A Practical Methodology For Integration of 4D Seismic in Steam-Assisted-Gravity-Drainage Reservoir Characterization
Bibliographic record
Abstract
Summary 4D seismic is a dynamic source of data that provides information about changes in reservoir-rock and -fluid properties over time. Seismic attributes are sensitive to variations in the fluid content, temperature, and pressure distribution; therefore, 4D-seismic images contain information on the nature of fluid flow within the reservoir. Perhaps the most-reliable and -important information that can be learned from 4D-seismic images is related to anomalies in fluid flow within the reservoir. During steam-assisted gravity drainage (SAGD), the steam-chamber propagation is fairly clear from 4D-seismic images, mainly because of higher gas saturation in the chamber. Therefore, anomalies are revealed by the absence or unexpected location of the steam chamber. In this paper, a practical methodology is proposed for consideration of anomalies identified from 4D-seismic images in geostatistical reservoir models. The geostatistical realizations are updated to enforce missing anomalies and improve reservoir characterization. The updated models are suitable for reservoir decision making and management.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".