Frequent Seismic Monitoring for Pro-Active Reservoir Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it