Integration of well logs and seismic attributes for prediction of thermal maturity and TOC content in the Kazhdumi Formation (central Persian Gulf basin)

This study proposes an indirect method for estimating source rock properties, specifically thermal maturity and Total Organic Carbon (TOC), using well logs and seismic data within the central Persian Gulf hydrocarbon fields. The primary focus is on the Kazhdumi Formation, an important source rock in the southern Iran. The study is carried out in two distinct steps, based on the employed data. In the first step, a Maturity Index (MI) is calculated utilizing well logs (such as neutron, density and water saturation) within the Kazhdumi Formation across six oil and gas fields. Then, the TOC content is determined using Delta Log R method across four wells. Geochemical data, including vitrinite reflectance and Rock-Eval pyrolysis data, used as validators of the calculated MI and TOC. In the second step, 2-dimensional post-stack seismic data analysis is conducted where the necessary data, including a full set of well logs are accessible. Multi-attribute regression is employed to extract seismic attributes and consequently used to predict the target parameters. MI and TOC logs are then predicted through Probabilistic Neural Network analysis (PNN) which is used for the indirect prediction of the target logs. Finally, two seismic volumes are obtained to provide a mapping of the predicted MI and TOC variations in the Kazhdumi Formation. One advantage of using this method is its efficiency and cost effective compared to the geochemical testing. Additionally, it provides a continuous prediction of the target parameter in an extensive area. Lastly, this method benefits from the availability of seismic data compared to the geochemical data in hydrocarbon fields that are typically sparse. This approach is proved to be applicable to source rock analysis in different areas for indirect estimation of thermal maturity and TOC.