Abstarct: Accurate knowledge from source rock geochemical parameters is necessary to predict the future performance of the oil field. One of this parameters is Total Organic Carbon (TOC). Therefore, improved modeling techniques are necessary to increase the accuracy in this parameter estimation. The main purpose of this study is identification of enriched zones of organic matters, which is done by creating a lixnk between petrophysical, geochemical evaluation and interpretation of seismic section. According to comprehensive covering of seismic data compared to logging data, is tried to applying and integrating different data including geochemical, logging and seismic Attributes to estimate TOC and investigating kind of TOC distribution in additions to wells and also intervals between them to detect shale laxyers with higher total organic carbon content. First with using common methods of geochemical evaluation in three wells in Persian Gulf region in Kazhdumi formation, geochemical potential¬, type of kerogen and product were analyzed and then TOC was estimated with △¬log¬R method and estimated values of this method were compared with Rock-Eval values. After this step, neural network training was done which network with MSE= 0.048 and correlation (R) about 82 percent compared to test data and 90 compared validation data, was as a good estimator and some were able to detect shale laxyers with higher total organic carbon content. Using well logs to examined formation lithology. Formation lithology obtained from petrophysical evaluation reviewed and approved by using associated cross plotsso with cross plots to identified laxyers with high organic carbon content .also using porosity and shale volume parameters obtained petrophysical evaluation. Finally, using corrected logs (invironmental correction), two-dimensional seismic data, appropriate seismic attributes were extracted around three wells, geochemical–seismic section of TOC was obtained that MLFN method with 75 percent compared to train data and 48 compared to validation data had the best performance and shale laxyers with higher organic carbon content were detected.