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.

关联被广泛使用，其重要性在大数据中变得更加明显。通常，科学家和工程师会寻找高度相关的变量，而忽略弱相关的变量。通过将相关性与因果推理相集成，我们在评估非常规资源时使用了更全面的相关性视图。在地层评估中，涉及许多变量，它们通常对油气藏有不同的影响，并且它们是相互关联的。这些现象经常导致因果相关变量之间的弱关联。我们表明，使用弱或中度相关性可以帮助识别易燃气岩相。这种方法与辨别统计现象有关，称为辛普森悖论。尽管这种悖论在文献中被解释为一种偏见，我们表明，它可以真正表示出弱关联，这是多种原因造成的。更重要的是，我们将证明，当基于物理定律进行因果推理时，有线测井数据的弱相关性和强相关性均可用于解码易发性有机泥岩，以评估和开发页岩气资源。