Numerical Simulation of Gas Production from Gas Shale Reservoirs—Influence of Gas Sorption Hysteresis
Bibliographic record
Abstract
The true contribution of gas desorption to shale gas production is often overshadowed by the use of adsorption isotherms for desorbed gas calculations on the assumption that both processes are identical under high pressure, high temperature conditions. In this study, three shale samples were used to study the adsorption and desorption isotherms of methane at a temperature of 80 °C, using volumetric method. The resulting isotherms were modeled using the Langmuir model, following the conversion of measured excess amounts to absolute values. All three samples exhibited significant hysteresis between the sorption processes and the desorption isotherms gave lower Langmuir parameters than the corresponding adsorption isotherms. Langmuir volume showed positive correlation with total organic carbon (TOC) content for both sorption processes. A compositional three-dimensional (3D), dual-porosity model was then developed in GEM® (a product of the Computer Modelling Group (CMG) Ltd., Calgary, AB, Canada) to test the effect of the observed hysteresis on shale gas production. For each sample, a base scenario, corresponding to a “no-sorption” case was compared against two other cases; one with adsorption Langmuir parameters (adsorption case) and the other with desorption Langmuir parameters (desorption case). The simulation results showed that while gas production can be significantly under-predicted if gas sorption is not considered, the use of adsorption isotherms in lieu of desorption can lead to over-prediction of gas production performances.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".