A new and reliable dual model- and data-driven TOC prediction concept: A TOC logging evaluation method using multiple overlapping methods integrated with semi-supervised deep learning

Abstract

The total organic carbon content (TOC) is the most important parameter when determining the source rock quality. At present, there are two main types of TOC well logging calculation methods: model-driven and data-driven methods. However, research on the these two types of methods has slowed over the past ten years, and the ongoing research is very limited; thus, it is difficult to improve the TOC calculation accuracy. This paper analyses the advantages and disadvantages of current methods and proposes a new model- and data-driven TOC prediction concept to improve the TOC prediction accuracy. The proposed concept takes the curve obtained with the overlap method as an input instead of using a conventional log curve; we believe that deep learning is best suited to accomplish this task. We propose a new integrated semi-supervised deep learning network—the integrated deep semi-supervised ladder network (IDLN) algorithm—for small-sample well logging interpretation problems based on the proposed approach. A two-step method consisting of intelligent curve overlapping and automatic TOC prediction is employed to better interpret logging data and core data; this method can predict the TOC without manually overlapping curves. Testing the data from 12 wells in 3 blocks revealed that the proposed concept and IDLN algorithm greatly improve the TOC prediction accuracy, and the mean square error was reduced by more than 50%. The new method is also more reasonable for locating non-reservoir segments. Moreover, this novel approach can further enhance the TOC prediction accuracy and may even constitute a new research direction for improving the existing logging interpretation theory. These novel principles can ultimately help evaluate source rock reservoirs.