Application of extreme learning machine and neural networks in total organic carbon content prediction in organic shale with wire line logs

Total organic carbon (TOC) is a critical parameter for source rock characterization in shale gas reservoirs. In this work, the use of extreme learning machines (ELM) for predicting TOC from well logs data have been investigated. We use log data from two wells located in an unconventional shale gas reservoir in the Sichuan Basin, China. Seven wireline logs from this well and a total of 185 TOC observations from core measurements were incorporated. Prediction accuracy of the model has been evaluated and compared with commonly used artificial neural network which is based on Levenberg-Marquardt logarithm (ANN-LM). An Extreme Learning Machine (ELM) network is a single hidden-layer feed-forward network with many advantages over multi-layer networks, such as fast computing speed and better generalization performance. The results demonstrated that TOC prediction by the ELM model and the ANN model, but the ELM method can achieve high accuracy while maintains high running speed. This study shows that ELM technology is a promising tool for TOC prediction, and this work can be incorporated into a software system that can be used in quick 'sweet spot' determination and well completion guidance.