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Record W2089667114 · doi:10.2118/79011-ms

Liquid Addition to Steam for Enhancing Recovery (LASER) of Bitumen with CSS: Evolution of Technology from Research Concept to a Field Pilot at Cold Lake

2002· article· en· W2089667114 on OpenAlexaff
R.P. Leaute

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsImperial Oil (Canada)
Fundersnot available
KeywordsAsphaltDiluentSteam injectionViscosityProcess engineeringEnvironmental scienceOil fieldPetroleum engineeringMaterials scienceNuclear engineeringWaste managementEngineeringChemistryComposite material

Abstract

fetched live from OpenAlex

Abstract This paper describes the process of injecting a liquid (C5+) hydrocarbon as a steam additive in a CSS mode of operations. The process has been termed LASER, for "Liquid Addition to Steam for Enhancing Recovery". The process concept was first tested in a 3D physical model apparatus using Cold Lake bitumen. A sustained uplift in bitumen production was observed in later CSS cycles when compared to other tests conducted without liquid addition. Based on numerical simulations, these effects can be attributed to additional viscosity reduction of heated bitumen when contacted with solvent. For bitumen-diluent mixtures, Shuh’s method of viscosity prediction of bitumen with liquid hydrocarbons is adequate to make realistic viscosity predictions based on actual measurements. Field-scale simulations were used to support LASER performance trends from the physical model and establish the optimal timing for applying the technology in the field. The key recovery performance indicators for LASER technology are (1) bitumen uplift over continued CSS performance and (2) fractional recovery of the injected diluent. A field pilot has been designed based on expectations of (1) an improvement of 33% in the cycle Oil-Steam Ratio (OSR) and (2) diluent recovery of 66% using 6% v/v of diluent injection with steam. The pilot location was chosen based on various factors, including improved characterization of historical performance within and around the pilot location. This was achieved by developing a novel multivariate analysis technique to correlate current OSR field performance and reduce associated background noise. An extensive monitoring program has been developed for the pilot. This program is critical for developing a reliable characterization of the diluent recovery. Diluent injection began in April 2002, and the pilot is expected to last approximately 2 years, corresponding to the average length of CSS cycle 7 at Cold Lake.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.275
Teacher spread0.243 · 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 source (direct Gemma or distilled Codex), 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

Citations83
Published2002
Admission routes1
Has abstractyes

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