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Record W2072109860 · doi:10.2118/07-09-03

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

2007· article· en· W2072109860 on OpenAlexaffabout
Peng Luo, C. H. Yang, Asok Kumar Tharanivasan, Yongan Gu

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2007
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsPetroleum Technology Research CentreUniversity of Regina
FundersSichuan UniversityHunan University
KeywordsSolventDissolutionAsphalteneChemistryLight crude oilButaneHeptaneHydrocarbonChemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract In this paper, a series of laboratory experiments are conducted under reservoir conditions to quantify the in situ upgrading of heavy oil due to the solvent dissolution and asphaltene precipitation by using a pure solvent (propane) and a solvent mixture (70 mol% methane + 25 mol% propane + 3.5 mol% n-butane + 1.5 mol% iso-butane). It is found that after a solvent is placed in contact with heavy oil at a relatively high pressure for a sufficiently long time, the heavy oil-solvent system at equilibrium state can be roughly divided into three different layers. The top layer is a solvent-enriched oil phase, the middle layer comprises heavy oil with the dissolved solvent and the bottom layer mainly consists of heavy components. The solvent-saturated heavy oils in these three layers have rather different physicochemical properties, such as the 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 layer has 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 higher viscosity than the original heavy oil. These experimental results indicate that in a solvent-based heavy oil recovery process, the solvent-saturated heavy oil in the top and middle layers can be recovered 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 deposits(1). Approximatelzy 70 to 80% of the original-oil-in-place (OOIP) remains unrecovered at the economic limit after cold production(2). Heavy oil contains a large portion of heavy components, which are the major reason for its high viscosity (>100 mPa •s) and low API gravity (< 20 °API)(3). 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 effectively and economically by using thermal methods alone due to thin pay zones and/or bottomwater aquifers. Also, low thermal conductivity, high water saturation and large heat losses to the overburden and underburden formations are among the major technical problems associated with the thermal methods(4). In the past, a number of experimental and numerical studies have been conducted to explore the potential of non-thermal recovery methods for heavy oil reservoirs. Solvent-based processes, such as vapour extraction (VAPEX)(5–8), are among the most promising heavy oil recovery techniques. In the VAPEX process, for example, gaseous light hydrocarbon solvents(9) or their mixtures together with non-condensable gases(10) are used to extract heavy oils and bitumen from the reservoir formations.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.503
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0100.002
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 head0.006
GPT teacher head0.229
Teacher spread0.223 · 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 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

Citations47
Published2007
Admission routes2
Has abstractyes

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