Experimental Investigation into the Process of Hydraulic Fracture Propagation and the Response of Acoustic Emissions in Fracture–Cavity Carbonate Reservoirs
Bibliographic record
Abstract
Fracture–cavity carbonate reservoirs account for a considerable proportion of oil and gas resources. Because of the complicated relationships between cavities, fractures and pores in these reservoirs, which are defined as cavity clusters, fracturing technology is employed to enhance their hydrocarbon productivity. However, almost all previous studies have just considered the effect of a single natural cavity or fracture on the propagation of a hydraulic fracture; therefore, the mechanism by which a hydraulic fracture interacts with a cavity cluster needs to be clarified. In this study, cavity clusters with different distributions were accurately prefabricated in synthetically made samples, and large-scale simulation equipment was employed to systematically perform fracturing experiments considering different horizontal differential stress levels. Meanwhile, the hydraulic fracture propagation behaviors were comprehensively analyzed through fracture morphology, fracturing curves, the complexity of the fracture network and acoustic emission monitoring. It was found that a natural fracture with a smaller approach angle is favorable in guiding a hydraulic fracture to a cavity. The fracturing curves were divided into the following four types: frequent fluctuations with “step-like” shapes, great fluctuations with slightly lower closure pressure, fluctuations with obviously lower closure pressure, and little fluctuations with obviously lower closure pressure. And different cavity cluster distributions play a dominant role in the complexity of generated hydraulic fracture networks. In addition, AE energy was used to judge the ease of crossing the cavity. The above findings indicated that for the actual exploration and exploitation of carbonate reservoirs, the geological exploration of different fracture–cavity structures in reservoirs would be required, and targeted fracturing engineering designs need to be carried out for different fracture–cavity carbonate reservoirs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".