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Combined Steam–Air Flooding Studies: Experiments, Numerical Simulation, and Field Test in the Qi-40 Block

2016· article· en· W2286470569 on OpenAlexaff
Jian Yang, Xiangfang Li, Zhangxin Chen, Ji Tian, Xinguang Liu, Keliu Wu

Bibliographic record

VenueEnergy & Fuels · 2016
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
FundersNational Natural Science Foundation of China
KeywordsFlooding (psychology)Petroleum engineeringVolume (thermodynamics)Environmental scienceOil fieldSteam injectionSecondary air injectionComputer simulationWaste managementGeologyEngineeringSimulationThermodynamics

Abstract

fetched live from OpenAlex

Air has the characteristics of low-temperature oxidation (LTO) as well as improving drainage energy. Therefore, it can be co-injected with steam to enhance an oil recovery factor. This paper studies combined steam–air flooding for heavy oil recovery. Four experiments, including one steam flooding experiment and three combined steam–air flooding experiments, are studied. The volume ratios of air and steam in these three combined flooding experiments are 1:2, 1:1, and 2:1, respectively. The experimental results show that combined steam–air flooding can enhance the oil recovery factor when the volume ratio of air and steam is in a certain range. Furthermore, numerical simulation results show that its temperature and oil recovery tendency are similar to those from the experiments. In the numerical simulation study, an optimal volume ratio of air and steam is 1.5:2. Finally, a field test of combined steam–air flooding in the Qi-40 block of the Liaohe oil field is studied to validate the experimental and numerical results. Results show that air can be an effective additive that enhances heavy oil recovery in a steam flooding 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.251
Threshold uncertainty score0.388

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 head0.017
GPT teacher head0.276
Teacher spread0.259 · 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 designBench or experimental
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

Citations28
Published2016
Admission routes1
Has abstractyes

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