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Record W2071776283 · doi:10.1504/ijogct.2015.068994

Performance of immiscible and miscible CO<SUB align="right">2 injection process in a tight carbonate reservoir (experimental and simulation approach)

2015· article· en· W2071776283 on OpenAlexaff
Ali Abedini, Farshid Torabi, Nader Mosavat

Bibliographic record

VenueInternational Journal of Oil Gas and Coal Technology · 2015
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsMiscibilityPetroleum engineeringVolumetric flow rateEnhanced oil recoveryCarbonateRelative permeabilityMaterials scienceTight oilPermeability (electromagnetism)ChemistryThermodynamicsGeologyComposite materialEngineeringWaste management

Abstract

fetched live from OpenAlex

In this study, the technical feasibility of CO2 injection in a core sample of a tight carbonate reservoir was investigated under various operating conditions. First, the minimum miscibility pressure (MMP) of CO2/oil system was determined to be 1,907 Psia. The effects of the operating pressure as well as the injection flow rate of CO2 on the oil recovery were examined. The results showed that at the operating pressures far below the MMP, the ultimate oil recovery factor is considerably low and increases as the pressure approaches near-miscible condition. When injection pressure was increased to 2,000 Psia, the ultimate oil recovery factor reached 0.81 and further increase in injection pressure did not significantly improve the recovery factor. For injection pressures lower than MMP (i.e., immiscible condition), increasing injection flow rate of CO2 resulted in lower oil recovery, while at the pressures near and above the MMP, the ultimate oil recovery was much less dependent on CO2 injection flow rate. All the test results were simulated using the CMG package, ver. 2011 and attempt was made to history match the experimental results. In this process, relative permeability curves were used as matching parameter. The simulation results were in good agreement with experimental values. [Received: March 12, 2013; Accepted: August 4, 2013]

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.372

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 head0.010
GPT teacher head0.266
Teacher spread0.256 · 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

Citations25
Published2015
Admission routes1
Has abstractyes

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