Investigating Multiphase Flow Phenomena in Fine-Grained Reservoir Rocks: Insights from Using Ethane Permeability Measurements over a Range of Pore Pressures
Bibliographic record
Abstract
The ability to quantify effective permeability at the various fluid saturations and stress states experienced during production from shale oil and shale gas reservoirs is required for efficient exploitation of the resources, but to date experimental challenges prevent measurement of the effective permeability of these materials over a range of fluid saturations. To work towards overcoming these challenges, we measured effective permeability of a suite of gas shales to gaseous ethane over a range of pore pressures up to the saturated vapour pressure. Liquid/semiliquid ethane saturation increases due to adsorption and capillary condensation with increasing pore pressure resulting in decreasing effective permeability to ethane gas. By how much effective permeability to ethane gas decreases with adsorption and capillary condensation depends on the pore size distribution of each sample and the stress state that effective permeability is measured at. Effective permeability decreases more at higher stress states because the pores are smaller at higher stress states. The largest effective permeability drops occur in samples with dominant pore sizes in the mesopore range. These pores are completely blocked due to capillary condensation at pore pressures near the saturated vapour pressure of ethane. Blockage of these pores cuts off the main fluid flow pathways in the rock, thereby drastically decreasing effective permeability to ethane gas.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".