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Abdulazeez Abdulraheem
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Proceedings Papers

Real-Time Rate of Penetration Prediction for Motorized Bottom Hole Assembly Using Artificial Intelligence

Amir Shokry Youssef, Salaheldin Elkatatny, Abdulazeez Abdulraheem, Idham Rahmatullah
Paper presented at the 58th U.S. Rock Mechanics/Geomechanics Symposium, June 23–26, 2024
Paper Number: ARMA-2024-1059
https://doi.org/10.56952/ARMA-2024-1059
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View Paper titled, Real-Time Rate of Penetration Prediction for Motorized Bottom Hole Assembly Using Artificial Intelligence
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Proceedings Papers

Drilling Data Based Approach for Real-Time Gamma Ray Prediction in Surface Holes Using Artificial Intelligence

Amir Shokry Youssef, Salaheldin Elkatatny, Abdulazeez Abdulraheem
Paper presented at the 58th U.S. Rock Mechanics/Geomechanics Symposium, June 23–26, 2024
Paper Number: ARMA-2024-1057
https://doi.org/10.56952/ARMA-2024-1057
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View Paper titled, Drilling Data Based Approach for Real-Time Gamma Ray Prediction in Surface Holes Using Artificial Intelligence
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Proceedings Papers

Drilling Data Based Approach for Real-Time Rate of Penetration Prediction for Motorized Bottom Hole Assembly Using Artificial Intelligence

Amir Shokry Youssef, Ahmed Gowida, Salaheldin Elkatatny, Ahmed Farid Ibrahim, Abdulazeez Abdulraheem
Paper presented at the GOTECH, May 7–9, 2024
Paper Number: SPE-219368-MS
https://doi.org/10.2118/219368-MS
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View Paper titled, Drilling Data Based Approach for Real-Time Rate of Penetration Prediction for Motorized Bottom Hole Assembly Using Artificial Intelligence
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Proceedings Papers

Automating Prediction of the Formation Tops and Lithology Changes in Real-Time While Drilling Using Artificial Neural Networks

Ahmed Abdulhamid Mahmoud, Salaheldin Elkatatny, Abdulazeez Abdulraheem
Paper presented at the Offshore Technology Conference, May 6–9, 2024
Paper Number: OTC-35042-MS
https://doi.org/10.4043/35042-MS
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View Paper titled, Automating Prediction of the Formation Tops and Lithology Changes in Real-Time While Drilling Using Artificial Neural Networks
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Proceedings Papers

Functional Neural Networks Model for Prediction of the Formation Tops in Real-Time While Drilling

Ahmed Abdulhamid Mahmoud, Salaheldin Elkatatny, Abdulazeez Abdulraheem, Ahmed Gowida
Paper presented at the SPE Conference at Oman Petroleum & Energy Show, April 22–24, 2024
Paper Number: SPE-218537-MS
https://doi.org/10.2118/218537-MS
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View Paper titled, Functional Neural Networks Model for Prediction of the Formation Tops in Real-Time While Drilling
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Proceedings Papers

Big Data Analysis Using Unsupervised Machine Learning: K-means Clustering and Isolation Forest Models for Efficient Anomaly Detection and Removal in Complex Lithologies

Aneeq Nasir Janjua, Abdulazeez Abdulraheem, Zeeshan Tariq
Paper presented at the International Petroleum Technology Conference, February 12, 2024
Paper Number: IPTC-23580-EA
https://doi.org/10.2523/IPTC-23580-EA
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View Paper titled, Big Data Analysis Using Unsupervised Machine Learning: K-means Clustering and Isolation Forest Models for Efficient Anomaly Detection and Removal in Complex Lithologies
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Journal Articles

Enhancing Fracture Conductivity in Soft Chalk Formations With Diammonium Phosphate Treatment: A Study at High Temperature, Pressure, and Stresses

