A revised method for organic porosity estimation in shale reservoirs using Rock-Eval data: Example from Duvernay Formation in the Western Canada Sedimentary Basin
Bibliographic record
Abstract
ABSTRACT Studies suggest that nanometer-scale pores exist in organic matter as a result of thermal decomposition of kerogen. Depending on the host rock lithology, organic pores could be the primary storage for hydrocarbon accumulation in unconventional petroleum plays. Although various methods are publicly available, estimation of organic porosity remains a challenge because the procedures involve certain simplification or some implicit assumptions on the calculation of initial total organic carbon (TOC). In this study, we propose a revised method to address some of these issues. A model of estimating hydrocarbon expulsion efficiency is developed and incorporated into the calculation of initial TOC, thus producing an estimate of organic porosity with an improved mass balance. The method has been tested and compared with estimates using other methods based on a Rock-Eval data set in the literature. An application of the method to a large data set from the Upper Devonian Duvernay Formation petroleum system in the Western Canada Sedimentary Basin reveals that the modification has a significant effect on the estimated organic porosity. This study also indicates that organic porosity in the Duvernay Formation ranges greatly from none in immature intervals to >6% in highly mature and organic-rich shale intervals. Scanning electron microscope images of immature and mature organic-rich shale samples of the Duvernay Formation show a progressive increase in organic porosity with increasing thermal maturity, supporting the proposed model calculation. The presence of a large volume of organic porosity in mature shale intervals suggests a significant amount of hydrocarbon may be stored in the organic nanopores in the Duvernay Formation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 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".