Seismicity Induced by the Development of Unconventional Oil and Gas Resources
Bibliographic record
Abstract
Resource development in unconventional oil and gas plays is sometimes accompanied by unintended earthquakes, known as induced seismicity. To date, the largest such induced events have been the September 2016 5.8 M W Pawnee earthquake in Oklahoma, and the December 2018 5.2 M W earthquake in the Sichuan Basin. These earthquakes were triggered by different industrial processes, namely saltwater disposal (Pawnee) and hydraulic fracturing (Sichuan Basin). Current models indicate that such induced earthquakes occur by activation of a pre-existing fault system due to some combination of increased pore pressure, a change in fault-loading conditions arising from poroelastic effects, or precursory slow fault slip. This chapter provides a tutorial and review of basic underlying principles of induced seismicity and an overview of regulatory measures, along with several current research themes including tools for screening risk and forecasting maximum magnitude. These concepts are illustrated by case studies from the USA and western Canada.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".