Applicability of ultrasonic measurements to monitor and forecast stress change in subsurface storage applications
Bibliographic record
Abstract
The global expansion of subsurface CO₂ and hydrogen storage, alongside geothermal energy development, offers promising pathways for gigaton-scale CO₂ abatement. However, fluid injections and associated thermal effects can significantly alter reservoir stress states, risking fault reactivation and compromising caprock integrity. Direct stress measurements in the subsurface remain technically challenging, particularly beyond the near-wellbore zone. This study investigates how stress-induced changes in ultrasonic P- and S-wave velocities and amplitudes can serve as early indicators of irreversible rock deformation. Using triaxial cyclic and failure experiments on core samples from offshore Netherlands (depths: 3.1–4.2 km; porosity: 8–23 %), we demonstrate that wave velocities and amplitudes increase with axial loading in the elastic regime but decline progressively following crack initiation—well before mechanical failure. This trend reversal provides a reliable sonic precursor to failure. We propose a field-applicable traffic-light monitoring framework using sonic parameters to infer stress changes during injection operations. The observed inverse relationships between porosity and both mechanical strength and sonic velocity, along with the porosity-dependent velocity enhancement under confinement, present a novel opportunity to develop constitutive geomechanical models directly from reservoir sonic logs. This work advances non-invasive stress monitoring approaches and provides engineering geologists with robust tools to improve safety and predictability in subsurface energy storage projects. Moreover, such techniques can also be translated to integrity monitoring for underground mines and engineered structures. • Sonic velocity shifts reveal early signs of rock damage before mechanical failure. • Sonic attributes drop post-yield point forecasting risks during fluid injection. • A crack density model is proposed, utilizing sonic log to assess reservoir condition. • A traffic-light framework proposed, ensuring safe fluid injections by flagging stress shifts. • Findings improve stress monitoring tools for energy storage and engineering safety.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".