The impact of organic pores on estimation of overpressure generated by gas generation in organic-rich shale: Example from Devonian Duvernay Shale, Western Canada Sedimentary Basin
Bibliographic record
Abstract
Abstract Overpressure determination is essential for sweet spot identification, resource evaluation as well as drilling in organic-rich shale, a typical self-contained source-reservoir system. Traditional pressure prediction methods designed for conventional source rocks (e.g., the Eaton’s method) were typically established based on mechanical compaction, which did not consider the increase in organic porosity. In this study, a practical approach was introduced to investigate the impact of organic pores on overpressure estimation in organic-rich shale, where overpressure primarily results from gas generation, through introducing organic pores into the expression of sonic transit time and determining overpressure with organic-pore–corrected sonic readings. The proposed organic porosity and overpressure estimation models were applied to the Duvernay Shale of the Western Canada Sedimentary Basin to investigate the impact of organic porosity on prediction of overpressure due to fluid expansion in gas generation windows. A comparison of formation pressure predictions from the application example in the Duvernay Shale demonstrates that the proposed method can significantly improve prediction of the pore fluid pressure in mature source rock. The revised method provides better estimates of overpressure with an improved coefficient of determination of R2 = 0.7. In contrast, without considering the impact from organic pores in the shale, the traditional Eaton’s method overestimates the pressure by up to 42.83% and the correlation between the predicted and the observed results is poor (R2 = 0.15).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".