Quantification of the probability of induced seismicity associated with large-scale underground hydrogen storage in Dutch salt formations
Bibliographic record
Abstract
Construction and cyclic operation of multi-cavern systems within salt pillars present notable geomechanical challenges, including subsidence due to cavern convergence, pressure interactions between caverns, leakage and induced seismicity. Monitoring stations in the northeast of the Netherlands have consistently reported small seismic events (local magnitudes ≥ -2), the underlying physics of which are poorly understood. As the operational activity in the salt domes is expected to scale up due to the prospects of underground hydrogen storage (UHS) in salt caverns, it is crucial to investigate the mechanisms underlying the observed seismic events. More importantly, it is essential to quantify the probability of induced seismic events due to the increase in UHS projects.This study aims to assess the probability of induced seismicity associated with the prospect of large-scale hydrogen storage (UHS) plans. To this end, it is crucial to understand, analyse, and quantify the mechanisms behind induced seismicity observed due to the salt cavern leaching and cyclic storage operations within the Dutch salt domes. As a necessary bench-mark step for our study, it is essential to explain the occurrence of small-scale events for the existing caverns. We commence by constructing simplified yet meaningful simulation models, which include the basic characteristics of the salt formation, salt cavern, operational conditions, and the presence of structural features in the salt formation as well as in the over- and side-burden. Subsequently, the deformation evolution of the system is quantified and the impact of uncertainties on stress and deformation is assessed. The simulation model will be coupled to a seismogenic source model to compute the spatio-temporal development of the seismic activity in the model due to the deformation evolution.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".