Application of the Gray Level Co-occurrence Matrix (GLCM) Technique for Analyzing Fracture Networks in the Granitic Basement of Cuu Long Basin, Vietnam
Bibliographic record
Abstract
The Cuu Long Basin (CLB) in offshore Vietnam is a key hydrocarbon-producing region, known for its complex geological structures, particularly in the basement granite. However, traditional seismic methods often struggle to accurately map faults and fractures within the basement granite, largely due to the lack of distinct geological layering. To overcome this challenge, this study proposes the application of the gray level co-occurrence matrix (GLCM) technique to analyze the texture of geological features, such as faults and fractures, within the basement granite of the CLB. By applying the GLCM method, the study aims to detect basement faults, identify lineaments, and examine the compartmentalization of basement reservoirs. The results demonstrate that the GLCM analysis reveals zones with shallow-deep fault interactions have lower prospectivity compared to areas where older faults do not extend upwards. Additionally, the GLCM attributes show a correlation with the FMI (Formation Micro Imager) data, assisting in the identification of additional oil reserves. GLCM analysis reveals that zones with interactions between shallow and deep faults show lower prospectivity compared to areas where older faults remain confined to deeper levels in the basement granite of the CLB. These findings underscore the potential of GLCM as a valuable tool for enhancing fault detection and reservoir characterization in complex geological settings like the CLB.
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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 | 0.000 |
| 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 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".