Interpretation of charging phenomena based on reservoir fluid (PVT) data
Bibliographic record
Abstract
Abstract Molar concentration profiles of reservoir fluids reveal accumulation history and alteration. Slope Factors (SF) define rates of exponential decrease in concentration of light n -alkanes (C 3 - n C 5 ) and liquid pseudo-components (P 10+ ) with increasing carbon number. Most petroleum fluids are substantially characterized by SF(C 3 - n C 5 ) and SF(P 10+ ), the former invariably being the greater. Specific paired values of SF(C 3 - n C 5 ) and SF(P 10+ ) are process-diagnostic. In oils, maturation, gas injection, evaporative fractionation and migration depletion involving loss of gas, are recognizable. SF data are interpreted in the light of PVT analyses representing oils (principally from western Canada) and gas-condensates (from numerous basins), also asphaltene pyrolysis experiments and equation of state calculations. Covariant increase of SF(C 3 - n C 5 ) and SF(P 10+ ) during maturation is demonstrated, but correlation is frequently destroyed by modification of the light ends by the admixture of allochthonous gas, increasing only SF(C 3 - n C 5 ). Secondary gas-enrichment is a requisite process for the generation of gas-condensates by evaporative fractionation. Compositional criteria for the recognition of enrichment are provided for the first time, particularly attainment of a value of SF(C 3 - n C 5 ) exceeding 1.69 (a tentative limit). Available data indicate that the process has occurred in a large proportion of oil accumulations, ranging from 23% of 30 reservoirs in the Jurassic Smackover Formation in Alabama, to 78% of 36 in the northern North Sea.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".