The Effect of Correlated Permeability on Fluid‐Induced Seismicity
Bibliographic record
Abstract
Abstract One of the challenges associated with subsurface high‐pressure fluid injections is the estimation of the seismic hazard and its spatial footprint. Field data have shown that the spatial footprint typically varies significantly between injections into the basement and injections above basement. Here, we show that varying degrees of spatial correlations in porosity or log(permeability) can explain this observation. Using high‐resolution well‐log data, we first show that porosity within the basement tends to follow a power‐law scaling, S(k) ∼ 1/kβ, with β ≈ 0.9, while above basement β > 1.4. Using this in a novel conceptual model, we show that β controls the spatial footprint of fluid‐induced seismicity such that large values of β lead to more seismic activity at large distances and a higher variability in the spatio‐temporal migration of seismic events, hence, explaining the field observations. Our findings indicate that correlations in log(permeability) need to be incorporated in the seismic hazard assessment.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".