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An outlier detection approach for water footprint assessments in shale formations: case Eagle Ford play (Texas)

Abstract

The increasing trend on water use for hydraulic fracturing (HF) in multiple plays across the U.S. has raised the need to improve the HF water management model. Such approaches require good-quality datasets, particularly in water-stressed regions. In this work, we presented a QA/QC framework for HF data using an outlier detection methodology based on five univariate techniques: two interquartile ranges at 95 and 90% (PCTL95, PCTL90), the median absolute deviation (MAD) and Z score with thresholds of two and three times the standard deviation (2STD, 3STD). The cleaning techniques were tested using multiple variables from two data sources centered on the Eagle Ford play (EFP), Texas, for the period 2011–2017. Results suggest that the PCTL95 and MAD techniques are the best choices to remove long-tailed statistical distributions of different variables, classifying the minimum number of records as outliers. Overall, outliers represent 13–23% of the total HF water volume in the EFP. In addition, outliers highly impacted minimum and maximum HF water use values (min–max range of 0–47 m3/m and 5.3–24.6 m3/m of frac length, before and after the outlier removal process, respectively), that are frequently used as a proxy to develop future water–energy scenarios in early-stage plays. The data and framework presented here can be extended to other plays to improve water footprint estimates with similar conditions.

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Acknowledgements

Saúl Arciniega-Esparza was supported by the CONACYT graduate scholarship program. Antonio Hernández-Espriú acknowledges financial support provided by the COMEXUS Fulbright-García Robles Fellowship, the UNAM-DGAPA PASPA, and the Matías-Romero (SRE-UT LLILAS) Research Visiting Programs, which supported this research during his sabbatical leave at the Bureau of Economic Geology (UT Austin). The authors are grateful to IHS Enerdeq for granting them access to their database. They truly thank Bridget Scanlon, Brad Wolaver, Robert Reedy, and Casee Lemons for their technical advices during the databases analysis. Finally, the authors are grateful for the constructive comments from Editor-in-Chief James W. LaMoreaux as well as two anonymous reviewers who provided feedback that greatly improved the manuscript.

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All the authors contributed to the study conception and design. The material preparation and data collection were made by Saúl Arciniega-Esparza and Antonio Hernández-Espriú. Analysis was performed by Saúl Arciniega-Esparza, and statistical results were discussed and reviewed by Antonio Hernández-Espriú and Michael H. Young. The first draft manuscript was written by Saúl Arciniega-Esparza and Agustín Breña-Naranjo, and all the authors commented on previous versions of the manuscript. Adrián Pedrozo-Acuña and Agustín Breña-Naranjo proposed alternative methods that were implemented in this study. Antonio Hernández-Esrpiú and Michael H. Young critically revised the work and all the authors approved the final manuscript.

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Correspondence to Antonio Hernández-Espriú.

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Arciniega-Esparza, S., Hernández-Espriú, A., Breña-Naranjo, J.A. et al. An outlier detection approach for water footprint assessments in shale formations: case Eagle Ford play (Texas). Environ Earth Sci 79, 454 (2020). https://doi.org/10.1007/s12665-020-09197-8

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  • DOI: https://doi.org/10.1007/s12665-020-09197-8

Keywords

  • Outliers
  • Geospatial analysis
  • Water use
  • Hydraulic fracturing
  • Eagle Ford
  • Shale gas