Publication Date:
2024-02-15
Description:
Hydraulic fracturing (HF) operations are widely associated with induced seismicity in the Western Canadian Sedimentary Basin. This study correlates injection parameters of 12,903 HF stages in the Kiskatinaw area in northeast British Columbia with an enhanced catalog containing 40,046 earthquakes using a supervised machine learning approach. It identifies relevant combinations of geological and operational parameters related to individual HF stages in efforts to decipher fault activation mechanisms. Our results suggest that stages targeting specific geological units (here, the Lower Montney formation) are more likely to induce an earthquake. Additional parameters positively correlated with earthquake likelihood include target formation thickness, injection volume, and completion date. Furthermore, the COVID‐19 lockdown may have reduced the potential cumulative effect of HF operations. Our results demonstrate the value of machine learning approaches for implementation as guidance tools that help facilitate safe development of unconventional energy technologies.
Description:
Plain Language Summary:
Hydraulic fracturing (HF), a technique used in unconventional energy production, increases rock permeability to enhance fluid movement. Its use has led to an unprecedented increase of associated earthquakes in the Western Canadian Sedimentary Basin in the last decade, among other regions. Numerous studies have investigated the relationship between induced earthquakes and HF operations, but the connection between specific geological and operational parameters and earthquake occurrence is only partly understood. Here, we use a supervised machine learning approach with publicly available injection data from the British Columbia Oil and Gas Commission to identify influential HF parameters for increasing the likelihood of a specific operation inducing an earthquake. We find that geological parameters, such as the target formation and its thickness, are most influential. A small number of operational parameters are also important, such as the injected fluid volume and the operation date. Our findings demonstrate an approach with the potential to develop tools to help enable the continued development of alternative energy technology. They also emphasize the need for public access to operational data to estimate and reduce the hazard and associated risk of induced seismicity.
Description:
Key Points:
We use supervised machine learning to investigate the relationship between hydraulic fracturing operation parameters and induced seismicity.
Geological properties and a limited number of operational parameters predominantly influence the probability of an induced earthquake.
The approach has the potential to guide detailed investigations of injection parameters critical for inducing earthquakes.
Description:
Deutsche Forschungsgemeinschaft
http://dx.doi.org/10.13039/501100001659
Description:
Gouvernement du Canada Natural Sciences and Engineering Research Council of Canada
http://dx.doi.org/10.13039/501100000038
Description:
https://doi.org/10.5281/zenodo.5501399
Description:
https://ds.iris.edu/gmap/XL
Description:
https://files.bcogc.ca/thinclient/
Description:
https://open.canada.ca/data/en/dataset/7f245e4d-76c2-4caa-951a-45d1d2051333
Description:
https://github.com/obspy/obspy
Description:
https://github.com/eqcorrscan/EQcorrscan
Description:
https://github.com/smousavi05/EQTransformer
Description:
https://github.com/Dal-mzhang/REAL
Description:
https://scikit-learn.org/stable/
Description:
https://docs.fast.ai/
Description:
https://xgboost.readthedocs.io/en/stable/
Description:
https://github.com/slundberg/shap
Description:
https://docs.generic-mapping-tools.org/latest/
Keywords:
ddc:551.22
;
induced seismicity
;
machine learning
;
hydraulic fracturing
Language:
English
Type:
doc-type:article
Permalink
