MétaCan
Menu
Back to cohort
Record W2073499541 · doi:10.2118/65520-ms

Christina Lake Thermal Project

2000· article· en· W2073499541 on OpenAlexaboutno aff
J. C. Suggett, Simon Gittins, Sung-Won Youn

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsSteam-assisted gravity drainageAsphaltOil sandsRange (aeronautics)PeatEnvironmental scienceEngineeringGeologyCivil engineeringArchaeologyGeography

Abstract

fetched live from OpenAlex

Abstract This paper provides a description of PanCanadian’s Christina Lake Thermal Project. PanCanadian is implementing a commercial steam assisted gravity drainage (SAGD) project to recover 7.5 to 9.0 API bitumen from the Athabasca oil sands in the McMurray Formation. The project is located in Northeast Alberta, approximately 130 km north of Lac La Biche and 170 km south of Fort McMurray, in township 76 range 6 W4M. The project will be built in three phases for total bitumen production of 70,000 barrels of bitumen per day. Phase 1 will produce 10,000 bpd, with Phases 2 and 3 producing 30,000 bpd each. This paper will outline PanCanadian’s reasons for taking a phased approach to the Christina Lake development. Dual well SAGD will be implemented with 500 to 750 m long horizontal well pairs. Phase 1 is expected to begin steaming operations in the fourth quarter of 2001. The timing of subsequent phases will depend on Phase 1 results, market conditions and project economics. In March, 2000 PanCanadian received Alberta Energy and Utility Board approval for the project.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.162
Threshold uncertainty score0.359

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.1070.014

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 head0.016
GPT teacher head0.262
Teacher spread0.246 · 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 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

Citations12
Published2000
Admission routes1
Has abstractyes

Explore more

Same topicReservoir Engineering and Simulation MethodsFrench-language works237,207