Bibliographic record
Abstract
Hydraulic fracturing is a widely used and ecient technique for enhancing oil extraction from heavy oil sands deposits. Application of this technique has been extended from cemented rocks to uncemented materials, such as oil sands. Models, which have originally been developed for analyzing hydraulic fracturing in rocks, are in general not satisfactory for oil sands. This is due to a high leak-o in oil sands, which causes the mechanism of hydraulic fracturing to be di erent from that for rocks. A thermal hydro-mechanical fracture nite element model is developed, which is able to simulate hydraulic fracturing under isothermal and non-isothermal conditions. Plane strain or axisymmetric hydraulic fracture problems can be simulated by this model and various boundary conditions, such as speci ed pore pressure/ uid ux, speci ed temperature/heat ux, and speci ed loads/traction, can be modeled. The developed model has been veri ed by comparing its results to existing analytical and numerical solutions for thermoelastic consolidation problems. The model has been used to simulate a laboratory experiment of hydraulic fracture propagation in oil sands. The results from the numerical model are in agreement with experimental observations. The numerical model and laboratory experiments both indicate that, for uncemented porous materials, such as sands (as opposed to rocks), a single planar fracture is unlikely to occur and a system of multiple fractures or a fracture zone consisting of interconnected tiny cracks should be expected.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".