Abdoljavad Asakere
Mosayyeb Kamari
Mohamad Abdide
Ali Erfani-neya

Reservoir rock lithology and fluids contacts can be determined by petrophysical logs. This determination uncertainty leds to uncertainty in hydrocarbon inplace estimation. The objective of this paper is introducing a new approaches for this purpose by artificial neural networks as one of the artificial intelligence technique. Estimation of hydrocarbon saturation and detection of these zone was done by this technique.For this purpose, after data preprocessing and applying bad hole models, the investigations be done by four different types of artificial neural networks (MLP, RBF, LSSVM, CMIS) and petrophysical fullset logs (Rt, DT, RHOB, PEF, NPHI, GR, CALI). The results correlation coefficient for this type of ANN were 0.995, 0.995, 0.992 and 0.997 respectively. Compare with similar research by Discriminant Analysis with correlation coefficient of 92.2, this networks give the better outputs.

Gas zones, Gas saturation percent, Petrophysical logs, Artificial Neural Networks (ANN)