A New Method for Modeling Bypassed Oil Recovery in EOS Compositional Simulation
Bibliographic record
Abstract
Abstract The conventional reservoir simulation uses the local equilibrium assumption, where fluids are completely mixed and at equilibrium within individual gridblocks. This assumption allows for no bypassing of oil at the sub-grid scale in gas injection simulation. Oil bypassing by gas, however, always occurs due to micro and macroscopic heterogeneities, gravity, and front instability. There are a few methods proposed in the literature that attempt to model the effects of sub-grid heterogeneity in gas injection simulation. The concept of miscible residual oil saturation (i.e., the Sorm method) excludes the immobile oil from flash calculations and explicitly models bypassed oil. However, the Sorm method cannot model bypassed oil recovery because it allows for no mass flux between the bypassed and flowing fractions. The alpha-factor method uses transport coefficients to adjust components’ flux in compositional simulation. It has been used to retain a desired amount of residual oil in history matching for predominantly single-phase flow. However, its applicability to partial miscibility conditions is not fully understood. This is likely because the presence of more phases with partial miscibility yields more non-linearity and severer non-uniqueness in the history matching process. In this research, we develop an efficient method to model bypassed oil recovery in multiphase compositional simulation. Oil bypassing in gas injection simulation is first explained using dual-porosity-flow (DPF) models. We show that DPF can capture the characteristics of the dispersion-capacitance model, where oil held up in the bypassed fraction gradually migrates to the flowing fraction through intra-block mass flux. Fluid flow in the presence of capacitance is characterized using the DPF parameters; the bypassed fraction, throughput ratio (RT), and Peclet number. A new fluid characterization method is then used to efficiently reproduce DPF characteristics using a single-porosity-flow (SPF) model. An EOS fluid model is corrected for capacitance by adding heavier oil components that are characterized depending on RT. A case study demonstrates successful application of the new method to reflect capacitance effects observed in corefloods in field-scale gas injection simulation. Prediction of residual oil distribution within a reservoir depends significantly on how bypassed oil is modeled in the simulation. Unlike the alpha-factor method, our method requires no changes in the governing equations and in 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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".