Use of the photoelectric effect as a reservoir quality indicator in the Niobrara Formation, Piceance Basin, northwest Colorado
Bibliographic record
Abstract
The late Cretaceous Niobrara Formation and underlying lower Mancos Group have significant petroleum potential in the Piceance Basin of northwest Colorado. Relative to the Denver Basin Niobrara, the Piceance Basin Niobrara has had significantly less drilling activity, and therefore fewer subsurface data are available. There are several key geologic differences pertaining to the Niobrara depositional history in these two basins. First, the overall thickness of the formation increases greatly to the west. Thicknesses of 91.4 m (300 ft), common in the Denver Basin, become thicknesses of as much as 548.6 m (1800 ft) in the Piceance Basin. Second, to date, maximum total organic carbon (TOC) values from the Piceance Basin are approximately 3 wt%, whereas in the Denver Basin, TOC values may be as high as 8 wt%. Several factors may contribute to the lesser TOC, but a significant factor is the dilution of organic material by increasing siliciclastic deposition. Unlike the Denver Basin stratigraphy of organic-rich marls providing the bulk of sourcing to carbonate-rich benches of greater fracture porosity, TOC and carbonate richness are coupled in the Piceance Basin. Core and well-log data suggest that the Piceance Basin Niobrara Formation’s carbonate-rich strata have higher TOC content relative to the interlaying clay-rich strata. This relationship enables the use of the photoelectric effect, the PEF or PE log, to map trends of carbonate richness and classify reservoir quality, where carbonate and organics may be at maximum values. In other unconventional reservoirs that share similar depositional histories and display the positive correlation between carbonate and organic richness, the PEF curve should be used for reservoir quality screening purposes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".