Natural Fractures Characterization and In Situ Stresses Inference in a Carbonate Reservoir—An Integrated Approach
Bibliographic record
Abstract
In this paper, we characterized the natural fracture systems and inferred the state of in situ stress field through an integrated study in a very complex and heterogeneous fractured carbonate reservoir. Relative magnitudes and orientations of the in-situ principal stresses in a naturally fractured carbonate heavy oil field were estimated with a combination of available data (World Stress Map, geological and geotectonic evidence, outcrop studies) and techniques (core analysis, borehole image logs and Side View Seismic Location). The estimates made here using various tools and data including routine core analysis and image logs are confirmatory to estimates made by the World Stress Map and geotectonic facts. NE-SW and NW-SE found to be the dominant orientations for maximum and minimum horizontal stresses in the study area. In addition, three dominant orientations were identified for vertical and sub-vertical fractures atop the crestal region of the anticlinal structure. Image logs found useful in recognition and delineation of natural fractures. The results implemented in a real field development and proved practical in optimal well placement, drilling and production practices. Such integrated studies can be instrumental in any E&P projects and related projects such as geological CO2 sequestration site characterization.
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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 | 0.000 |
| 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 | 0.001 | 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".