A comprehensive review on the flow behaviour in shale gas reservoirs: Multi‐scale, multi‐phase, and multi‐physics
Bibliographic record
Abstract
Abstract An accurate description of fluid flow is critical for the prediction of the productivity of shale gas reservoirs. In this paper, we present the current advances and a systematic summary on the fluid flow in shale gas reservoirs. First, shale pore structures, consisting of organic pores and inorganic pores ranging from nanoscale to micro‐scale, and reservoir fluids, including free gas, absorbed gas, and water, are systematically presented. Thereafter, multi‐physics flow phenomena motivated by scale effects, such as continuum flow, slip flow, transition flow, free molecular flow, and surface diffusion for gas, as well as the effective viscosity and slip boundary condition for water, are carefully summarized. Meanwhile, these flow mechanisms are discussed with molecular dynamics (MD) simulations and theoretical analysis. Subsequently, on the basis of upscaling approaches, including capillary bundle models, the lattice Boltzmann method (LBM), and pore network models (PNMs), fluid flow through heterogeneous shale matrix is reviewed and the influences of scale effects and pore structures are clarified. Additionally, shale gas well performance is discussed by combining the multiple transport mechanisms and a fracturing‐shut‐in‐flowback‐production process. Our review concluded that the fluid flow behaviour in shale gas reservoirs is a complex multi‐scale process accompanied by multi‐physical phenomena and multi‐fluid distributions. Keeping this in mind is helpful for predicting the shale gas production and recoverable gas resources. We expect this study can not only help by providing a better understanding of the fluid flow in shale reservoirs but also provide significant implications to address other multiphase flow processes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".