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Record W4393221953 · doi:10.1021/acsomega.3c09889

Combining Steam and Flue Gas as a Strategy to Support Energy Efficiency: A Comprehensive Review of the Associated Mechanisms

2024· review· en· W4393221953 on OpenAlexaff
R. Pérez, Laura Osma, Hugo Alejandro García Duarte

Bibliographic record

VenueACS Omega · 2024
Typereview
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
FundersEcopetrol
KeywordsFlue gasSteam injectionEnhanced oil recoveryPetroleum engineeringEnvironmental scienceEfficient energy useWaste managementProcess engineeringEngineering

Abstract

fetched live from OpenAlex

Conventional steam injection projects have long been an iconic process in the development of heavy oil reserves; nevertheless, they face significant challenges in terms of energy efficiency, environmental compliance, and economic viability. Factors such as oil price fluctuations, the imperative for an energy transition, and the push to reduce carbon footprints are hindering new or ongoing implementations of traditional steam injection technologies. In response to these challenges, hybrid methods, such as the combination of steam and flue gas, are emerging as an opportunity to optimize thermal processes to improve oil recovery, energy efficiency, and environmental sustainability and extend reservoir productivity life. Steam injection enhances oil recovery by reducing the viscosity of crude oil, improving oil mobility and facilitating its extraction. The utilization of flue gas in steam injection processes has a significant impact on oil recovery and energy efficiency, leveraging industrial byproducts. This not only lowers operating costs but also reduces environmental emissions, aligned with energy transition trends. Incorporating the flue gas into a steam-based process in heavy oil reservoirs has emerged as a promising thermally enhanced oil recovery strategy. This work presents a comprehensive review based on experimental, numerical, and field studies of hybrid steam and flue gas technology as an EOR process. The main recovery mechanisms associated with the process are analyzed. In addition, the laboratory equipment required for experimental evaluations is presented, and reservoir modeling, kinetic and compositional effects on reservoir fluids, and the reduction in heat losses in the steam injection process are discussed. Furthermore, field implementations are reviewed to evaluate lessons learned and experiences on an operative scale. The combination of steam and flue gas represents an opportunity for carbon utilization and geological carbon sequestration. This dual functionality underscores its potential to enhance oil recovery and address carbon-related environmental concerns.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.846
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.298
Teacher spread0.274 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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