Mahmoud Desouky, Murtada Saleh Aljawad, Amao Abduljamiu, Theis Solling, Abdulazeez Abdulraheem, Murtadha J. AlTammar, Khalid M. Alruwaili
Journal: SPE Journal
Publisher: Society of Petroleum Engineers (SPE)
SPE J. 28 (06): 3280–3290.
Paper Number: SPE-215857-PA
https://doi.org/10.2118/215857-PA
Published: 13 December 2023
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View article titled, Enhancing Fracture Conductivity in Soft Chalk Formations With Diammonium Phosphate Treatment: A Study at High Temperature, Pressure, and Stresses
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Proceedings Papers

A Comparative Study Between Artificial Intelligence Models and a Statistical Learning Model to Predict Gas Adsorption in Shales

Moustafa Aly, Abdulazeez Abdulraheem, Taha Moawad
Paper presented at the SPE Canadian Energy Technology Conference and Exhibition, March 15–16, 2023
Paper Number: SPE-212742-MS
https://doi.org/10.2118/212742-MS
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View Paper titled, A Comparative Study Between Artificial Intelligence Models and a Statistical Learning Model to Predict Gas Adsorption in Shales
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Proceedings Papers

Drilling Data-Based Approach for Pressure Gradient Estimation Using Random Forest

Ahmed Abdelaal, Salaheldin Elkatatny, Abdulazeez Abdulraheem
Paper presented at the International Geomechanics Symposium, November 7–10, 2022
Paper Number: ARMA-IGS-2022-164
https://doi.org/10.56952/IGS-2022-164
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View Paper titled, Drilling Data-Based Approach for Pressure Gradient Estimation Using Random Forest
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Proceedings Papers

Prediction of Dew Point Pressure for High-Pressure Gas Reservoirs Using Artificial Intelligence Techniques

Amjed Hassan, Mohamed Mahmoud, Abdulazeez Abdulraheem
Paper presented at the ADIPEC, October 31–November 3, 2022
Paper Number: SPE-211064-MS
https://doi.org/10.2118/211064-MS
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View Paper titled, Prediction of Dew Point Pressure for High-Pressure Gas Reservoirs Using Artificial Intelligence Techniques
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Proceedings Papers

Artificial Intelligence Approach for Predicting the Shale Brittleness Index - A Middle East Basin Case Study

Ayyaz Mustafa, Zeeshan Tariq, Mohamed A. Mahmoud, Abdulazeez Abdulraheem
Paper presented at the SPE EuropEC - Europe Energy Conference featured at the 83rd EAGE Annual Conference & Exhibition, June 6–9, 2022
Paper Number: SPE-209707-MS
https://doi.org/10.2118/209707-MS
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View Paper titled, Artificial Intelligence Approach for Predicting the Shale Brittleness Index - A Middle East Basin Case Study
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Proceedings Papers

Integration of Formation and Drilling Parameters to Generate a Deterministic ROP Model

Mohammad Rasheed Khan, Ahmed Sadeed, Shams Kalam, Sidqi Abu-Khamsin, Abdulazeez Abdulraheem, Rizwan Ahmed Khan, Asiya Abbasi
Paper presented at the Offshore Technology Conference, May 2–5, 2022
Paper Number: OTC-32111-MS
https://doi.org/10.4043/32111-MS
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View Paper titled, Integration of Formation and Drilling Parameters to Generate a Deterministic ROP Model
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Proceedings Papers

Using Data-Mining Crisp-DM Methodology to Predict Drilling Troubles In Real-time

Salem Gharbi, Abdul Azeez Al Majed, Abdulazeez Abdulraheem, Shirish Patil, Salaheldin Elkatatny
Paper presented at the SPE Conference at Oman Petroleum & Energy Show, March 21–23, 2022
Paper Number: SPE-200105-MS
https://doi.org/10.2118/200105-MS
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View Paper titled, Using Data-Mining Crisp-DM Methodology to Predict Drilling Troubles In Real-time
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Proceedings Papers

What is the Best Artificial Intelligent Technology to Solve Drilling Challenges?

