| Langmuir adsorption isotherm in unconventional resources: Applicability and limitations S Alafnan, A Awotunde, G Glatz, S Adjei, I Alrumaih, A Gowida Journal of Petroleum Science and Engineering 207, 109172, 2021 | 90 | 2021 |
| Unconfined compressive strength (UCS) prediction in real-time while drilling using artificial intelligence tools A Gowida, S Elkatatny, H Gamal Neural Computing and Applications 33 (13), 8043-8054, 2021 | 72 | 2021 |
| Data-Driven Framework to Predict the Rheological Properties of CaCl2 Brine-Based Drill-in Fluid Using Artificial Neural Network A Gowida, S Elkatatny, E Ramadan, A Abdulraheem Energies 12 (10), 1880, 2019 | 44 | 2019 |
| New computational artificial intelligence models for generating synthetic formation bulk density logs while drilling A Gowida, S Elkatatny, S Al-Afnan, A Abdulraheem Sustainability 12 (2), 686, 2020 | 35 | 2020 |
| A hybrid artificial intelligence model to predict the elastic behavior of sandstone rocks A Gowida, T Moussa, S Elkatatny, A Ali Sustainability 11 (19), 5283, 2019 | 33 | 2019 |
| Advancement of hydraulic fracture diagnostics in unconventional formations A Mahmoud, A Gowida, MS Aljawad, M Al-Ramadan, AF Ibrahim Geofluids 2021 (1), 4223858, 2021 | 31 | 2021 |
| Newly developed correlations to predict the rheological parameters of high-bentonite drilling fluid using neural networks A Gowida, S Elkatatny, K Abdelgawad, R Gajbhiye Sensors 20 (10), 2787, 2020 | 28 | 2020 |
| Machine learning application to predict in-situ stresses from logging data AF Ibrahim, A Gowida, A Ali, S Elkatatny Scientific Reports 11 (1), 23445, 2021 | 27 | 2021 |
| Application of artificial neural network to predict formation bulk density while drilling A Gowida, S Elkatatny, A Abdulraheem Petrophysics 60 (05), 660-674, 2019 | 25 | 2019 |
| Prediction of sonic wave transit times from drilling parameters while horizontal drilling in carbonate rocks using neural networks A Gowida, S Elkatatny Petrophysics 61 (05), 482-494, 2020 | 18 | 2020 |
| Cement evaluation challenges AH Gowida, Z Ahmad, S Elkatatny, M Mahmoud SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition, SPE …, 2018 | 17 | 2018 |
| Synthetic well-log generation: New approach to predict formation bulk density while drilling using neural networks and fuzzy logic A Gowida, S Elkatatny, A Abdulraheem, DA Shehri International Petroleum Technology Conference, D022S147R002, 2020 | 15 | 2020 |
| A hybrid data-driven solution to facilitate safe mud window prediction A Gowida, AF Ibrahim, S Elkatatny Scientific Reports 12 (1), 15773, 2022 | 8 | 2022 |
| The prediction of wellhead pressure for multiphase flow of vertical wells using artificial neural networks I Gomaa, A Gowida, S Elkatatny, A Abdulraheem Arabian Journal of Geosciences 14, 1-10, 2021 | 7 | 2021 |
| Real-time prediction of in-situ stresses while drilling using surface drilling parameters from gas reservoir AF Ibrahim, A Gowida, A Ali, S Elkatatny Journal of Natural Gas Science and Engineering 97, 104368, 2022 | 6 | 2022 |
| Comparative analysis between different artificial based models for predicting static Poisson’s ratio of sandstone formations A Gowida, S Elkatatny, T Moussa International petroleum technology conference, D032S220R002, 2020 | 6 | 2020 |
| Exploring the potential of laser technology in oil well drilling: An overview A Gowida, H Gamal, S Elkatatny Geoenergy Science and Engineering, 212278, 2023 | 5 | 2023 |
| New empirical correlations to estimate the least principal stresses using conventional logging data A Gowida, AF Ibrahim, S Elkatatny, A Ali ACS omega 7 (16), 13507-13519, 2022 | 5 | 2022 |
| Accelerated low-temperature oxidation for sand consolidation and production control AH Gowida, SA Abu-Khamsin, MA Mahmoud, MS Aljawad, SF Alafnan Journal of Petroleum Science and Engineering 214, 110567, 2022 | 3 | 2022 |
| Impact of Pressure and Temperature on Foam Behavior for Enhanced Underbalanced Drilling Operations A Gowida, S Elkatatny, AF Ibrahim, MS Kamal ACS omega 9 (1), 1042-1055, 2023 | 2 | 2023 |
Salaheldin ElkatatnyProfessor, King Fahd University of Petroleum & Minerals在 kfupm.edu.sa 的电子邮件经过验证
