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

Abstract 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.

[1]  J. Aldrich.  Correlations Genuine and Spurious in Pearson and Yule , 1995 .

[2]  Y. Zee Ma,et al.  Uncertainty Analysis in Well-Log and Petrophysical Interpretations , 2011 .

[3]  S. Bryant,et al.  Pore structure of shale , 2015 .

[4]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[5]  N. Szabó,et al.  Extending the Application of a Shale Volume Estimation Formula Derived from Factor Analysis of Wireline Logging Data , 2013, Mathematical Geosciences.

[6]  Jon R. Kettenring,et al.  The Practice of Cluster Analysis , 2006, J. Classif..

[7]  Nanny Wermuth,et al.  Causal Inference and Statistical Fallacies , 2001 .

[8]  Peter J. Huber,et al.  Data Analysis: What Can Be Learned From the Past 50 Years , 2011 .

[9]  Q. Passey,et al.  Clay Estimation From Gr And Neutron -Density Porosity Logs , 1994 .

[10]  A. Sakhaee-Pour,et al.  Slippage in shale based on acyclic pore model , 2018, International Journal of Heat and Mass Transfer.

[11]  Ehsan Heidaryan,et al.  Flow modeling in shale gas reservoirs: A comprehensive review , 2020 .

[12]  Abhishek Sharma,et al.  Application of Data Science and Machine Learning for Well Completion Optimization , 2018 .

[13]  Zhichuan Niu,et al.  Experimental investigation of quantum Simpson’s paradox , 2013 .

[14]  Y. Z. Ma.  Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling , 2019 .

[15]  Peter Kaufman,et al.  Identifying Hydrocarbon Zones in Unconventional Formations by Discerning Simpson's Paradox , 2014 .

[16]  Y. Zee Ma,et al.  Uses and abuses in applying neural networks for predictions in hydrocarbon resource evaluation , 2015 .

[17]  Y. Z. Ma,et al.  Tight Gas Sandstone Reservoirs, Part 2: Petrophysical Analysis and Reservoir Modeling , 2016 .

[18]  W. Hoyle,et al.  Quantitative Interpretation of Thermal Neutron Decay Time Logs: Part I. Fundamentals and Techniques , 1971 .

[19]  Wenfeng Li,et al.  Fractal dimensions of shale , 2016 .

[20]  Viktor Mayer-Schnberger,et al.  Big Data: A Revolution That Will Transform How We Live, Work, and Think , 2013 .

[21]  J. Pearl.  Causality: Models, Reasoning and Inference , 2000 .