Diagenetic study of Tertiary sequences, Beaufort-Mackenzie Basin - Phase 1
Bibliographic record
Abstract
The Geological Survey of Canada (GSC) is involved with a multi-disciplinary study of petroleum systems of the Beaufort-Mackenzie region of Arctic Canada under the Earth Sciences Sector Geo-Mapping for Energy and Minerals (GEM) Program. As part of this research, a contracted petrological study of petroleum reservoir quality was undertaken to investigate the post-depositional controls on porosity and permeability of the Tertiary (Paleogene age; 62 - 24 Ma) sedimentary sequences (Aklak, Taglu, Richards and Kugmallit sequences) that host petroleum discoveries in the area. Porosity is important for calculating the volume of petroleum within a reservoir and permeability affects that rate at which petroleum can be produced. Both parameters are critical for evaluating the economic viability of individual petroleum prospects and for assessing the petroleum resource potential of the area. Generally, porosity and permeability are high at the time of sediment deposition but both are reduced by mechanical compaction with increasing sediment burial. As temperature and burial depth increase, diagenetic processes such as mineral dissolution or precipitation (cementation) can further enhance or reduce porosity and permeability. This study focuses on how cementation processes affect reservoir quality and it builds on the previous work of Dr. Volkmar Schmidt which is summarized in GSC Open File 1534. The report is rich in petrographic descriptions and contains many photographic images of rock cores and thin sections used for microscopic examination. These results provide a firm foundation for future studies of the diagenetic evolution of sedimentary successions of the Beaufort-Mackenzie Basin. This study was funded by the former Beaufort-Mackenzie consortium of companies (BP Canada Energy Company, Chevron Canada Limited, ConocoPhillips Canada Resources Corporation, Imperial Oil Resources Ventures Limited, MGM Energy Corporation, and Shell Exploration and Production Company).
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".