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Record W2735829355 · doi:10.2118/186102-pa

Use of Nickel Nanoparticles for Promoting Aquathermolysis Reaction During Cyclic Steam Stimulation

2017· article· en· W2735829355 on OpenAlexafffund
Siyuan Yi, Tayfun Babadagli, Huazhou Li

Bibliographic record

VenueSPE Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSteam injectionNickelAsphalteneNanoparticleChemical engineeringPenetration (warfare)Enhanced oil recoveryMaterials scienceEmulsionPetroleum engineeringChemistryPulp and paper industryMetallurgyNanotechnologyGeology

Abstract

fetched live from OpenAlex

Summary Late cycles of cyclic steam stimulation (CSS) are characterized by a decreasing heavy-oil recovery and an increasing water cut. Nickel nanoparticles can be used to promote aquathermolysis reactions between water and heavy oil in steam-injection processes, thereby increasing the recovery factor (RF). In this paper, detailed investigations were performed to determine the optimal operational parameters and answers to the following questions: What is the optimal concentration of nickel nanoparticles for promoting aquathermolysis under high steam temperature? Can we improve oil recovery at lower steam temperatures with the presence of nickel nanoparticles? What effect does the penetration depth of nickel nanoparticles have on the final oil recovery? CSS experiments were conducted between temperatures of 150 and 220°C. Steam generated under these temperatures was injected into sandpacks saturated with Mexican heavy oil. Powder-form nickel nanoparticle was introduced into this process to boost the oil production. In an attempt to obtain the optimal concentration, different concentrations were tested. Next, oil sands without any nanoparticle additives were first added into the cylinder. Then, only one-third of the sandpack was mixed with nickel nanoparticles near the injection port. Experiments were executed to study the effects of temperature, nickel concentrations, and nanoparticle-penetration depth on the ultimate oil recovery and produced oil/water ratios after each cycle. Produced-oil quality and emulsion formation were evaluated with gas-chromatography (GC) analysis, viscosity measurements, saturates/asphaltenes/resins/aromatics (SARA) tests, and microscopic analysis of the effluents. Experimental results show that the best concentration of nickel nanoparticles, which gives the highest ultimate oil RF, is 0.20 wt% of initial oil in place (IOIP) under 220°C, whereas the nickel concentration of 0.05 wt% provides the highest RFs at the early stages. A lower temperature of 150°C provides a much-lower RF than 220°C, which is mainly because of a lower level of aquathermolysis reactions at 150°C. By analyzing the compositions of produced oil and gas samples with GC and SARA tests, we confirm that the major reaction mechanism during the aquathermolysis reaction is the breakage of the carbon/sulfur (C/S) bond; the nickel nanoparticles can act as catalyst for the aquathermolysis reaction; and the catalytic effect becomes less remarkable from cycle to cycle. One run of the experiment to test the effect of particle-penetration depth revealed that the nickel nanoparticles distributed near the injection port greatly contributed to the ultimate RF.

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 score0.031
Threshold uncertainty score0.348

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 head0.029
GPT teacher head0.276
Teacher spread0.247 · 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

Citations54
Published2017
Admission routes2
Has abstractyes

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