Characterization and Quantitative Assessment of Shale Fracture Characteristics and Fracability Based on a Three-Dimensional Digital Core
Bibliographic record
Abstract
At present, assessment techniques for the fracability of shale reservoirs, which rely on the formation of an effective fracture network, are scarce. Hence, in order to assess the fracability, it is critical to establish a quantitative correlation between the pattern of fracture distribution after fracture and fracability. The present investigation utilizes three-dimensional digital core technology and triaxial compression experiments to simulate the fracturing process in typical domestic shale reservoir cores. In addition to utilizing the maximum ball algorithm to extract fracture images, a number of other techniques are employed to compute the spatial quantitative parameters of the fractures, including least squares fitting, image tracking algorithms, and three-dimensional image topology algorithms. The introduction of the notion of three-dimensional fracture complexity serves to delineate the degree of successful fracture network formation subsequent to fracturing. A quantitative fracability characterization model is developed by integrating the constraints of fracture network formation potential and fragmentation potential. The results of this study show that the quantitative characterization of the characteristic parameters of cracks can be achieved by establishing a method for extracting crack information as well as parameters after core compression and completing the construction of a three-dimensional complexity characterization model. Meanwhile, the three-dimensional post-compression fracture image validation shows that the core fracturability index can better reflect the actual fracturing situation, which is in line with the microseismic monitoring results, and significantly improves the accuracy of fracturability characterization, which is an important guideline for the fracturing design of shale gas 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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".