Availability and high potential of wireline data to correlate with geochemical properties convinced petroleum geologists to use them for source rock detection and calculating their richness. In this research, intelligent techniques and mathematical relationships were used to evaluate source rock potential and organic matter type indirectly. Artificial neural networks (ANN) and ΔLogR techniques were successfully used to model the relation between wireline logs and Total Organic Carbon (TOC) content. Furthermore, wireline data and TOC values were used as input data to model a second ANN for S2 parameter (present hydrocarbon potential) calculation. Owing to its great ability in the course of solving non-linear problems with overwhelming complexity, the back propagation method was used to train the networks using 70 points datasets. Predicted TOC contents were validated by Rock-Eval pyrolysis results of which revealed dependency of the ΔLogR accuracy to the organic richness and lithology, and therefore higher precision of the ANN outputs in compare to the ΔLogR results. Hydrogen Index (HI) and kerogen type were also effectively predicted using mathematical relationship between TOC and S2 factors. Reliability of all steps of the methodology was approved using a 16 members retest dataset for which testing predicted parameters against measured data demonstrated good correlation coefficients combined with negligible errors. Finally, applicability of the methodology was checked by applying it on two wells in the Dezful Embayment to evaluate geochemical and depositional properties of the Paleocene-Early Oligocene Pabdeh Formation. © 2017