Toc Determination of Gadvan Formation in South Pars Gas Field, Using Artificial Neural Network Technique

TOC Content in a source rock is potentially affecting logging data (density, sonic, neutron and<br>resistivity logs). Hence analyses on these logs assist to a reliable assessment of a source rock which is<br>quick and economically cheap method rather than direct geochemical analysis. A source rock interval<br>poses less density, low velocity, higher sonic porosity, high GR values and increase in resistivity. In<br>this research Gadvan Formation was studied in two boreholes as potential source rocks. The log data of<br>two wells were used to construct intelligent models in a source rock of the south Pars Gas field,<br>southwest of Iran. A suite of geophysical logs (neutron, density, sonic and resistivity Logs) and cutting<br>chips samples were used to determine TOC Content of this Formation. Rock- Eval pyrolysis data reveal<br>that Formation is poor source rock (less than 0.5%), whereas logging data and intelligent methods<br>calculations suggest the Gadvan Formation as poor source rock. Hence we attempt to correlate<br>between geophysical data and direct TOC content measurements using ΔLogR, Rock- Eval and neural<br>network techniques. The results showed that intelligent models were successful for prediction of TOC<br>content from conventional well log data. In the meanwhile, similar responses from different other<br>intelligent methods indicated their validity for solving complex problems.