Using Causal Inference in Field Development Optimization: Application to Unconventional Plays

In the current era of big data and machine learning, a strong focus exists on prediction and classification. In industrial applications, however, many important questions are not about prediction or classification; rather, they are causal: if I change A, what will happen to B? Traditional regression...

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Bibliographic Details
Published in:Mathematical geosciences 2020-07, Vol.52 (5), p.619-635
Main Authors: Bertoncello, Antoine, Oppenheim, Georges, Cordier, Philippe, Gourvénec, Sébastien, Mathieu, Jean-Philippe, Chaput, Eric, Kurth, Tobias
Format: Article
Language:English
Subjects:
Causality
Chemistry and Earth Sciences
Classification
Computer Science
Correlation
Data science
Earth and Environmental Science
Earth Sciences
Geotechnical Engineering & Applied Earth Sciences
Hydrogeology
Industrial applications
Learning algorithms
Machine learning
Optimization
Physics
Predictions
Statistical analysis
Statistics
Statistics for Engineering
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Summary:In the current era of big data and machine learning, a strong focus exists on prediction and classification. In industrial applications, however, many important questions are not about prediction or classification; rather, they are causal: if I change A, what will happen to B? Traditional regression techniques such as machine learning optimize predictions based on correlations seen in the data and are not robust tools for epidemiologists and biostatisticians when evaluating the efficacy of new treatments or medications using observational data. Therefore, a set of statistical tools have been developed to go beyond correlations and aim to make inferences about causal relationships between variables. The goal of the present work is to apply one of these statistical tools, propensity score matching, in the oil and gas context, which is a novel application of the method. Two case studies are presented, one on proppant type and the other on lateral length, to determine their respective impacts on productivity.
ISSN:1874-8961
1874-8953
DOI:10.1007/s11004-019-09847-z