Ras Fanar field is one of the largest oil-bearing carbonate reservoirs in the Gulf of Suez. The field produces from the Middle Miocene Nullipore carbonate reservoir, which consists mainly of algal-rich dolomite and dolomitic limestone rocks, and range in thickness between 400 and 980 ft. All porosity types within the Nullipore rocks have been modified by diagenetic processes such as dolomitization, leaching, and cementation; hence, the difficulty arise in the accurate determination of certain petrophysical parameters, such as porosity and permeability, using logging data only. In this study, artificial neural networks (ANN) are used to estimate and predict the most important petrophysical parameters of Nullipore reservoir based on well logging data and available core plug analyses. The different petrophysical parameters are first calculated from conventional logging and measured core analyses. It is found that pore spaces are uniform all over the reservoirs (17–23%), while hydrocarbon content constitutes more than 55% and represented mainly by oil with little saturations of secondary gasses. A regular regression analysis is carried out over the calculated and measured parameters, especially porosity and permeability. Fair to good correlation (R