Enhancing hydraulic fracturing for in-situ remediation in low-permeability soils: A comprehensive investigation of fracture propagation
Bibliographic record
Abstract
Enhancing the complexity of the hydraulic fractures to provide a wide channel for the injection of the agent is crucial for remediating low-permeability contaminated sites. This study involved a physical simulation experiment of large-scale true triaxial hydraulic fracturing in undisturbed soil, as well as field fracturing tests, to investigate fracture initiation mechanisms and the influence of different factors on fracture propagation. The study revealed a unique failure mode for low-permeability soils characterized by impact splitting, involving simultaneous tensile and shear failure. Three typical fracture propagation patterns emerged: (1) horizontal fracture, (2) parallel fracture, and (3) complex fracture. Silty clay predominantly exhibited horizontal fractures, while mucky clay facilitated the formation of complex fractures dominated by multiple transverse fractures. As the vertical stress difference coefficient increased from 1.0 to 1.5, the pressure on the fracture surface enhanced the connection between hydraulic fractures and natural fractures. Hydraulic fracturing in low-permeability soils necessitated large displacements and high-viscosity fracturing fluids to sustain fracture propagation. The field fracturing test results underscored that soil type and in-situ stress were the primary factors governing hydraulic fracture initiation and propagation. Identifying the optimal fracturing location was critical for achieving the maximum stimulated formation volume.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| 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".