Intensive Use of 4D Seismic in Reservoir Monitoring, Modelling and Management: The Dalia Case Study
Bibliographic record
Abstract
Introduction Born nearly thirty years ago, time lapse (4D) seismic monitoring technology has been developed in some cases to monitor fluid movement and to distinguish between drained and un-drained portions of a reservoir. It allows quantitatively improving reservoir models, particularly their predictive capability. Indeed, the benefits of time-lapse seismic for reservoir characterization depend on the quality of 4D acquisition and processing, but they also greatly depend on the particular 4D inversion and interpretation methods used. Finally, a decisive aspect is certainly the capability of integrating results from different disciplines in an effective way. Timing is also crucial: results delivered in a few months can have a direct operational impact such as field monitoring or well location and design optimization. For the last ten years, Total has recognized the importance of time lapse seismic and has therefore conducted 4D seismic monitoring in different geological environments. Examples of 4D experiences range from monitoring of water injection and production for complex reservoir management and field development in the Gulf of Guinea (Angola and Nigeria); monitoring of geomechanical effects in HPHT fields (Elgin-Franklin, UK), in compacting reservoirs in Norway (Ekofisk and Valhall) and in the Gulf of Mexico (Matterhorn, US); monitoring of steam chamber in tar sands (Surmont, Canada) and monitoring of compaction and water rise in carbonates (South-East Asia). This paper focuses on the intense use of 4D seismic on the DALIA Field (Angola, Block 17).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".