Unlocking the secrets of North American shale reservoirs using deterministic rock-physics models
Bibliographic record
Abstract
I present a new workflow that has been used to build detailed data-driven rock physics models of various prospective unconventional shale intervals within the Permian Basin of the US, and the Horn River and Western Canadian Sedimentary Basins of Canada. A couple of simple approximations enable us to estimate depth-varying in-situ geophysical properties (Vp, Vs and density) for the three commonly-defined and often volumetrically-dominant mineral groups – carbonates, clays and silicates. Confidence that these estimated mineral properties are physically reasonable is obtained by: (1) testing using synthetic data; and (2) comparison with published data. After the detailed rock physics models have been built, we can test whether simplified models may be usefully applied, to either geophysical well log data or seismic AVO inversion data, to predict mineralogy for regional reservoir characterization studies. Presentation Date: Tuesday, September 26, 2017 Start Time: 11:25 AM Location: 381A Presentation Type: ORAL
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".