A New Way of Compositional Simulation Without Phase Labeling
Bibliographic record
Abstract
Abstract Current relative permeability models rely on phase labeling, and cannot accurately capture the effect of compositional variations on relative permeabilities and capillary pressures in enhanced oil recovery (EOR) processes. Discontinuities in flux calculations not only cause serious convergence and stability, but also affects the estimated recovery factor. We developed a fully compositional simulation model using an equation of state (EoS) for relative permeabilities to eliminate the unphysical discontinuities in flux functions caused by phase labeling. In addition, we extended our relative permeability EoS to three phases. The model can capture complex hysteresis effects on three-phase relative permeability. The tuned model is used for simulation of multi-cycle WAG injection. The approach allows for a new search scheme to improve initial estimates for flash calculation. The results show increased robustness of high-resolution compositional simulation for both front calculations (recovery estimates) and convergence of flash algorithms. This paper provides a novel way forward to develop a fully compositional reservoir simulation based solely on continuous and robust equation-of-state relative permeabilities. In addition, this paper provides a detailed analysis of the effects of discontinuous phase labeling on simulation performance and accuracy for 1-D and 2-D water-alternating-gas flooding and three-hydrocarbon-phase flow. The results demonstrate the significant benefits of using an EoS for relative permeabilities.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".