Using poro-viscoelastic forward modeling to reduce exploration risks in frontier basins
Bibliographic record
Abstract
To reduce the risk in exploration investments in the Canadian Arctic, a poro-viscoelastic (PVE) forward modeling scheme is tested using seismic and well log data collected during the initial round of exploration that took place between the late 1960s and the early 1980s. The synthetic seismograms are modeled with a 2-D implementation of the PVE formalism. The PVE modeling is tested form different cases such as a gas and oil reservoirs and an igneous intrusion, the latest representing an exploration risk in the Canadian Arctic. Modeling results are discussed in terms of wavefront propagation and acoustic and PVE zero-offset reflections in both the time and the frequency domains. Time-domain observations show that best ties with seismic data are achieved by the PVE modeling in the case of the gas reservoir whereas for the intrusion acoustic and PVE modeling ties are almost equivalent. Frequency-domain analysis indicates that the key reflections have slightly higher frequency content as opposed to what is observed on the seismic traces at the well locations. Finally, wavefield imaging shed light on the characteristics of the reflections of brine-, gas- and oil-filled reservoirs and igneous intrusions as changes in amplitude are mostly attributed to the tuning effect.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".