Integration of petrophysical and rock physics analysis to reservoir characterization in the Scarborough Field, Northwest Shelf, Australia - Ahmed Bakry; Qamar Yasin; Ashraf Ghoneimi; Muhammad Nabih (Journal of African Earth Sciences, 2026)

Robust prediction of advanced reservoir petrophysical parameters using conventional logs: Insights from XGB and neural network models - Ahmed Bakry; Ashraf Ghoneimi; Muhammad Nabih; Qamar Yasin; Mohamed Abd Elaziz; Ahmed Elgazar (Marine Geophysical Research, 2026)

Contribution of Fluid Substitution and Cheetah Optimizer Algorithm in Predicting Rock-Physics Parameters of Gas-Bearing Reservoirs in the Eastern Mediterranean Sea, Egypt - Mohamed Abd Elaziz; Ashraf Ghoneimi; Muhammad Nabih; Ahmed Bakry; Mohammed Azmi Al-Betar (Natural Resources Research, 2023)
In this study, the elastic characteristics of reservoir rocks and their relationship to porosity and pore fluid were predicted using the fluid substitution method in combination with machine learning techniques. We first discarded the data at gas points to remove the erroneous effect of gas on the prediction process of Poisson’s ratio using the three proposed machine learning models. Then, the prediction was carried out after substituting the gas zones by oil and by water. As a result, the prediction was enhanced and showed stronger correlation coefficient values. The integration of fluid substitution and machine learning methods was applied in the reservoir of Scarab field as a case study from the Eastern Mediterranean to detect the effect of different pore fluids (gas, oil, and water) on Poisson's ratio estimation. The main objective of the study was to analyze the seismic and well log data to estimate and predict the Poisson’s ratio in four fluid-content cases; these are gas-bearing reservoir, reservoir after removal of log data of gas-bearing zones, and reservoirs after gas-substitution with oil and with water. These four cases were dealt with directly and by using the machine learning algorithms based on the proposed model of random vector functional link (RVFL), which was enhanced by the Cheetah optimizer (CO). This study shows how the performance of RVFL is affected by the presence or absence of gas zones. It is shown that the Poisson’s ratio value increases when gas is substituted with water more than when gas is substituted with oil. For validation of these results, regression analysis technique was used and the correlation coefficient of the CO–RVFL model increased after removing well log data of gas zones and was more enhanced after fluid substitution from gas to oil or to water.

Rock physics analysis from predicted Poisson's ratio using RVFL based on Wild Geese Algorithm in scarab gas field in WDDM concession, Egypt - Muhammad Nabih; Ashraf Ghoneimi; Ahmed Bakry; Samia Allaoua Chelloug; Mohammed Azmi Al-Betar; Mohamed Abd Elaziz (Marine and Petroleum Geology, 2023)

Predicting Shale Volume from Seismic Traces Using Modified Random Vector Functional Link Based on Transient Search Optimization Model: A Case Study from Netherlands North Sea - Mohamed Abd Elaziz; Ashraf Ghoneimi; Ammar H. Elsheikh; Laith Abualigah; Ahmed Bakry; Muhammad Nabih (Natural Resources Research, 2022)

Reliability of velocity-deviation logs for shale content evaluation in clastic reservoirs: a case study, Egypt - Muhammad Nabih (Arabian Journal of Geosciences, 2021)

Fracture reopening by micro-earthquakes, a mechanism for oil seepage in mildly active rifts: a case study from Gemsa oilfield, the southern Gulf of Suez rift, Egypt - Shawky Sakran; Muhammad Nabih; Ahmed Henaish; Abdelmohsen Ziko (Arabian Journal of Geosciences, 2016)

Prediction of permeability anisotropy using Dar-Zarrouk parameters of deep resistivity logs: a case study in the northern Western Desert of Egypt - Mohamed Attwa; Muhammad Nabih (Arabian Journal of Geosciences, 2015)