Combining periodic hydraulic tests and surface tilt measurements to explore in situ fracture hydromechanics
Bibliographic record
Abstract
Abstract Fractured bedrock reservoirs are of socio‐economical importance, as they may be used for storage or retrieval of fluids and energy. In particular, the hydromechanical behavior of fractures needs to be understood as it has implications on flow and governs stability issues (e.g., microseismicity). Laboratory, numerical, or field experiments have brought considerable insights to this topic. Nevertheless, in situ hydromechanical experiments are relatively uncommon, mainly because of technical and instrumental limitations. Here we present the early stage development and validation of a novel approach aiming at capturing the integrated hydromechanical behavior of natural fractures. It combines the use of surface tiltmeters to monitor the deformation associated with the periodic pressurization of fractures at depth in crystalline rocks. Periodic injection and withdrawal advantageously avoids mobilizing or extracting significant amounts of fluid, and it hinders any risk of reservoir failure. The oscillatory perturbation is intended to (1) facilitate the recognition of its signature in tilt measurements and (2) vary the hydraulic penetration depth in order to sample different volumes of the fractured bedrock around the inlet and thereby assess scale effects typical of fractured systems. By stacking tilt signals, we managed to recover small tilt amplitudes associated with pressure‐derived fracture deformation. Therewith, we distinguish differences in mechanical properties between the three tested fractures, but we show that tilt amplitudes are weakly dependent on pressure penetration depth. Using an elastic model, we obtain fracture stiffness estimates that are consistent with published data. Our results should encourage further improvement of the method.
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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 | 0.001 |
| 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 | 0.000 |
| 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".