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Record W4415184650 · doi:10.1016/j.enggeo.2025.108421

Applicability of ultrasonic measurements to monitor and forecast stress change in subsurface storage applications

2025· article· en· W4415184650 on OpenAlex
Debanjan Chandra, Lujain Alghannam, Auke Barnhoorn

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEngineering Geology · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsnot available
FundersAlpha Cancer TechnologiesEuropean CommissionRijksdienst voor Ondernemend Nederland
KeywordsUltrasonic sensorSonic loggingGeomechanicsStress (linguistics)Geothermal gradientAmplitudeSubmarine pipelinePassive seismicPorosityCaprock

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.393

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.233
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it