Induced seismicity data prep: Automate data processing and data set production in Texas and New Mexico using Python and ArcGIS Pro tools

Author:

Breton Caroline1ORCID,Shensky Michael2ORCID,Savvaidis Alexandros3ORCID

Affiliation:

1. The University of Texas at Austin, John A. and Katherine G. Jackson School of Geosciences, Bureau of Economic Geology, Austin, Texas, USA. (corresponding author)

2. The University of Texas at Austin, University of Texas Libraries, Research Support & Digital Initiatives: Research Data Services, Austin, Texas, USA.

3. The University of Texas at Austin, John A. and Katherine G. Jackson School of Geosciences, Bureau of Economic Geology, Austin, Texas, USA.

Abstract

Causal factors of induced seismicity in the Permian Basin are investigated by collecting and processing data on reported earthquakes, hydraulic fracture operations, and salt water disposal (SWD). Data are collected from five online sources: (1) the TexNet Earthquake Catalog, which provides earthquake data for Texas; (2) the TexNet Injection Volume Reporting Tool, which provides daily SWD data for select Texas wells; (3) the FracFocus Chemical Disclosure Registry, which provides hydraulic fracture data to the public; and (4) B3 Insight and (5) IHS Enerdeq Browser, which are proprietary database services that provide current and historical well data through paid subscriptions. TexNet makes their data available to the public at dynamic map websites. We automate data processing and data management using Python and ArcGIS Pro tools. The workflow produces quick, reliable, consistent, and reproducible output. We developed a Python script for each collected data table to filter, select fields, and write a new table. We created ArcGIS Pro Model Builder models for each new table to control format properties at import to the geodatabase. Further models contain customized ArcToolbox tools arranged to run geospatial, quality assurance, and quality control processing steps. In addition to discussing the source data and general workflow, we also review the results of the automated data processing. To illustrate our method, we create areas of investigation around the 5.4 magnitude Coalson earthquake to collect and process available data to create maps, charts, and data products for use in subsequent analysis. We make our Python scripts available on GitHub.

Publisher

Society of Exploration Geophysicists

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