Structural and time-lapse imaging through gas clouds via FWI at Eldfisk field, North Sea
Bibliographic record
Abstract
In this study, we address the persistent challenges of structural and time-lapse imaging within the Seismically Obscured Areas (SOAs) at Eldfisk field, North Sea. Despite numerous seismic programs, imaging efforts within SOAs at structures A and B continue to be hindered, primarily due to the presence of expansive, complex gas clouds in the overburden. To address this, we apply Full-Waveform Inversion (FWI) to 3D and 4D Ocean Bottom Node (OBN) data, aiming to enhance both structural and time-lapse imaging in these areas. Starting with a legacy velocity model, FWI is used as the main tool for model building, capturing detailed velocity changes associated with gas clouds. The refined model is then input into a Q (the quality factor)-compensated Reverse-Time Migration (Q-RTM) pseudo-gather-based imaging flow to generate significantly improved structural images in the SOAs. Additionally, our application of 4D FWI successfully uncovers time-lapse velocity changes that align with the injection and production history within the SOAs, which were not observed by traditional seismic-based 4D methods that require a good image.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".