Frequent Seismic Monitoring for Pro-Active Reservoir Management
Bibliographic record
Abstract
Summary Beyond exploration, the most important role for geophysics in the oil and gas industry is to influence field operations, so that the value of existing assets is fully realized. The recent trend in time-lapse seismic has been toward very frequent reservoir monitoring, with the aspiration to optimize both near- and long-term field management. In this paper we describe steps taken by Shell to tackle the main challenges of frequent seismic monitoring — cost, intrusiveness, and value realization. Offshore, cost reductions can be achieved through novel types of receivers and more efficient vessel utilization. Onshore, cost and footprint reductions are sought through novel survey designs, including fiber-optic DAS cables, sparse geometries, and movable subsurface sources. A demonstration of value is currently pursued through a large onshore trial of continuous monitoring of steam injection at Peace River, Canada, active since 2014. Initial results indicate that steam non-conformance can be diagnosed, remediation actions taken, and their effectiveness evaluated. Inter-disciplinary collaboration is a must. The associated workflow for assimilating frequent seismic data continues to develop and should benefit future monitoring projects both onshore and offshore.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".