Coupled THMC model-based prediction of hydraulic fracture geometry and size under self-propping phase-transition fracturing
Bibliographic record
Abstract
Abstract The Self-Propping Phase-transition Fracturing Technology (SPFT) represents a novel and environmentally friendly approach for a cost-effective and efficient development of the world’s abundant unconventional resources, especially in the context of a carbon-constrained sustainable future. SPFT involves the coupling of Thermal, Hydraulic, Mechanical, and Chemical (THMC) fields, which makes it challenging to understand the mechanism and path of hydraulic fracture propagation. This study addresses these challenges by developing a set of THMC multifield coupling models based on SPFT parameters and the physical/chemical characteristics of the Phase-transition Fracturing Fluid System (PFFS). An algorithm, integrating the Finite Element Method, Discretized Virtual Internal Bonds, and Element Partition Method (FEM-DVIB-EPM), is proposed and validated through a case study. The results demonstrate that the FEM-DVIB-EPM coupling algorithm reduces complexity and enhances solving efficiency. The length of the hydraulic fracture increases with the quantity and displacement of PFFS, and excessive displacement may result in uncontrolled fracture height. Within the parameters considered, a minimal difference in fracture length is observed when the PFFS amount exceeds 130 m3, that means the fracture length tends to stabilize. This study contributes to understanding the hydraulic fracture propagation mechanism induced by SPFT, offering insights for optimizing hydraulic fracturing technology and treatment parameters.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".