Bibliographic record
Abstract
Abstract Gas phase formation in supersaturated live heavy oil occurs by bubble nucleation and growth. Modeling the dynamics of these processes in cold production is essential, since they are key mechanisms in determining oil recovery. The greatest challenge in field scale simulations of cold production is to quantify the spatial and temporal changes of the gas exsolution and transport processes. This paper describes a new kinetic model which, when coupled with a thermal reservoir simulator, can simulate the dynamics of gas exsolution and transport processes in a heavy oil reservoir. In this model, two relatively simple types of mass transfer equations predict bubble nucleation and growth in a live heavy reservoir. The model structure and parameters were investigated in comparison with a previously published model. The capability of the proposed kinetic model to handle the dynamics of gas phase formation in a heavy oil reservoir was explored in two sets of laboratory experimental data. In set 1, numerical history matches of pressure data were performed for eight constant withdrawal rate experiments. In set 2, numerical history matches of oil and gas production data were performed for four pressure depletion experiments. A close agreement was achieved between numerical simulation and experimental results. The model can be applied in the field scale simulations of cold production to predict gas exsolution and gas builds up in an oil reservoir.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".