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Record W2032648128 · doi:10.2118/2004-270

Further Investigation of CO2 Based Vapex for the Recovery of Heavy Oils and Bitumen

2004· article· en· W2032648128 on OpenAlexaffabout
Khelifa Talbi, Brij Maini

Bibliographic record

VenueCanadian International Petroleum Conference · 2004
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAsphaltPetroleum engineeringWaste managementMaterials scienceEnvironmental scienceProcess engineeringComposite materialEngineering

Abstract

fetched live from OpenAlex

Abstract Vapour extraction (Vapex) has recently emerged as a cost effective and environmentally friendly recovery technique for the huge resources of heavy oils and bitumen available in Canada, USA, and Venezuela. The current version of Vapex relies on injection of light hydrocarbon gases for reducing the oil viscosity. The economic viability of this process is very sensitive to the cost of injected gases in relation to the selling price of the produced oil. One attractive option for reducing the cost of injected gases appears to be the use of CO2 as a major component of the injected solvent. This modification will utilize mixtures of CO2 and propane as the solvent instead of the currently popular mixtures of methane and propane. Since CO2 is significantly more soluble in heavy oils than methane, it is likely that such mixtures will provide greater reduction in viscosity compared to equivalent mixtures of methane and propane. In this work methane-propane & CO2-propane were investigated as solvents for Vapex process for in situ recovery of heavy oil and bitumen. Twelve laboratory experiments were performed with 2 types of oil (4500 m.Pa.s & 18600 m.Pa.s at 21 °C). These tests were performed in a partially scaled physical model at different operating pressures (200 to 600 psia) and were designed to compare the performance of methane based solvents with that of CO2 based solvents. The main conclusion from this study is that the CO2 based Vapex process is more cost effective and environmentally friendly than the conventional Vapex process. Introduction With the decline of the conventional oil reserves, a major thrust of oil industries throughout the word is on the exploitation of heavy oil and bitumen reserves. The magnitude of these resources worldwide is about six trillion barrels of oil in place, six times the total conventional reserves1, and may be the future source of energy. The majority of these resources are located in Venezuela, Canada and the United States2. In most cases, conventional recovery methods cannot be implemented in heavy oil and bitumen reservoirs due to the high viscosity, and lowdegree API gravities5 of the oil. This high viscosity rules out the primary production and even in lower viscosity reservoirs the primary recovery is less than 10% of the original oil in place (OOIP) 3,4. The viscosity of heavy oil and bitumen are strong functions of temperature and decrease drastically with increase in temperature. Therefore, thermal processes are the logical first option for such oils. With these processes namely, cyclic steam stimulation (CSS), in-situ combustion (ISC), steam-assisted gravity drainage (SAGD), etc., the viscosity is reduced by heating the reservoir. Steam Assisted Gravity Drainage (SAGD) 6,7process has gained tremendous popularity in the industry for its usefulness in producing high viscosity heavy oil and bitumen. In this process the heat is injected into the reservoir by injecting steam through a horizontal well; steam condenses at the oil interface and heats the oil. Consequently the viscosity is lowered and the hot oil drains down under the influence of gravity into another horizontal well located near the bottom of the formation.

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 score0.125
Threshold uncertainty score0.993

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 head0.018
GPT teacher head0.230
Teacher spread0.212 · 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
Published2004
Admission routes2
Has abstractyes

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