Phase Behavior in Nanopores and Its Indication for Cyclic Gas Injection in a Volatile Oil Reservoir from Duvernay Shale
Bibliographic record
Abstract
Abstract Duvernay shale spans over 6 million acres with a total resource of 440 billion barrels’ oil equivalent in the Western Canada Sedimentary Basin (WCSB). The oil recovery factors typically decrease with the decreasing of gas-oil ratio (GOR) in oil window of Duvernay shale. The volatile oil recovery factors are typically 5–10%. Enhanced oil recovery technologies should be applied to improve the economics of the reservoirs. In this paper, the volatile oil from the Duvernay shale was taken as an example for phase behavior study. We analyzed the nanopore confinement on phase behavior and physical properties of Duvernay shale oil. The shift of critical properties was quantified within nanopores. With the confinement of nanopores, the viscosity, density, and bubble point pressure of the oil decrease with the shrinking of the pore size. Minimum miscibility pressure (MMP) was calculated for different injected gases. The MMP from high to low is N2>CH4>lean gas>rich gas>CO2. In the case of injecting the same gas component, the MMP decreases as the pore size decreases. The wellhead rich gas is suggested to be the main gas source for gas injection in Duvernay shale. The formation pressure should be rapidly increased to the MMP and maintained close to it, which would help to improve the effect of gas injection and enhance shale oil recovery. This paper can provide critical insights for the research of shale oil gas injection for enhanced oil recovery.
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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 | 0.000 |
| 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 | 0.000 |
| 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 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".