This work presents a method for rock porosity prediction from the X-ray computed tomography (CT) logs obtained using a double energy approach, bulk density (RHOB) and photoelectric factor (PEF). The proposed method seeks to correlate the known porosity from the Routine Core Analysis (RCAL) with RHOB and PEF high-resolution logs, as the response of these two measurements depends on the volumetric quantity of different rock materials and of the volume of its porous space. Artificial Neural Networks (ANNs) are
