TOC content prediction based on a combined Gaussian process regression model

Abstract

Continuous total organic carbon (TOC) content estimation plays a crucial role in source rock reservoir evaluation. However, conventional prediction methods (e.g., empirical formulas or the δlogR method) have low prediction accuracy. Although machine learning algorithms possess a better prediction accuracy, they have also reached a bottleneck in this regard. Recently, ensemble learning and combined model prediction have achieved excellent results in many fields. In this study, a combined model was used for building a more accurate prediction model. Based on the theory that different formations correspond to different logging responses, we hypothesized that a Gaussian mixture model (GMM) classification of the logging data would be able to represent different geological conditions. Hence, we constructed independent Gaussian process regression (GPR) model based on the classified data; in particular, the posterior probability given by the GMM was used as a weight coefficient to combine the prediction results of all the independent GPR models. In addition, a confidence interval was obtained through the GPR to quantify the uncertainty of the prediction results. Finally, an error analysis showed that the combined-GPR model performed better than the δlogR method and other machine learning algorithms. In conclusion, the combined prediction model proposed in this study is reliable and effective, and it provides a new solution for quantitative prediction problems in corresponding fields.