Integration of image and dipole sonic logs for identification of natural fractures and stress-induced anisotropy in Asmari reservoir (A case study, SW Iran)
Bibliographic record
Abstract
Borehole sonic dispersion analysis is a technique that provides valuable insights into the realm of borehole sonic interpretation. This research involves an analysis of shear-wave anisotropy and ultrasonic image logs to differentiate between types of fractures and their orientations. Evaluating fractures relies on core samples and image logs are limited. This highlights the need for a more affordable and efficient way to analyse fractures. A challenge in the wellbore is distinguishing natural fractures from those caused by drilling. Using oil-based mud often makes it hard to find signs indicating the direction of in-situ stress. A new method has been created to reliably identify natural fractures when image logs are insufficient for mapping fracture networks. The cross-dipole data reveals five main zones exhibiting shear-wave splitting. Higher anisotropy is observed at shallower depths, while the deeper interval shows low porosity accompanied by considerable inhomogeneity, highlighting potential areas of concern. The dominant directions of anisotropy are aligned with NW-SE, WNW-ESE, and N-S orientations. Slowness frequency analysis of rotated flexural waves identifies fracture types. Dispersion profiles show natural and induced fractures, with cross-over patterns indicating stress-induced anisotropy. Significant inhomogeneity is observed in the bottom interval, where the differences between maximum and minimum energy level are pronounced. Wider dispersion curves suggest breakouts are slowing high-frequency flexural waves, indicating mechanical damage. The maximum stress direction is determined by the fast-shear azimuth. In conclusion, this study demonstrates that by integrating acoustic shear dispersion, shear anisotropy, Stoneley analysis, and image log data, fractures within the borehole wall can be effectively investigated. • The dominant mechanisms contributing to anisotropy in this well are natural fractures and breakouts. • The sonic and image log relationship relies on fracture density, aperture, and borehole proximity. • Image and dipole sonic logs provide complementary information for fracture analysis, and their consistency is crucial for accurate fracture characterization. • The methodology outlined demonstrates a remarkable consistency between image and dipole sonic logs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".