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Record W2038654684 · doi:10.3720/japt.71.186

Oil Sands Development in Canada with the SAGD Technology-Past, Present and Future-

2006· article· en· W2038654684 on OpenAlexaboutno aff
Yasuhiko Wada

Bibliographic record

VenueJournal of the Japanese Association for Petroleum Technology · 2006
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsOil sandsAsphaltPetroleum engineeringUnconventional oilSteam-assisted gravity drainageDrillingProduction (economics)Oil reservesEngineeringPetroleum industryResource (disambiguation)Fossil fuelEnvironmental sciencePetroleumGeologyWaste managementEnvironmental engineeringComputer scienceGeography

Abstract

fetched live from OpenAlex

The amount of proven reserves of oil sands in Canada is estimated to be 175 billion barrels, placing the deposit second in the world in size. Even if the production increases to the same peak level as the North Sea, the R/P ratio is still 80 years, showing the richness of the oil sands resource. The current combination of, the advantageous location adjacent to the extensive U.S. market, with established infrastructure for transportation, the decrease of development and production costs as a result of improved technology, and the recent rise in oil prices are attracting many investors to oil sands development. Canada Oil Sands Co., Ltd. (CANOS), through its subsidiary JACOS, started the pursuit of commercial bitumen production in 1978. After decades of technical trials, CANOS has implemented the SAGD method in 1997 and started production in 1999. It is currently producing 8,000 to 9,000 b/d of bitumen. Throughout its operation history, CANOS has faced many technical challenges and significant advances have been made. These challenges include; ·Application of horizontal well drilling technology. ·Proving the efficiency of the SAGD mechanism. ·Improving G&G evaluation accuracy using sequence stratigraphic framework and 3D seismic data. ·Improving reservoir evaluation quality with the help of sector models and 3D simulation models. ·Optimization of operations including improvement of steam-oil ratio. ·Analysis of volatile pricing of diluted bitumen. SAGD is a new innovative technology and requires the integration of various technical fields. With continuing room for improvement, SAGD offers challenging but worthwhile opportunities for geoscientists and petroleum engineers. The attractive investment environment resembles that of the North Sea development in its early days. In the North Sea, Japanese companies were at a disadvantage as late-comers. CANOS, on the other hand is, and plans to stay on the leading edge of Canadian oil sands development.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.830
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.003
GPT teacher head0.185
Teacher spread0.182 · 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 designSimulation or modeling
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

Citations3
Published2006
Admission routes1
Has abstractyes

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