Comment on "Correlation analysis of element contents and mechanical characteristics of shale reservoirs" by Liu et al. (2018). (June 2020)

• Record Type: Journal Article
• Authors: Zhang, Ye; Ma, Y.Z.; Gomez, Ernest
• Is Part Of: Marine and petroleum geology. Volume 116(2020)
• Publication Date: 2020-06
• Subjects: Submarine geology -- Periodicals; Petroleum -- Geology -- Periodicals; Géologie sous-marine -- Périodiques; Pétrole -- Géologie -- Périodiques; Petroleum -- Geology; Submarine geology; Periodicals; Electronic journals; 551.468
• DOI: 10.1016/j.marpetgeo.2019.06.004
• Languages: English
• ISSNs: 0264-8172

Abstract

Abstract: We found the article and conclusion by Liu et al. (2018) interesting, as the authors conducted a correlation analysis between element contents and rock mechanical characteristics of an organic rich shale in southern China. When analyzing data obtained from logging shale gas wells, non-parametric methods for calculating correlation coefficients were employed along with statistical significance tests that utilize confidence levels. The authors' conclusion suggests that mechanical properties cannot be solely determined based on the elemental composition of shale, while mineralogy (quartz vs. clay fractions) can explain some of the effects observed in their correlation analysis. In our opinion, a couple of points regarding the correlation analysis and the brittleness index, as introduced by Liu et al. (2018), were not clearly presented. Further elucidations are needed to clarify these points for the readers of this journal. Herein, we present a discussion on the two following issues: The first issue on correlation analysis impacts the second issue of constructing a mathematical model of brittleness index, and thus is slightly more extensively discussed. Highlights: Distinction the strength and significance of the correlation coefficients. The mathematical model of the brittleness index should be based on the correlation analysis of mineral elements. It is important to define the null hypothesis. It is important to understand Simpson's paradox in defining conditional correlations.