Multi-scale, multi-continuum and multi-physics model to simulate coupled fluid flow and geomechanics in shale gas reservoirs, A
Bibliographic record
Abstract
In this study, several efficient and accurate mathematical models and numerical solutions to unconventional reservoir development problems are developed.The first is the threedimensional embedded discrete fracture method (3D-EDFM), which is able to simulate fluid flow with multiple 3D hydraulic fractures with arbitrary strike and dip angles, shapes, curvatures, conductivities and connections.The second is a multi-porosity and multi-physics fluid flow model, which can capture gas flow behaviors in shales, which is complicated by highly heterogeneous and hierarchical rock structures (ranging from organic nanopores, inorganic nanopores, less permeable micro-fractures, more permeable macro-fractures to hydraulic fractures).The third is an iterative numerical approach combining the extended finite element method (X-FEM) and the embedded discrete fracture method (EDFM), which is developed for simulating the fluid-driven fracture propagation process in porous media.Physical explanations and mathematical equations behind these mathematical models and numerical approaches are described in detail.Their advantages over alternative numerical methods are discussed.These numerical methods are incorporated into an in-house program.A series of synthetic but realistic cases are simulated.Simulated results reveal physical understandings qualitatively and match with available analytical solutions quantitatively.These novel mathematical models and computational solutions provide numerical approaches to understand complicated physical phenomena in developing unconventional reservoirs, thus they help in the better management of unconventional 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".