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Record W2039413040 · doi:10.2118/150169-ms

Pressure Maintenance and Improving Oil Recovery with CO2 Injection in Heavy Oil Reservoirs

2011· article· en· W2039413040 on OpenAlexafffund
Sixu Zheng, Huazhou Li, Daoyong Yang

Bibliographic record

VenueSPE Heavy Oil Conference and Exhibition · 2011
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Regina
FundersNatural Sciences and Engineering Research Council of CanadaPetroleum Technology Research Centre
KeywordsPetroleum engineeringOil in placeInjectorResidual oilOil productionSaturation (graph theory)Water injection (oil production)Environmental scienceGas oil ratioEnhanced oil recoveryOil wellResidualFossil fuelMaterials sciencePetroleumGeologyWaste managementEngineeringComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

Abstract In this paper, techniques have been developed to experimentally evaluate performance of CO2 injection in heavy oil reservoirs for pressure maintenance purpose. More specifically, a three-dimensional (3D) physical model consisting of five vertical wells and three horizontal wells is used to examine effect of well configurations on pressure maintenance and oil recovery with CO2 injection in heavy oil reservoirs. The initial oil saturation, oil production rate, water cut, gas-oil ratio, ultimate oil recovery, and distribution of residual oil saturation are examined under various well configurations, which can be optimized to maximize heavy oil recovery when CO2 injection is employed for pressure maintenance purpose. The well configuration with a horizontal producer plus four vertical injectors is found to achieve a better performance than the conventional five-spot well configuration, while the oil recovery is experimentally determined to be 15.7% of OOIP during CO2 injection process.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.944
Threshold uncertainty score0.956

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 communication0.0000.001
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 head0.016
GPT teacher head0.209
Teacher spread0.193 · 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 designOther design
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

Citations10
Published2011
Admission routes2
Has abstractyes

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