Permeability Enhancement During Artificial Fracturing: Implications for Deep Sedimentary Enhanced Geothermal Systems
Bibliographic record
Abstract
Summary A portion of natural fractures may become re-activated during hydraulic fracturing treatment in deep sedimentary enhanced geothermal systems (EGS), dependent on their geometry and orientation, among other factors. While the stress-dependency of natural fractures in low-permeability sedimentary rocks has been the subject of many studies, very few experimental works have met the considerable technical challenge of measuring permeability during fracturing process. This experimental study investigates the evolution of natural fractures permeability in low-permeability sedimentary rocks during fracturing process with examples from low-permeability siltstones (Canadian Montney Formation). The primary objective was to evaluate the mechanisms that control natural fracture permeability due to fracture tip extension and/or fracture aperture dilation during fracturing. Experimental observations indicated that the permeability of fractured core (∼1 md) was significantly, about two orders of magnitude, larger than that of intact core (∼0.01 md) but only about 2–4 times larger than that of partially-fractured core (0.2–0.4 md). Notably, the geometries of the induced fractures differed significantly between the intact (rough and zigzagged) and partially-fractured (smooth with straight aperture) cores. The outcomes of this study could be important for assessing the evolution of natural fracture permeability during hydraulic fracturing in deep sedimentary EGS.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".