Characterization and multiphase flow of Oil/CO2 systems in porous media focusing on asphaltene precipitation: A systematic review
Bibliographic record
Abstract
CO 2 injection is a well-known and highly efficient enhanced oil recovery (EOR) technique. In this method, undesirable asphaltene precipitation and deposition may occur at upstream and downstream facilities. Inhibition, controlling, and mitigating the asphaltene precipitation phenomenon are important steps to optimize the design and operation of this recovery process. Therefore, studying various physicochemical and thermodynamic properties as well as characterization methods of Oil/CO 2 /Asphaltene mixtures in porous and pipeline systems are of great interest to petroleum industry . Predicting and controlling the rheology and phase behavior of a multiphase system can be achieved by experimental and modeling studies. Various asphaltene precipitation and deposition experiments have been implemented for wide ranges of pressure, temperature, and composition in different media to evaluate asphaltene precipitation envelope, asphaltene precipitation amount, the effect of thermodynamic and textural properties on asphaltene precipitation/deposition phenomena, and associated formation damage. In addition to the experimental studies, a large number of modeling studies have been focused on transport phenomena through porous media while experiencing asphaltene precipitation/deposition problems. This review paper aims to comprehensively study the properties and characterization methods of Oil/CO 2 /Asphaltene systems. Moreover, a brief review of the previous experimental and modeling studies of Oil/CO 2 /Asphaltene systems is provided, focusing on various asphaltene precipitation and deposition models and mechanisms. Future research should focus on developing novel multi-scale experimental techniques that better simulate realistic reservoir conditions, along with advanced hybrid predictive models that combine effective approaches such as machine learning and molecular dynamics to more accurately capture asphaltene behavior in complex systems. The outcomes of the present study confirm that CO 2 injection can be an efficient technique for inhibiting asphaltene precipitation and deposition during EOR methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".