Developing Fracture Measure as an Index of Fracture Impact on Well-Logs
Bibliographic record
Abstract
Due to the three-dimensional nature of fractures, it is difficult to characterize them completely and accurately. In this paper, a novel fractured zone detection criterion, Fracture Measure (FM), is proposed. FM is a parameter calculated by aperture, fracture type, azimuth and apparent distance. These factors have not been incorporated in previous studies to detect fractured zones. This study attempts to estimate FM by Artificial Neural Network to see if there is any relation between FM and conventional logs and to check the generalization ability of FM. Two datasets were used for the investigation: a real carbonate reservoir of an oil field in Iran and a synthetic heterogeneous reservoir, here incalled SYN. Comparing outputs of heterogeneous and homogeneous conditions showed that the Classification Correctness Rate (CCR) of the model in the homogeneous state was approximately 97%, and in the heterogeneous condition, it was between 74% and approximately 92%. Generalization ability in the homogeneous state varied from 91% to 94%, and in the heterogeneous condition, varied from 52% to 86%. In the real dataset, ANN was able to estimate FM with an average accuracy of approximately 80%and Classification Correctness Rate (CCR) of approximately 100%, which shows that FM could be modeled through well-logs. It is noteworthy that FM is capable of providing a fuzzy measure for fracture study.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".