Three-phase Hydrocarbon Thermodynamic Liquid-Liquid-Vapour Equilibrium in CO2 Process
Bibliographic record
Abstract
Abstract Hydrocarbon phase behaviour must be rigorously represented when there is a need to properly account for mass transfer between phases in a porous medium. The overly simplified black-oil formulation, although appropriate for primary depletion and waterflooding, provides inadequate representation of miscible displacement processes. As a result, compositional simulation has evolved to provide thermodynamically consistent means to accurately describe the phases and compositions present within the porous reservoir rocks. Compositional simulators have become essential modelling tools for CO2 processes in the Petroleum Industry. Advances in computational power have encouraged the development of meaningful improvements and refinements that were not possible until very recently. CO2 injection into an oil reservoir at low temperatures causes the appearance of a three-phase hydrocarbon thermodynamic Liquid-Liquid-Vapour (LLV) equilibrium. The traditional use of a two-phase flash calculation in this three-phase region may lead to instability problems. Besides, commercial compositional simulators normally do not consider two-phase hydrocarbon Liquid-Liquid (LL) thermodynamic equilibrium that appears in oil reservoirs at low temperatures in the presence of CO2. Instead, it is treated as a Liquid-Vapour (LV) thermodynamic equilibrium and the fluid flux behaviour is not well represented. A compositional simulator must be able to represent adequately the LL hydrocarbon thermodynamic equilibrium when it is present in order to rigorously model the reservoir phase behaviour in the presence of CO2. A novel procedure has been developed to overcome instabilities which may arise in calculation of multiphase liquid-liquid-vapour (LLV) hydrocarbon phase equilibrium. In addition, a new procedure has been developed for representing the thermodynamic liquid-liquid hydrocarbon equilibrium in a compositional simulator. This new procedure represents the real behaviour of the fluid flux. It is more rigorous than the traditional approach of lumping of the two liquid phases into a pseudo single liquid phase or as a liquid-vapour (LV) thermodynamic equilibrium. The results of this implementation are presented and analyzed in detail.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".