Computing Gas in Place in a Complex Volcanic Reservoir in China
Bibliographic record
Abstract
Abstract Gas-bearing reservoirs of the YingCheng Group in the SongLiao Basin in northeastern China are hosted in a complex assemblage of volcanic rocks. These reservoirs present a large number of interpretation challenges that have made the evaluation of gas-in-place (GIP) problematic. A fit-for-purpose workflow was developed for one accumulation in this area to provide robust GIP estimates in support of a development decision. The workflow involves a number of novel techniques developed to address the challenges presented by these reservoirs. Rock typing was conducted through integration of core descriptions with neutron capture spectroscopy, nuclear magnetic resonance logs, and borehole images using a neural network approach. These rock types, characterizing variations in chemistry and rock texture, were then propagated in a geocellular model using multivariate seismic attribute analysis and distribution rules based on volcanic analogues. Porosity and water saturation from an innovative petrophysical interpretation methodology were propagated throughout the model based on these rock types. The distribution of both rock types and petrophysical properties was performed stochastically and a range of potential GIP estimates was developed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".