MétaCan
Menu
Back to cohort
Record W4404896198 · doi:10.1016/j.geoen.2024.213554

Characterization and multiphase flow of Oil/CO2 systems in porous media focusing on asphaltene precipitation: A systematic review

2024· review· en· W4404896198 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueGeoenergy Science and Engineering · 2024
Typereview
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCharacterization (materials science)Porous mediumAsphalteneMultiphase flowPetroleum engineeringPrecipitationPorosityMaterials scienceFlow (mathematics)Reservoir modelingChemical engineeringGeologyEnvironmental scienceGeotechnical engineeringMechanicsNanotechnologyMeteorologyEngineeringPhysics

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.152
Threshold uncertainty score0.897

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.254
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it