<scp>CFD</scp> simulations of a bubble column containing enhanced oil recovery chemicals
Bibliographic record
Abstract
Abstract Chemical enhanced oil recovery (EOR) methods return produced water containing polymers and surfactants which poses a water treatment challenge at offshore facilities. The present work shows that numerical simulations of gas‐water systems containing these chemicals remain challenging. Computational fluid dynamics (CFD) was used to predict gas holdup in a laboratory bubble column containing brine and EOR chemicals. The synthetic produced water was treated as non‐Newtonian in the simulations to match the experimentally‐determined physical properties. Two‐ and three‐dimensional numerical simulations were performed, and the latter were shown to be more appropriate through statistical analysis. Three classical drag models were assessed, and the results indicated that none of them could account for the high liquid viscosities and low surface tensions encountered in the system. A modification to the drag model of Tomiyama et al. (1988) was proposed to account for drag increases at low bubble Reynolds numbers when liquid apparent viscosity is high and surface tension is low. The importance of considering the interaction between apparent viscosity and surface tension was also shown through statistical analysis. CFD predictions of gas holdup showed that the proposed drag modification reduced errors from approximately 30% to less than 10% when compared to the experimental data. Radial profiles of the axial liquid velocity were also assessed and are apparently related to gas holdup prediction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".