Conglomerate petrology characterization using high-definition borehole electrical images in the Upper Urho Formation at well JL42, Zhongguai Uplift, Junggar Basin, China
Bibliographic record
Abstract
Abstract Cores drilled from wells are significant resources for understanding the geologic characteristics of petroleum reservoirs. However, due to the high cost and long rig time involved, it is impossible to obtain cores from the entire sedimentary formation in a drilling well. Furthermore, core breakage limits the amount of information that can be obtained in the vertical deposition environment of deep-buried formations. Therefore, we have used ultrahigh-resolution and high-quality borehole electrical images obtained by a borehole electrical imaging tool, High-Definition Formation MicroImager (FMI-HD), to supplement “core” information and characterize the petrologic features, such as grain size and sedimentary structure, of conglomeratic formations in the Permian Upper Urho Formation at well JL42, Zhongguai Uplift, Junggar Basin, China. We have observed conglomeratic cores at 95.92 m in well JL42 and recorded various petrologic features of the core cylinders. In the cored interval, the FMI-HD images were compared with core photos in detail; grain size results from the FMI-HD images and cores were very similar. However, there were major differences in the structural results due to core interruption. In addition, the high-resolution depositional facies of the Upper Urho Formation at well JL42 were dissected in terms of the distributive fluvial system, not the fan-delta system, using vertical grain size features derived from FMI-HD images. Boulders, cobbles, coarse pebbles, and fine pebbles were developed in thick gravelly channels in the lower proximal facies, whereas fine pebbles and granules were developed in thin channels in the upper medial facies. Therefore, FMI-HD images can be efficiently used to supplement cores and sedimentary information, which provides important insights on the paleogeology of conglomeratic formations and in turn on the exploration potential of petroleum systems.
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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 | 0.001 | 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".