A Numerical Model for Multi-Mechanism Flow in Shale Gas Reservoirs with Application to Laboratory Scale Testing
Bibliographic record
Abstract
Abstract Shale is a complex unconventional reservoir having a variety of storage and flow mechanisms coupled together. Contrary to conventional reservoirs where gas is stored only in the pore space as free gas; current numerical simulators assume gas is stored as free and adsorbed phase in shale. Recent advancement of visualization and measurement techniques has enabled us to look at shale more closely. Shale has been found to contain a well-developed nanopore network in the organic matter or kerogen. Correspondingly we believe that gas is stored via four storage mechanisms: gas in natural fractures, free gas in matrix pores, adsorbed gas and gas dissolved in kerogen bulk in the shales. In this work we formulate a flow model for shale gas reservoirs including the physics at nano scale. The model incorporates the gas stored in micro-fractures, gas stored in nanopores, gas adsorbed on the pore walls and gas dissolved in kerogen bulk. This complex quad porosity system has coupled equations between three interconnected systems; between matrix and fracture set, between matrix and adsorbed gas and between matrix and kerogen bulk. Sets of governing equations were derived for the coupled systems and numerically solved to find gas production as a function of time. The model was validated against laboratory observed data for a shale canister test from a Canadian shale gas field. This laboratory scale model can be suitably up-scaled for field scale simulation of shale reservoirs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".