Applying artificial intelligent and empirical techniques for prediction of total organic carbon content from wireline data: A case study from Pabdeh Formation in Mansuri oilfield, SW Iran

Abstract

Total organic carbon (TOC) content is one of the most crucial parameters needed for petroleum source rock evaluation. In this study, artificial neural network (ANN) and empirically derived ΔlogR techniques were used to estimate organic richness of the Paleocene-Oligocene Pabdeh source rock in the Mansuri oilfield (SW Iran) using the wireline data. The ANN model trained by the Levenberg-Marquardt back propagation algorithm was employed for prediction of TOC content from the responses of the sonic, neutron, density and spectral gamma-ray well logs. The linear regression analysis between TOC values measured by Rock-Eval analysis and the corresponding ones predicted by ΔlogR and ANN methods was performed to validate the performance of each method. The ANN model is capable of predicting TOC contents with higher coefficient of determination (R2) value (0.7830) compared to ΔlogR technique (0.5091). Correspondingly, the mean squared error (MSE) value for the constructed neural network (0.1647) is lower than ΔlogR method (0.2410). Overall, it can be concluded that the proposed ANN model provides more accurate qualitative and quantitative predictions of organic richness, and can be reliably applied for high-resolution estimation of TOC contained in the Pabdeh source rock throughout the Mansuri oilfield.