Vesicle distribution in basalt lava flow units in the Mesozoic rift basins of northeast China and its application in gas reservoir prediction
Bibliographic record
Abstract
The Mesozoic rift basins of northeast China are characterized by a significant proportion of basalt due to the progressive emplacement of basalt lava flows. The objective of this study was to construct vesicle distribution models of the basalt lava flow units, including conceptual geological models and thickness models, to understand the architecture of the basalt and the gas reservoir distribution. The conceptual geological models were constructed based on the characteristics of outcropping basalt lava flows, with supplemental seismic data used to extrapolate the lateral extents of large-scale basalt lava flows. The thickness models were constructed using data on the thicknesses of basalt lava flow units and vesicle zones. These data were obtained from 27 units in outcrop and 204 units interpreted from well logs in the basin. The conceptual geological models revealed that the shapes of the basalt lava flow units change from braided to tabular with increasing thickness and that their inner structures can be divided into three vesicle zones: the top vesicle zone, the massive core zone, and the base vesicle zone. The thickness models revealed that trends in the thickness of the top vesicle zone relative to the thickness of the basalt lava flow unit can be expressed using a piecewise function that can be separated into a linear function and a logarithmic function. Similarly, trends in the thickness of the massive core zone and the base vesicle zone relative to the basalt lava flow unit thickness can be expressed by a piecewise linear function. Vesicle distribution models provide an effective means of determining the proportion and distribution of vesicle zones in basalt with limited borehole data. We also constructed a reservoir model based on our vesicle distribution models, and this model revealed that suitable petroleum reservoirs are primarily located in the thinner braided lava flows.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".