Salem Gharbi, Abdul Azeez Al Majed, Abdulazeez Abdulraheem, Shirish Patil, Salaheldin Elkatatny
Paper presented at the SPE Conference at Oman Petroleum & Energy Show, March 21–23, 2022
Paper Number: SPE-200183-MS
https://doi.org/10.2118/200183-MS
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View Paper titled, What is the Best Artificial Intelligent Technology to Solve Drilling Challenges?
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Proceedings Papers

A New Approach to Quantify the Wellhead Performance for Gas Condensate Reservoirs Using Artificial Intelligent Techniques

Mohammed Eliebid, Amjed Hassan, Mohamed Mahmoud, Abdulazeez Abdulraheem
Paper presented at the International Petroleum Technology Conference, February 21–23, 2022
Paper Number: IPTC-22116-MS
https://doi.org/10.2523/IPTC-22116-MS
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View Paper titled, A New Approach to Quantify the Wellhead Performance for Gas Condensate Reservoirs Using Artificial Intelligent Techniques
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Proceedings Papers

Permeability Prediction from Clay Volume Using Artificial Intelligence

Hussain A. Alhamood, Abdulazeez Abdulraheem
Paper presented at the International Petroleum Technology Conference, February 21–23, 2022
Paper Number: IPTC-22521-EA
https://doi.org/10.2523/IPTC-22521-EA
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View Paper titled, Permeability Prediction from Clay Volume Using Artificial Intelligence
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Proceedings Papers

A Data Driven Machine Learning Approach to Predict the Nuclear Magnetic Resonance Porosity of the Carbonate Reservoir

Ayyaz Ayyaz Mustafa, Zeeshan Zeeshan Tariq, Mohamed Mohamed Mahmoud, Abdulazeez Abdulraheem
Paper presented at the International Petroleum Technology Conference, February 21–23, 2022
Paper Number: IPTC-22081-MS
https://doi.org/10.2523/IPTC-22081-MS
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View Paper titled, A Data Driven Machine Learning Approach to Predict the Nuclear Magnetic Resonance Porosity of the Carbonate Reservoir
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Proceedings Papers

Emerging Techniques in Measuring Capillary Pressure and Permeability Using NMR and AI

Ahmed Ghamdi, Abubakar Isah, Mahmoud Elsayed, Kareem Garadi, Abdulazeez Abdulraheem
Paper presented at the SPE Middle East Oil & Gas Show and Conference, November 28–December 1, 2021
Paper Number: SPE-204633-MS
https://doi.org/10.2118/204633-MS
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View Paper titled, Emerging Techniques in Measuring Capillary Pressure and Permeability Using NMR and AI
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Proceedings Papers

Development of Novel Shale Swelling Inhibitors Using Hydrophobic Ionic Liquids and Gemini Surfactants for Water-Based Drilling Fluids

Rizwan Ahmed Khan, Mobeen Murtaza, Hafiz Mudaser Ahmad, Abdulazeez Abdulraheem, Muhammad Shahzad Kamal, Mohamed Mahmoud
Paper presented at the SPE Middle East Oil & Gas Show and Conference, November 28–December 1, 2021
Paper Number: SPE-204740-MS
https://doi.org/10.2118/204740-MS
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View Paper titled, Development of Novel Shale Swelling Inhibitors Using Hydrophobic Ionic Liquids and Gemini Surfactants for Water-Based Drilling Fluids
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Proceedings Papers

Development of Machine Learning Based Propped Fracture Conductivity Correlations in Shale Formations

Mahmoud Desouky, Zeeshan Tariq, Murtada Al jawad, Hamed Alhoori, Mohamed Mahmoud, Abdulazeez Abdulraheem
Paper presented at the SPE Middle East Oil & Gas Show and Conference, November 28–December 1, 2021
Paper Number: SPE-204606-MS
https://doi.org/10.2118/204606-MS
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View Paper titled, Development of Machine Learning Based Propped Fracture Conductivity Correlations in Shale Formations
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