Alteration's Control on Frictional Behavior and the Depth of the Ductile Shear Zone in Geothermal Reservoirs in Volcanic Arcs
Bibliographic record
Abstract
Abstract The majority of geothermal energy is produced in tectonically active volcanic‐arc regions due to their high geothermal gradients. Reservoirs in these settings are often stratified with smectite/kaolinite‐, illite‐, and chlorite‐rich zones, in order of increasing depth and temperature. Eighteen andesitic core and surface samples were taken from five geothermal fields in the Lesser‐Antilles and Cascade volcanic arcs. The collected samples have experienced various degrees of alteration and can be considered, in their ensemble, to be representative of the previously mentioned alteration zones. The influence of the alteration was assessed through biaxial rate‐and‐state friction experiments on prepared gouge. The samples were each tested at 10, 30, and 50 MPa normal stress in both nominally dry and nominally wet conditions. While significant water‐induced frictional‐strength reduction was observed, phyllosilicate content dominates frictional behavior, with increased phyllosilicate content reducing frictional strength, promoting velocity‐strengthening behavior, and reducing frictional healing. Negative frictional healing is observed and likely related to the presence of expandable clays, leading to frictional weakness over long time periods. It is suggested that, by controlling frictional strength, phyllosilicate content influences the depth of onset of ductile shear zones, which often underlie these reservoirs and are critical for the horizontal advection and vertical sealing of geothermal fluid. Further, as these types of reservoirs are likely critically stressed, varying degrees of alteration within different reservoir zones can give rise to the formation of stress jumps. Overall, the frictional behavior depended to a first order on overall phyllosilicate content, potentially simplifying engineering studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".