Correlation-based data analytics of wireline logs for decoding and modeling shale gas resources

Correlation is widely used and its importance has become even more pronounced in big data. Generally, scientists and engineers look for highly correlated variables and ignore weakly correlated variables. We use a more holistic view of correlations in evaluating unconventional resources by integrating correlation with causal inference. In formation evalutions, many variables are involved and they generally have different effects on hydrocarbon accumulations, and they are inter-correlated. These phenomena often lead to weak correlations among causally related variables. We show that using weak or moderate correlations can help identify gas-prone lithofacies. This methodology is related to discerning a statistical phenomenon, termed Simpson's paradox. Although this paradox has been interpreted as a bias in the literature, we show that it can genuinely represent a weak correlation as an effect of multiple causes. More importantly, we will show that both a weak and strong correlation of wireline-log data can be used to decode gas-prone organic mudstones in evaluating and developing shale gas resources when the causal inference based on physical laws is invoked.