Bibliographic record
Abstract
This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 165428, ’Continuous Land Seismic Reservoir Monitoring of Thermal EOR in the Netherlands,’ by Julien Cotton, Laurene Michou, and Eric Forgues, CGG, and Kees Hornman, Shell Global Solutions International, prepared for the 2013 SPE Heavy Oil Conference Canada, Calgary, 11-13 June. The paper has not been peer reviewed. A reservoir-monitoring system has been installed on a medium-heavy-oil onshore field in the context of redevelopment by gravity-assisted steamflood. The challenge was to monitor the lateral and vertical expansion of the steam chest continuously with seismic reflection. The high sensitivity of the buried acquisition system allows the tracking and monitoring of very small variations of the reservoir’s physical properties in both the spatial and calendar domains. A daily 4D “movie” of the changes allows proposing a scenario that explains the unexpected behavior of the production. Introduction The Schoonebeek field is in the northeast of the Netherlands with 1 billion bbl of stock-tank oil initially in place. Between 1948 and 1996, the oil was produced with several small-scale thermal enhanced-oil-recovery pilots with vertical wells. Today, the oil is produced by gravity-assisted steamdrive. A permanent seismic system including 2D and 3D phases was installed in 2010 to monitor the reservoir’s evolution during steam injection. The monitoring lasted 2 years. During this time, the propagation of the steam plume injected into a horizontal well located between two horizontal producer wells was tracked in order to understand the 4D behavior of the steam and possibly update the dynamic production model.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".