Coupled Fluid Flow to Geomechanics in Fractued Reservoir: Governing Equation, Geomechanics Parameters and Numerical Method
Bibliographic record
Abstract
Abstract The effectiveness of a hydraulic fracturing technology has been primarily attributed to a creation of the geometry in the primary fracture. This concept however has been challenged particularly in low-permeability formations in which the size of a Stimulating Reservoir Volume (SRV) becomes the most important issue. Unlike the primary fracture, the area in the SRV is controlled by the fundamental geomechanics behaviors of the formation and a secondary fracture network propagation with possible different modes, and more importantly by the formation permeability change which is controlled by the induced stresses near the primary fracture. In this paper, the induced stresses near a hydraulic fracture in a pure elastic, poroelastic, dual-porosity media are analyzed and compared in order to characterize the low-permeability, sandstone and fractured formations, respectively. The general formulation for a fractured reservoir by a dual porosity model is developed and pore pressures and stresses near a wellbore and a hydraulic fracture are highlighted for production enhancement as the permeability change near a wellbore or a hydraulic fracture may contribute to such an enhancement significantly. Those key parameters controlling the pressure and stresses change and numerical method used are analyzed and presented.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".