Well‐seismic bandwidth and time‐lapse seismic characterization related to CO <sub>2</sub> injection and fluid substitution: Physical considerations
Bibliographic record
Abstract
Reservoirs are commonly heterogeneous. Injection of CO2 (or other fluids) related to enhanced oil recovery (EOR) operations may cause strong lateral and depth-dependent changes of heterogeneity both within the reservoir and in the surrounding formations. A seismic signal propagating through the reservoir and the surrounding formations before and after the injection undergoes different velocity dispersion and amplitude attenuation, which result in time shifts and waveform distortion. This paper discusses the physical aspects of well log integration with seismic and time-lapse seismic characterization based on a thinly layered model (1D heterogeneity). The results show that discrete layering and interval multiple reflections (or scattering) within sedimentary sequences have a significant influence on synthetic seismograms. The velocity and density perturbations inside and outside the reservoir will mainly result in the time-lapse amplitude anomaly at the top of the reservoir and the local coda wave distortion from near the top of the reservoir to the strong basal reflection below the reservoir (BBR). The distortion of the coda wave is highly dependent on the magnitudes of medium perturbations. Large perturbations may cause time a sag for the basal reflection as well as later events, which mainly include primary reflections. Those results have important implications for time-lapse seismic monitoring.
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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 | 0.001 |
| 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".