Global review of induced earthquakes in oil and gas production fields
Bibliographic record
Abstract
Human-induced earthquakes have a long history, which could be caused by impoundment of reservoirs, underground mining, geothermal operations, oil and gas production, and wastewater injection. Analyses of seismicity and other monitoring data suggest: (1) rapid increase of seismicity during 2009～2015 in Oklahoma, USA was mainly due to deep disposal of wastewater from oil and gas extraction; (2) sharp increase of seismicity during 2009～2015 in Alberta, Canada, shows strong spatial and temporal correlation with hydraulic fracturing activities, and the conclusion is that hydraulic fracturing induced earthquakes (M>3.0); (3) In the Groningen gas field in Netherlands, studies revealed strong correlation between induced seismicity and gas depletion starting from 1991 and identified a close link between induced seismicity and reservoir compaction resulting from extraction of gas. The conclusion is that the gas production induced the earthquake (ML3.6) in 2012, which significantly caused damage in the local area; (4) In Sichuan shale gas production field, China, studies suggest that both the long-term injection of wastewater and short-term shale gas hydraulic fracturing correlate with the increasing seismicity, there have been different views on the cause of sizable earthquakes (ML5.7, 5.3) occurring in late 2018 and early 2019 in the shale gas production area. Studying induced seismicity due to industry activities has been one of the major efforts and become a research field in the scientific community. Besides revealing spatial and temporal distributions of induced seismicity, this review summarizes analyses of induced earthquakes from seismology, geomechanics, and structural geology. Developing solutions to the mitigation of earthquake risk during industry activities has been another major effort. This review introduces various theoretical and practical approaches to address the subject, including induced seismicity monitoring and management system, probability estimation methods, and risk assessment using geophysical and geological data, and also presents suggestions to address the issues in China.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".