Inertial and turbulent flow in hydro‐mechanically coupled KGD‐like fractures
Bibliographic record
Abstract
Abstract Fluid flow in deformable fractures is important to many applications including hydraulic stimulation. Fracture flow simulations typically rely on the Poiseuille flow model which presumes laminar quasi‐steady‐state flow conditions with negligible inertia. The high flow rates involved in industrial applications bring these assumptions into question. The GG22 flow model is a new method to capture inertial effects and turbulent flow phenomena with a moderate increase in computational complexity. Here we develop and verify the first hydro‐mechanically coupled finite element – finite volume model for fracture flow which uses the GG22 model. We use the model to examine fracture flow between oscillating elastic plates and show that inertia may elicit phase‐shifts in the fluid response, larger fluid pressures and rock mass stresses, and induce wave‐like behaviour even in a quasi‐static rock mass. We then apply the model to hydraulic stimulation of a cemented fracture in both the viscous and toughness dominant propagation regimes and examine the influence of inertia and turbulence compared to Poiseuille flow. We demonstrate that inertial effects due to the variation of aperture are negligible in two‐dimensional KGD‐like fractures with constant injection rates. If the flow rates are large enough to invoke turbulent flow behaviour, then significantly different fracture propagation behaviour is observed. The modelling established in this article will serve as the basis to examine the role of inertia and turbulence in axisymmetrical radial fractures and dynamic stimulation with pressure pulsing where we expect the role of inertia to be more significant.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".