A grid-search approach for 4D pressure-saturation discrimination
Bibliographic record
Abstract
ABSTRACT We develop a grid-search-based inversion method to discriminate between changes of pore pressure and water saturation. In this approach, we build a forward model by combining different rock-physics models and we predict grids of time-lapse attributes within a range of pressure and saturation. The time-lapse measurements from the data are matched with these predicted attributes to find the solution. We suggest using the independent time shifts and time-lapse attenuation to stabilize the inversion result. A synthetic example illustrates the advantages of the inversion workflow. The method helps to visualize how pressure and saturation change the elastic properties. This advantage is also useful in quality control and uncertainty analysis. We apply our method to a real underground blowout example in the North Sea area where gas has leaked into two sand layers at different conditions. We find that gas migrated according to the sand structure with the dipping layer leading to faster migration rates. The inversion results indicate that careful data preparation is crucial, not only to obtain stable inversion performance, but also to capture valuable information when interpreting the blowout development.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".