Detailing the relationship between hydraulic fracturing parameters and induced seismicity using small-magnitude earthquakes 
Notice bibliographique
Résumé
<p>The extensive development over the last decade of low-permeability tight shale formations in the Western Canada Sedimentary Basin (WCSB) using hydraulic fracturing (HF) techniques for oil and gas exploration has been associated with  an increasing number of M3+ earthquakes (e.g., M<sub>L</sub> 4.5 on 30 November 2018 near Dawson Creek, and a M<sub>w </sub>4.6 on 17 August 2016 near Fort St. John). Avoiding economic losses due to operational shutdowns and mitigating damage caused by ground shaking requires developing quantitative relationships between operational parameters and the rate of fault activation in areas of low historical seismicity rates such as the WCSB.</p><p>Here we present the first results of a detailed study of the relationship between earthquake occurrence and operational parameters using dense seismic array and the British Columbia Oil and Gas Commission operational database to quantitatively assess the relative influence of operational parameters and geological conditions on earthquake generation. We first enhance a local, automatically generated seismic catalog of > 8000 events in the Kiskatinaw (Montney Formation) in the time period between July 2017 -  December 2020 area using a multi-station matched-filter approach.  We then use a machine learning picker as an independent detection algorithm for the same time period and retain events with the best initial locations detected by both the matched-filter and machine-learning approaches. The combined approach leads to  > 30,000 additional earthquakes, which we relocate using a double-difference technique, lowering the magnitude of completeness M<sub>c</sub> from ~1.3 to ~0.2.</p><p>As shown by several previous studies, while most earthquakes show a clear spatio-temporal correlation with HF operations, the majority of HF operations are not associated with felt earthquakes (e.g., M3+). To investigate the correlation between individual HF stage stimulation and earthquake occurrence, we correlate operational and geological characteristics with > 13000 HF stages. Geological data consists of the target formation for injection, which consists of either the Lower or Upper Montney Formations for the majority of stages. We then use a gradient-boosted decision tree machine learning algorithm combined with an approach to explain the model predictions to assess whether a specific stage is seismogenic. The decision-tree-algorithm allows us to estimate the importance of each injection parameter for the generation of seismicity. First results show that the target formation is the most influential parameter, where the Lower Montney Formation is more prone to higher rates of seismicity. In addition, the total pumped fluid volume and the maximum treating pressure are the important injection parameters that are positively correlated with seismicity. In contrast, the average injection rate and breakdown pressure may be relatively less influencial. We will present the results for specific stages and discuss the importance of their injection parameters in relation to seismicity. Our results could help to determine why only some HF wells are seismogenic.</p>
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».