AE Response Characteristics of Intersections between Hydraulic Fractures and Embedded-Discontinuous Rock in Granitic HDR
Bibliographic record
Abstract
Acoustic emission (AE) is one of the most common means used to evaluate the geometries of hydraulic fractures (HFs). The preexisting discontinuities can significantly affect the propagation behaviors of HFs, which is the key to thermal reservoir construction. Hot dry rock (HDR) is high-temperature rock with no fluid and refers to high-temperature crystalline rock masses, such as granite, that exhibit low porosity and low permeability. Based on the geological survey and well-core analysis, not only natural fractures but also quartz veins and lithologic interfaces develop in granite, and the latter two are essentially different from conventional discontinuities. AE can effectively assess the development of HFs in HDRs; however, the AE response characteristics of intersections between HFs and embedded-discontinuous rock in granite have not been well understood. This study aims to address this. Physical simulation experiments of hydraulic fracturing and AE tests were conducted on granite with different preexisting discontinuities, especially the embedded-discontinuous rocks. Our results show that HF propagation in the granite matrix or through several discontinuities could cause stronger AE responses. For example, the AE events are in greater numbers and have higher energy. Intersections of HFs with natural fractures and lithologic interfaces induced stronger responses than did quartz veins. The AE response characteristics were tightly associated with the cementing strength, physical property differences, and interfacial roughness. Microobservation experiments show the defects at the interface between the embedded-discontinuous rocks provide conditions for the initiation and propagation of HFs. The AE response and injection pressure–time curve could reflect the properties of the fracturing process, and there was a positive correlation between the fluctuations of curves and AE energy. The analysis of the AE response can assist in obtaining the distribution information of preexisting discontinuities.
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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 | 0.001 | 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".