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Record W4237317700 · doi:10.2118/2005-098

In Situ Upgrading of Heavy Oil in a Solvent- Based Heavy Oil Recovery Process

2005· article· en· W4237317700 on OpenAlexaffabout
Peikai Luo, Caiqian Yang, A.K. Tharanivasan, Yongan Gu

Bibliographic record

VenueCanadian International Petroleum Conference · 2005
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsProcess (computing)In situPetroleum engineeringSolventProcess engineeringEnvironmental scienceWaste managementComputer scienceChemistryEngineeringOrganic chemistryOperating system

Abstract

fetched live from OpenAlex

Abstract During a solvent-based heavy oil recovery process, such as vapor extraction (VAPEX), a condensable solvent is injectedinto a heavy oil reservoir. Solvent dissolution into heavy oil and possible asphaltene precipitation drastically reduce its viscosity so that the diluted heavy oil can flow towards a production well. In the past, several physical modeling studies have shown that the produced heavy oil has much less amount of heavy components than the original heavy oil. This phenomenon is often referred to as in-situ upgrading. In this paper, a series of laboratory experiments is conducted under reservoir conditions to quantify the in-situ upgrading of heavy oil due to the solventdissolution and asphaltene precipitation by using a pure solvent (propane) and a mixture solvent (70 mol% methane + 25 mol% propane + 3.5 mol% n-butane + 1.5 mol% iso-butane), respectively. It is found that after a solvent is made in contact with heavy oil at a relatively high pressure for a sufficiently long time, the solvent-heavy oil system at equilibrium state can be roughly divided into three different layers. The top layer is a solvent-enriched liquid phase, the middle layer comprises heavyoil with the dissolved solvent and the bottom layer mainly consists of heavy components. The solvent-heavy oil mixtures inthese three layers show rather different chemical and physical properties, such as solvent concentration, carbon number distribution and viscosity. The top layer has the highest concentrations of solvent and light components and the lowest viscosity of heavy oil even after its dissolved solvent is flashed off. The heavy oil in the middle layer has similar carbon number distribution to the original heavy oil. The bottom layerhas the lowest solvent concentration and the highest concentration of heavy components. The heavy oil in the bottom layer after its dissolved solvent is flashed off has much higherviscosity than the original heavy oil. These experimental results indicate that in a solvent-based heavy oil recovery process, the solvent-heavy oil mixture in the top and middle layers can berecovered because of its lower viscosity, whereas the heavy oil in the bottom layer may be left behind in the heavy oil reservoir because of its higher viscosity. In this way, the produced heavy oil is in-situ upgraded during the solvent-based heavy oil recovery process. Introduction Western Canada has tremendous heavy oil and bitumen Deposit[1, 2]. Approximately 70% to 80% of the original-oil-inplace (OOIP) remains unrecovered at the economic limit after the cold production[3]. Heavy oil contains a large portion of heavy components, which are the major reason for its high viscosity (>1,000 mPa?s) and low API gravity (<20 ° API gravity) [4]. Heavy oils and bitumen are highly viscous so that they cannot be recovered by using some conventional recovery techniques for medium and/or light oils. In practice, thermal methods are often used because they can dramatically reduce heavy oil viscosity. However, the majority of Canadian heavy oil reservoirs cannot be exploited economically by using thermal methods alone due to thin pay zones and/or bottom water aquifer[5].

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score1.000

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.252
Teacher spread0.237 · 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.

Study designSimulation or modeling
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

Citations7
Published2005
Admission routes2
Has abstractyes

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