A Numerical Investigation of the Hazardous Injection Area of Induced Earthquake during Hydraulic Fracturing
Bibliographic record
Abstract
Hot dry rock (HDR) geothermal energy has many advantages, such as being renewable, clean, widely distributed, and without time and weather limitations. Hydraulic fracturing is usually needed for the exploitation of HDR geothermal energy. It has many hidden faults in the reservoir/caprock sequences. Injecting fluid into underground formations during hydraulic fracturing often induces fault slip and leads to earthquakes. Therefore, to well understand the induced fault slip and earthquakes is important for the applications and development of HDR geothermal exploitation. In this study, we investigated the hazardous injection area of the induced earthquakes during hydraulic fracturing. The study was based on a hydraulic fracturing test in Qiabuqia geothermal field in China. According to the field, a fault‐surrounding rock‐fracturing region system was developed to study the influences of fluid injection on the stability of the specific fault. A total of 60 hydraulic fracturing regions and 180 numerical experiments were designed. The results revealed that the hazardous injection regions that threaten the fault’s stability were near to the fault and concentrated on the following four areas: (a) above the top of the fault in underlying strata; (b) above the top of the hanging wall of the fault in underlying strata; (c) near to the fault planes in both footwall and hanging wall; (d) at the bottom of the footwall of the fault in underlying strata. The hazardous injection area can be controlled effectively by adjusting the injection pressure.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".