Bibliographic record
Abstract
ABSTRACT Calibrating the 4D signal at the well with information obtained from production data is essential for it to be used quantitatively. We have developed a model-based inversion method to estimate the changes of elastic parameters in the reservoir due to production at the well. Our scheme is based on the observation that flow behavior is constrained by the dynamic properties of the layer (i.e., permeability), and, therefore, a layered model should be used to parameterize the inversion. The inversion scheme considers traveltime (inside and below the reservoir, but not in the overburden) and impedance effects implied by the change of elastic parameters (inside the reservoir). Therefore, even at zero offset, we can separate changes in density from changes in P-velocity. When using multiple offset data, we can use an exact formulation for the reflectivity if the base logs (density, P-velocity, and S-velocity) are available, otherwise, an approximation to the exact form can be used. Theoretical and practical analyses have shown that P-velocity is the best resolved parameter followed by density and, finally, S-velocity. Compared to classical data-driven inversion, our procedure introduces fewer artifacts and is less sensitive to tuning because the layered model parameterization introduces the missing low and high frequencies (although the seismic bandwidth plays an essential role in the resolution). This 4D inversion at the well is part of a larger scheme that uses the results obtained by this scheme to extend the inversion to the whole data set.
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".