Effects of Adsorption and Confinement on Shale Gas Production Behavior
Bibliographic record
Abstract
Abstract Shale gas becomes an important natural gas supplier in recent years. The technologies including horizontal wells and hydraulic fracturing drive the booming of shale gas industry. Gas in shale reservoirs is stored as free gas in both mineral pores and natural fractures, as well as absorbed gas on pores surface. The effect of gas adsorption is generally ignored in conventional reservoirs. However, the absorbed gas has to be taken into consideration for shale gas production because of its huge amount in nanoscale porous media. The smaller the pore throat radius, the more significant is the effect of confinement. Therefore, production behavior can be altered by the effects of adsorption and confinement in shale gas reservoirs. On the basis of Montney shale gas reservoir modeling, effects of adsorption and confinement on shale gas production behavior are investigated in this paper. Results show that total gas production increases with the consideration of adsorption and confinement effects. As gas density and viscosity decreases prior to condensation occur with the effect of confinement caused by nanoscale pore throat, incremental of density difference between free gas and absorbed gas will delay the production of absorbed gas. Moreover, the difference between the amount of free gas produced and absorbed gas produced become larger with the effect of confinement during reservoir depletion.
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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 | 0.001 |
| 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 | 0.000 |
| 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".