In‐situ stress determination and fracture characterization using image logs: The Paleogene Dongying Formation in Nanpu Sag, Bohai Bay Basin, China
Bibliographic record
Abstract
Abstract Fracture detection and in‐situ stress determination via well logs are important for exploration and exploitation of subsurface hydrocarbons. Cores, thin sections, and image logs are used to describe and interpret the subsurface fractures and in‐situ stress in the Paleogene Dongying sandstones in Nanpu Sag, Bohai Bay Basin, China. The maximum horizontal stress (SHmax) indicates a nearly east‐west trend according to the borehole breakouts and drilling‐induced fractures. Natural fractures in the Dongying sandstones are classified into: (1) open fractures and (2) closed fractures. The cement‐filled (closed) fractures are commonly detected by image logs as bright discontinuous sinusoidal waves due to resistive filling materials, while the open fractures are evident on image logs appearing as dark sine waves. The open fractures are of dominantly high dip angles, and the rose diagrams confirm the presences of two sets of fractures: northwest‐southeast and northwest‐southeast orientation. Fractures with strikes approximately parallel to the SHmax have good connectedness, making a significant contribution in hydrocarbon production. In addition, the oil‐bearing layers are mainly associated with the intervals with open fractures. The presence of natural fractures provides important pore spaces and fluid flow conduits. Insights can be provided into the subsurface fracture (natural and induced) detection and characterization using image logs.
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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 | 0.000 | 0.000 |
| 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".