A comparative study of estimating the oil generation window for evaluation and tracking the source rock potential by indirect methods

The limited availability of rock sample-based geochemical tests presents a challenge for source rock evaluation in areas where rock data are absent, requiring interpreters to extrapolate sparse datasets. In this study, indirect methods are used to bridge the gap between the field data in hydrocarbon exploration studies, focusing on the Jurassic-Cretaceous formations, in the Persian Gulf basin. Log-derived equations are utilized to obtained the onset of oil generation and thermal maturity. Then seismic data are used for prediction, modeling and extrapolation of thermal maturity as an important source rock property. Vitrinite Reflectance (VR%) is used as the main criteria of thermal maturity evaluation, estimating from well logs and then integrated with seismic attributes, using Probabilistic Neural Network analysis (PNN). Finally, seismic sections are produced from VR variation in the target source rock throughout the area. The outcome is evaluated and calibrated with comprehensive geochemical tests results and burial/thermal history models, indicating the highest compatibility. Utilizing seismic attributes for source rock interpretation is highly effective for obtaining more accurate results in less time. By this procedure, the need for geochemical data and the scope of sedimentological studies are reduced which are ultimately carried out in one field to compare with the obtained results.