Varying-scale shale gas flow: Discrete fracture networks (DFN) based numerical simulation
Bibliographic record
Abstract
The development of unconventional gas reservoirs represents totally distinctive characteristics as compared with its conventional counterparts. The prevailing commercial strategy of stimulating fractures to connect the matrix to wellbores results in an even more complicated shale gas flow behavior, in which matrix flow is fairly coupled with fracture flow. Numerous works have been contributed to unveil the underground shale gas production mechanisms. And some impressive progresses have been made in describing the complex subterranean shale gas flow, such as the introduction of discrete fracture network (DFN) from National Energy Technology Laboratory (NETL). However, none of them captures the varying-scale nature of the in-place gas flow in shale sediments. In this work, we try to fill this gap. Following the concept of DFN, we set up mathematical models for shale gas flow in matrix and fracture networks, and also for their mass transfer in between without neglecting its varying-scale nature. In addition, we also investigate comprehensively the impact of various effects and phenomena occurred in pore spaces during production course, such as adsorption and desorption on rock surfaces, gas slippage and Knudsen diffusion, and diffusion in bulk kerogen, on the overall shale gas production using our new model for a specific shale gas reservoir case study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".