>Magister Scientiae - MSc / This study has been conducted to accurately determine fluid saturation within Fula sub-basin reservoirs which is located at the Southern part of the Republic of Sudan. The area is regarded as Shaly Sand Reservoirs. Four deferent shaly sand lithofacies (A, B, C, D) have been identified. Using method based on the Artificial Neural Networks (ANN), the core surrounding facies, within Fula reservoirs were identified. An average shale volume of 0.126 within the studied reservoirs was determined using gamma ray and resistivity logs. While average porosity of 26.7% within the reservoirs was determined using density log and the average core grain density. An average water resistivity of 0.8 Ohm-m was estimated using Pickett plot method. While formation temperature was estimated using the gradient that constrained between surface and bottom hole temperature. Water saturation was determined using Archie model and four shaly sand empirical models, the calculation was constrained within each facies zone to specify a model for each facies, and another approach was used to obtain the water saturation based on Artificial Neural Networks. The net pay was identified for each reservoir by applying cut-offs on permeability 5 mD, porosity 16%, shale volume 0.33, and water saturation 0.65. The gross thickness of the reservoirs ranges from 7.62m to 19.85m and net pay intervals from 4.877m to 19.202m. The study succeeded in establishing water saturation model for the Fula sub-basin based on neural networking which was very consistent with the core data, and hence has been used for net pay determination.