Ultrasonic response of a brine-saturated reservoir rock during coupled stress and fluid perturbation during liquid-CO2 injection
Bibliographic record
Abstract
• Stress changes with and without fluid perturbation show distinct ultrasonic velocity responses. • Amplitude and velocity changes are strongly influenced by fluid composition and stress change respectively. • Shear wave amplitudes rise with pore fluid density even during inelastic deformation. • Patchy saturation delays velocity and amplitude response during fluid displacement. • Refines seismic monitoring protocol for safer CO 2 storage operations. CO 2 injection into porous sandstone reservoirs offers a promising pathway to curb anthropogenic carbon emissions, but poses risks of leakage and induced seismicity from stress perturbations and fault reactivation without meticulous monitoring. Here, we present a time-lapse monitoring approach based on laboratory measurements of ultrasonic Vp, Vs and corresponding peak amplitudes in critically stressed, partially saturated North Sea sandstones (porosity 9–23%). Our experiments show that Vp and Vs exhibit higher sensitivity (4–15%) to stress changes compared to fluid saturation changes (0.8–1%), whereas amplitudes are more responsive (30–500%) to saturation, showing staggered change when brine is displaced by CO 2 . Under pure stress perturbation, amplitude variations are smaller (10–50%). During elastic deformation, the Vp/Vs ratio decreases while the ratio of their corresponding amplitudes increases, underscoring the need for both P- and S-wave measurements. Velocity and amplitude changes are more pronounced in high-porosity rocks. In a critically stressed state (beyond yield/before failure), the rise in pore fluid density from CO 2 injection boosts shear wave amplitudes, offsetting attenuation from inelastic deformation. Knowing the pre-injection stress state enables these velocity and amplitude trends to serve as robust indicators of reservoir conditions during and after CO 2 injection. This cost-effective approach can be adapted to reservoir-scale monitoring and extends beyond CCS, supporting enhanced detection of stress and fluid-induced changes in subsurface formations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".