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Record W2903822843 · doi:10.1115/1.4042238

Low Salinity Hot Water Injection With Addition of Nanoparticles for Enhancing Heavy Oil Recovery

2018· article· en· W2903822843 on OpenAlex
Yanan Ding, Sixu Zheng, Xiaoyan Meng, Daoyong Yang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Energy Resources Technology · 2018
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of CalgaryUniversity of Regina
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEnhanced oil recoveryResidual oilNanoparticleWater injection (oil production)Materials scienceSalinityWater floodingPetroleum engineeringOil in placeSurface tensionChemical engineeringSaturation (graph theory)Steam injectionWettingOil productionEnvironmental scienceChemistryComposite materialNanotechnologyPetroleumThermodynamicsGeology

Abstract

fetched live from OpenAlex

In this study, a novel technique of low salinity hot water (LSHW) injection with addition of nanoparticles has been developed to examine the synergistic effects of thermal energy, low salinity water (LSW) flooding, and nanoparticles for enhancing heavy oil recovery, while optimizing the operating parameters for such a hybrid enhanced oil recovery (EOR) method. Experimentally, one-dimensional displacement experiments under different temperatures (17 °C, 45 °C, and 70 °C) and pressures (about 2000–4700 kPa) have been performed, while two types of nanoparticles (i.e., SiO2 and Al2O3) are, respectively, examined as the additive in the LSW. The performance of LSW injection with and without nanoparticles at various temperatures is evaluated, allowing optimization of the timing to initiate LSW injection. The corresponding initial oil saturation, production rate, water cut, ultimate oil recovery, and residual oil saturation profile after each flooding process are continuously monitored and measured under various operating conditions. Compared to conventional water injection, the LSW injection is found to effectively improve heavy oil recovery by 2.4–7.2% as an EOR technique in the presence of nanoparticles. Also, the addition of nanoparticles into the LSHW can promote synergistic effect of thermal energy, wettability alteration, and reduction of interfacial tension (IFT), which improves displacement efficiency and thus enhances oil recovery. It has been experimentally demonstrated that such LSHW injection with the addition of nanoparticles can be optimized to greatly improve oil recovery up to 40.2% in heavy oil reservoirs with low energy consumption. Theoretically, numerical simulation for the different flooding scenarios has been performed to capture the underlying recovery mechanisms by history matching the experimental measurements. It is observed from the tuned relative permeability curves that both LSW and the addition of nanoparticles in LSW are capable of altering the sand surface to more water wet, which confirms wettability alteration as an important EOR mechanism for the application of LSW and nanoparticles in heavy oil recovery in addition to IFT reduction.

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.

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 score0.063
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.005
GPT teacher head0.204
Teacher spread0.199 · 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