Integrating Core and Well Logs for Unconventional Shale Evaluation in Western Canada
Bibliographic record
Abstract
Summary Unconventional shale reservoirs such as the Montney Formation in the Western Canada Sedimentary Basin have become an attractive target because of its huge volume. As the technological advancement reduce the challenges faced, rapid drilling activities are taking place to produce hydrocarbon in place ( Lewis, et al., 2004 ) Key parameters for unconventional shale evaluation include: 1) X-ray Diffraction (XRD), 2) routine core analysis (RCA), 3) total organic carbon content (TOC), 4) core water-saturation (Dean-Stark’s), and 5) quantitative matrix components from elemental capture spectroscopy (ECS) logs. Integrating these pieces of information are essential to produce robust interpretations. The challenging aspect in evaluating unconventional play is the selection of the model ( Quirein, et al., 2010 ). A good evaluation workflow has been developed by integrating XRD, SCAL, RCA and ECS logs data. All sources are essential to the interpretations as they provide independent calibration points such as porosity, matrix-density, permeability, minerals’ composition and water saturation. The steps described in this paper provides an effective approach for establishing and predicting mineralogy, matrix-density, porosity and permeability from wireline logs, as seen from core. Elemental Log Analysis (ELAN) are used to quantify the mineralogy of the interpreted wells, including TOC (and kerogen), porosity, permeability and water saturation.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".