Characterization of Multiphase Flow in CHOPS Processes Using a Systematic Framework
Bibliographic record
Abstract
Summary Cold heavy oil production with sand (CHOPS) technique has successfully improved oil recovery from heavy oil reservoirs due to high permeability channels resulted from sand failure and foamy oil flow enhancing heavy oil movability. However, impacts of the sand failure and the foamy oil flow on multiphase fluid flow features are still not properly understood. In this paper, an effective and systematic framework has been proposed to characterize the multiphase fluid flow and determine the three-phase relative permeability of CHOPS processes. A recently developed sand failure criterion and a relative permeability model have been integrated with a reservoir simulator to simulate the CHOPS process. The unknown parameters involved in the systematic framework are intelligently determined by an iterative ensemble smoother (IES) algorithm, which also enables sensitivity analysis on CHOPS production profiles. Subsequently, the proposed framework is tested through a laboratory CHOPS experiment. We obtained not only fairly history-matched production data (i.e., cumulative oil, gas, and sand production), but also satisfactorily converged three-phase relative permeability curves by iteratively assimilating production profiles of the CHOPS experiment. It has also been found that using two sets of three-phase relative permeability is capable of representing the segment-type nature of the multiphase flow and reproducing the transition stage on the production profiles. Furthermore, the comparison between two sets of three-phase relative permeability indicates that the sand failure phenomenon yields a reduced residual oil saturation and an increased oil/gas relative permeability. In addition, the sensitivity analysis demonstrates that the segment-type nature of production profiles results from the variation of key impact factors dominating the multiphase fluid flow in CHOPS processes. Overall, the proposed systematic framework can not only reproduce the physical phenomena of sand failure and foamy oil, but also provide a convenient tool to characterize the multiphase flow in CHOPS processes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".