DOES A GOOD OUTCROP JUST STAND OUT? – IDENTIFICATION AND ANALYSIS OF A POTENTIALLY REPRESENTATIVE BASIN CENTRED JURASSIC OIL BEARING SHALE OUTCROP
Bibliographic record
Abstract
Truly representative geological outcrops are not necessarily found conveniently along road cuts or by railway tracks. However, the ideal analogs for many of our technically challenging reservoirs do exist but have yet to be identified or adequately fingerprinted using the myriad of currently available sophisticated technical analysis tools at our disposition both in the field as well as in the lab. Canada’s Western Sedimentary Basin lacks a fully representative analog by which Jurassic Oil Prone fine grained unconventional resources can be profiled and characterized. In a collective effort, Geo-Libre Inc; ProGeo Consultants along with Calgary Rock and Materials have joined forces to analyze what may be a truly representative outcrop for Jurassic Oil Prone fine grained unconventional resources in the Western Canada Sedimentary Basin. The ultimate objective of this team effort being to answer some of the questions related to production challenges from tight basinal reservoir facies of Jurassic age which are abundant in Alberta and which hold potentially large petroleum resources. Methods which will be considered include using Xrf Profiling, Xrd, Porosity & Permeability, Mercury Injection Capillary Pressure (MICP) measurement, Petrographic Analysis & other techniques on outcrop samples.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".