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Record W1982790935 · doi:10.2523/iptc-16795-ms

Cold Lake Commercialization of the Liquid Addition to Steam for Enhancing Recovery (LASER) Process

2013· article· en· W1982790935 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Petroleum Technology Conference · 2013
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsImperial Oil (Canada)
Fundersnot available
KeywordsAsphaltSolventEnvironmental scienceSteam injectionPetroleum engineeringOil sandsGreenhouse gasEnhanced oil recoveryWaste managementProcess (computing)Materials scienceGeologyChemistryEngineeringComputer scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The Cold Lake project, in Alberta, Canada, is one of the world's largest heavy oil in-situ thermal developments with oil production of about 24,000 m3/d (150 kB/d) from more than 4000 wells. The world class Cold Lake hydrocarbon resource is a bitumen deposit, featuring in-situ viscosities in excess of 100,000 cP. The high viscosity of Cold Lake bitumen severely limits steam injectivity below fracture pressure, necessitating the development throughout the 1970s and 1980s of a modified cyclic steam stimulation (CSS) recovery process. Imperial Oil and ExxonMobil are pursuing a research program to develop the next-generation of bitumen recovery processes that use hydrocarbon solvents as a mobilizing agent, reducing greenhouse gas (GHG) emissions relative to the current commercial recovery processes. Imperial and ExxonMobil are pursuing three processes: a solvent-only process known as Cyclic Solvent Process (CSP), a solvent assisted SAGD process and a solvent assisted CSS process known as Liquid Addition to Steam for Enhancing Recovery (LASER). LASER has the potential to increase oil recovery by more than 5% and reduce direct GHG emissions intensity by approximately 25%. This paper describes the first commercial application of LASER. The Cold Lake H trunk LASER project is to date the world's largest implementation of a thermal solvent recovery process, with injection of 297 km3 (1.87 million barrels) of solvent into the 240 well project. This paper describes the successful operation of this thermal solvent project over a six year period; including reservoir numerical simulation work to develop bitumen and solvent production forecasts, development of field solvent production measurement methods, and the recovery process learnings from the first cycle of LASER operations. Completion of the first commercial LASER cycle has demonstrated on a large scale the success of solvent addition as a means to increase thermal efficiency and oil production in a heavy oil thermal recovery operation.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score0.397

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 head0.260
Teacher spread0.248 · 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