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Record W2061169088 · doi:10.2118/2007-096

Detection of the Onset of Asphaltene Precipitation in a Heavy Oil-Solvent System

2007· article· en· W2061169088 on OpenAlexafffundabout
Peng Luo, Nattawan Kladkaew, Yongan Gu, Chintana Saiwan

Bibliographic record

VenueCanadian International Petroleum Conference · 2007
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsPetroleum Technology Research CentreUniversity of Regina
FundersNatural Sciences and Engineering Research Council of CanadaPetroleum Technology Research CentreUniversity of Regina
KeywordsAsphaltenePrecipitationSolventPetroleum engineeringChemistryChemical engineeringMaterials scienceOrganic chemistryGeologyEngineeringPhysicsMeteorology

Abstract

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Abstract When a hydrocarbon solvent is made in contact with a heavy oil under a sufficiently high reservoir pressure, asphaltene precipitation occurs so that the heavy oil is in-situ upgraded during a solvent-based heavy oil recovery process. Some physicochemical properties of this in-situ upgraded heavy oil are rather different from those of the original crude oil in the heavy oil reservoir. In this paper, a series of saturation tests is conducted for a heavy oil-propane system under different saturation pressures in a see-through windowed high-pressure saturation cell with six sampling ports at different vertical locations. The onset of asphaltene precipitation is determined by measuring and comparing several physicochemical properties (e.g., the solubility, oil-swelling factor, density, viscosity, and asphaltene content) of the propane-saturated and flashed-off heavy oils taken from different parts of the propanesaturated heavy oil under each saturation pressure. It is found that when the heavy oil is saturated with propane at P ? 780 kPa, the respective properties of the solvent-saturated and flashed-off heavy oils taken from the upper and lower parts are different to large extents. This may be because asphaltene aggregation occurs in the solvent-saturated heavy oil and some heavy components move downward to the bottom of the saturation cell. If the saturation pressure is increased to P=850 kPa, asphaltene precipitation occurs and some large asphaltene particles are deposited onto the acrylic windows of the saturation cell. Although the physicochemical properties of the solvent-saturated and flashed-off heavy oils measured by using different experimental methods show variable sensitivities to the asphaltene precipitation, its onset can be successfully detected in practice. Introduction Western Canada has tremendous heavy oil and bitumen deposits with estimated original-oil-in-place (OOIP) of 2.5 trillion barrels[1]. They hold great potential to meet the future hydrocarbon fuel demand, while the conventional petroleum reserves are being depleted. Nevertheless, how to effectively and economically recover heavy oil and bitumen remains a technical challenge due to their extremely high viscosities. At present, thermal-based heavy oil recovery methods are often applied because they can dramatically reduce the heavy oil viscosity. However, large heating and water source requirements, heat losses to thin oil formations, and water treatment cost make these tertiary oil recovery methods ineffective and uneconomical. Solvent-based heavy oil recovery processes[2–7] have recently gained more and more attention because of their distinct advantages over the thermal-based heavy oil recovery methods. In a typical solvent-based heavy oil recovery process, such as vapour extraction (VAPEX) process, gaseous condensable solvents[8], together with non-condensable carrier gases[9], are injected and dissolved into the heavy oil to dramatically reduce its viscosity. In some cases, the heavy oil viscosity reduction due to sufficient solvent dissolution may be comparable to that achieved by applying the thermal-based heavy oil recovery methods. Another major advantage of the solvent-based heavy oil recovery processes is asphaltene precipitation through sufficient solvent dissolution so that the heavy oil in the reservoir is insitu upgraded. The precipitated asphaltenes are deposited onto the sand grains and thus left behind in the reservoir. The produced heavy oil has a much lower viscosity and better quality in comparison with the original crude heavy oil[10, 11].

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 score0.213
Threshold uncertainty score0.959

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 communication0.0000.000
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 head0.012
GPT teacher head0.235
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.

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

Citations8
Published2007
Admission routes3
Has abstractyes

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