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Record W2070285759 · doi:10.2118/88641-ms

CO2 Recovery and Utilization for EOR

2004· article· en· W2070285759 on OpenAlexaff
Kamal Morsi, John Leslie, Doug MacDonald

Bibliographic record

VenueAbu Dhabi International Conference and Exhibition · 2004
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsSNC-Lavalin (Canada)
Fundersnot available
KeywordsFlue gasNatural gasEnhanced oil recoveryProcess engineeringEnvironmental scienceBooster (rocketry)Petroleum engineeringComputer scienceEngineeringWaste management

Abstract

fetched live from OpenAlex

Abstract In order to replace valuable natural gas presently used for pressure maintenance ADNOC is investigating capture of carbon dioxide (CO2) from several flue gas sources and its transport to a developed field for Enhanced Oil Recovery (EOR). A study was commissioned to evaluate available technologies and determine the optimum technical and economic configuration for capture and delivery of high quality CO2 for injection. Three flue gas sources for CO2, were considered, appropriate capture technologies were chosen and a formal licensor selection process was performed. Key considerations in selection of technology and licensors were existence of successful commercial applications and utilities requirements (natural gas, power and cooling water). The process configuration chosen involves placing the absorber equipment near the flue gas sources, with a common stripping operation located at a single site. The study also included design of compression and pipeline facilities to deliver dense-phase CO2 to the nominated field, and booster pumps to raise the CO2 to required injection pressure. The paper describes the process employed to select technologies and discusses the considerations involved to arrive at the final configuration.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.049
GPT teacher head0.299
Teacher spread0.250 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations5
Published2004
Admission routes1
Has abstractyes

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