| A first attempt to predict reservoir porosity from advanced mud gas data F Anifowose, M Mezghani, S Badawood, J Ismail International Petroleum Technology Conference, D021S042R001, 2022 | 8 | 2022 |
| Contributions of machine learning to quantitative and real-time mud gas data analysis: A critical review F Anifowose, M Mezghani, S Badawood, J Ismail Applied Computing and Geosciences 16, 100095, 2022 | 5 | 2022 |
| A Field-Scale Real-Time Prediction of Reservoir Porosity from Advanced Mud Gas Data F Anifowose, M Mezghani, S Badawood, J Ismail SPE EuropEC-Europe Energy Conference featured at the 84th EAGE Annual …, 2023 | 1 | 2023 |
| Predicting Rock Properties from Formation Fluid Measurements: Examples, Challenges, and Future Possibilities FA Anifowose, MM Mezghani, V Torlov, SM Badawood SPE EOR Conference at Oil and Gas West Asia, D021S021R002, 2024 | | 2024 |
| Improved Reservoir Rock Porosity Prediction from Advanced Mud Gas Data F Anifowose, S Badawood International Petroleum Technology Conference, IPTC-23110-EA, 2024 | | 2024 |
| Real-time estimation of formation hydrocarbon mobility from mud gas data FA Anifowose, MM Mezghani, J Ismail, SM Badawood US Patent 11,867,604, 2024 | | 2024 |
| From Well to Field: Reservoir Rock Porosity Prediction from Advanced Mud Gas Data Using Machine Learning Methodology F Anifowose, M Mezghani, S Badawood, J Ismail SPE Middle East Oil and Gas Show and Conference, D011S033R004, 2023 | | 2023 |
| Should We Care About the Background Gas Effect on Reservoir Properties Prediction Using Machine Learning and Advanced Mud Gas Data? FA Anifowose, MM Mezghani, SM Badawood, J Ismail SPE Europec featured at EAGE Conference and Exhibition?, D041S010R002, 2022 | | 2022 |
| Applied Computing and Geosciences F Anifowose, M Mezghani, S Badawood, J Ismai | | |
