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Record W2767687173 · doi:10.1021/acs.iecr.7b03819

Reservoir Simulation and Production Optimization of Bitumen/Heavy Oil via Nanocatalytic in Situ Upgrading

2017· article· en· W2767687173 on OpenAlexafffund
Ngoc Nguyen, Zhangxin Chen, Pedro Pereira Almao, Carlos E. Scott, Brij Maini

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2017
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaAlberta Innovates - Technology FuturesCMG Reservoir Simulation Foundation
KeywordsAsphaltVapor qualityEnvironmental scienceSteam injectionPetroleum engineeringSteam-assisted gravity drainageWaste managementHydrogen productionOil productionMaterials scienceHydrogenOil sandsChemistryEngineeringHeat exchanger

Abstract

fetched live from OpenAlex

This paper presents recent development of in situ upgrading technology (ISUT) for producing heavy oil and bitumen in which a mixture of catalyst, hydrogen, and vacuum residue are injected together with steam to improve oil quality by converting heavy oil components into lighter oil components. Consequently, the produced oil is upgraded and the oil recovery factor is increased while less steam is used, which results in lower capital and operational costs. This technology helps to reduce environmental impacts and greenhouse gas emissions. Numerical simulation of a steam-assisted gravity drainage (SAGD) well pattern was conducted to study the improvement of oil production by applying coinjection of steam and the ISUT mixture (ST-ISUT). The results show that ST-ISUT method can increase the oil recovery factor by 36% and lowers the requirement of steam by 50% in comparison with the conventional steam injection method, and the produced oil has much better quality.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.001
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 head0.071
GPT teacher head0.336
Teacher spread0.266 · 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

Citations29
Published2017
Admission routes2
Has abstractyes

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