Human‐induced seismicity and large‐scale hydrocarbon production in the <scp>USA</scp> and <scp>C</scp>anada
Bibliographic record
Abstract
Abstract We compare current and historic seismicity rates in six States in the USA and three Provinces in Canada to past and present hydrocarbon production. All States/Provinces are major hydrocarbon producers. Our analyses span three to five decades depending on data availability. Total hydrocarbon production has significantly increased in the past few years in these regions. Increased production in most areas is due to large‐scale hydraulic fracturing and thus underground fluid injection. Furthermore, increased hydrocarbon production generally leads to increased water production, which must be treated, recycled, or disposed of underground. Increased fluid injection enhances the likelihood of fault reactivation, which may affect current seismicity rates. We find that increased seismicity in Oklahoma, likely due to salt‐water disposal, has an 85% correlation with oil production. Yet, the other areas do not display State/Province‐wide correlations between increased seismicity and production, despite 8–16‐fold increases in production in some States. However, in various cases, seismicity has locally increased. Multiple factors play an important role in determining the likelihood of anthropogenic activities influencing earthquake rates, including (i) the near‐surface tectonic background rate, (ii) the existence of critically stressed and favorably oriented faults, which must be hydraulically connected to injection wells, (iii) the orientation and magnitudes of the in situ stress field, combined with (iv) the injection volumes and implemented depletion strategies. A comparison with the seismic hazard maps for the USA and Canada shows that induced seismicity is less likely in areas with a lower hazard. The opposite, however, is not necessarily true.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".