time from the drilling data, saving time and cost, and enhancing the well stability by generating UCS log from the rig drilling data.
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Rate of penetration prediction while drilling vertical complex lithology using an ensemble learning model
TL;DR: The results showed that the RF-meta model outperformed the base learners and Maurer's and Bingham's empirical models in predicting the ROP in Well-2 with a low absolute average percentage error (AAPE) and a high coefficient of determination (R2) of 0.94.
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An Overview of the Common Water-Based Formulations Used for Drilling Onshore Gas Wells in the Middle East
TL;DR: In this paper, the field practices were combined with the literature to study the most practiced water-based drilling fluid recipes used for onshore gas applications in the Middle East (i.e., spud mud, high-bentonite spud, salt/polymer mud, and high-overbalanced mud).
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Intelligent Prediction for Rock Porosity While Drilling Complex Lithology in Real Time.
TL;DR: In this paper, two intelligent models were developed utilizing the random forest (RF) and decision tree (DT) techniques to predict the rock porosity in real time while drilling complex lithology using machine learning.