Nano-Confined Adsorbed and Free Gas in Shale Reservoirs: A Molecular Dynamic Study
Bibliographic record
Abstract
Abstract Gas production from shale reservoirs is expected to last for hundred years by some estimates. However, the phase behavior of gas in shale reservoirs is inherently more complex compared to the conventional ones. In shale reservoirs, a large amount of gas is stored in nano-size pores which are an integral part of kerogen structure. The phase behavior of this fluid is significantly different from those in the bulk and is still far from being fully investigated. Better understanding of the influence of nano-confinement on the fluid phase behavior should help in estimating the amount of gas in place and to develop an optimum production plan to maximize the recovery. Using molecular dynamics simulations, we modeled methane's phase behavior in shale gas reservoirs wherein the nano-pores in the kerogen play a major role in adsorption of the gas molecules. A fully atomistic model was used for methane and the pore walls; the pore walls were represented by graphite sheets. Eight pores of width ranging from 0.7 nm up to 4.0 nm were considered; the pores were connected to a bulk volume (reservoir). The amount of free and adsorbed gas in the pores was calculated in the pressure range from 6000 to 100 psi. The results demonstrate the influence of pore size on the mechanism of gas release and its dynamics. The gas production rate from pores is a result of the interplay between the rate of transport of a free gas molecule out of the pore, and the rate of desorption of the adsorbed gas from the pore wall. For the smallest pores, adsorption plays the main role in controlling the dynamics as compared with the large ones. Our data can be utilized for predicting the rate of gas production from shale gas based on the pore size distribution and fluid composition.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".