Effect of Hydrate Formation on the Gas Permeability of Turonian Low Cemented Reservoirs
Bibliographic record
Abstract
Summary Turonian reservoirs of the Western Siberia are one of the most promising hydrocarbon sources of the above senomanian sediments. However, gas reserves, associated with the Turonian deposits, are difficult to recover, due to the peculiarity of their composition, occurrence conditions and the properties variability course by possible hydrate formation. In this regard, the experimental modeling of the gas permeability of Turonian siltstones, characterized by low absolute permeability, under favorable conditions for hydrate formation were carried out. Experiments of gas filtration through moisture-saturated samples of Turonian siltstone under P-T conditions of the methane hydrate existence present gas permeability decreases caused by significant pore hydrate accumulation. In this study it was revealed that the hydrate accumulation and gas permeability of the Turonian siltstone is greatly influenced by their moisture content. So, with moisture saturation increasing from 57% to 75%, the total permeability naturally decreased, and the variations of gas permeability under conditions of hydrate formation were significantly reduced. It can be explaining by lower hydrate formation intensity caused by high degree of pore water saturation. The obtained results provide an opportunity to assess the optimal moisture content of Turonian siltstones, which causes the maximum gas permeability reduce by hydrate accumulation.
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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 | 0.000 | 0.000 |
| 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".