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 |
