Which operational factors lead to seismogenic fluid-injections? observations from ten large-scale studies in North America
Bibliographic record
Abstract
We investigated for the first time the role wastewater disposal (SWD) and hydraulic fracturing (HF) played throughout North America, under the same statistical framework. We employed new earthquake catalogs, novel declustering techniques and established physics-based principles. Our datasets included 600’000 SWD wells, 219’000 HF stimulations and 93’000 earthquakes from Oklahoma, Kansas, Western Canada Sedimentary Basin, Delaware basin, Eagle Ford play, Midland basin, Fort Worth Basin, Raton basin, Arkansas. Following Grigoratos et al. (2020. 2022), we first hindcasted the seismicity rates on a spatial grid using either actual or randomized injection data as input. In the end, each block is confidence level for its causal link with either HF or SWD. We classified each event as tectonic or triggered by SWD or HF, employing the aforementioned confidence intervals, with some additional spatio-temporal well-to-earthquake association filters used for sanity checks. Post-processing the results, we identified which operational factors appear associated with recorded seismicity or elevated magnitudes. Some of our key findings are listed below: HF - a tiny percentage of stimulations is responsible for most of the seismicity - 90% of seismic triggering started during stimulation, thus no need for large time-lags - no correlation between detectable seismic potential and injection rate or total volume - no correlation between seismic potential and stimulation depth, even for larger magnitudes - fault-specific geomechanical conditions dominate across basins; for the EQ rates, the fluid volumes are important only within local sub-km scales SWD - The vast majority of seismogenic wells were <5 km away from the closet earthquake - the injection rate, total volume and distance-to-basement are crucial for seismogenic potential and rupture-size - the absolute well-depth does not affect the magnitude of the triggered seismicity - the Mmax is not correlated to the Seismogenic Index (Shapiro et al, 2010); thus, reactivating many small faults does not necessarily imply that significantly larger favorable faults are also nearby - the Mmax is much more correlated to the distributed volumes (via pore-pressure diffusion principles) than to “static” injected volumes; thus, the Theis equation is valid for large-scale diffusivity values between 0.3 and 2 m2/s.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".