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Record W2398730894 · doi:10.2118/180700-ms

Unlocking Potential Of The Lower Grand Rapids Formation, Western Canada: The Role Of Sand Control and Operational Practices in SAGD Performance

2016· article· en· W2398730894 on OpenAlexaboutno aff
H.J. Williamson, A.. Babaganov, Uliana Romanova

Bibliographic record

VenueSPE Canada Heavy Oil Technical Conference · 2016
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsOil sandsSteam-assisted gravity drainagePetroleum engineeringAsphaltGeologyPetrographyWork (physics)Steam injectionPetroleum industryUnconventional oilOil fieldFossil fuelMining engineeringGeochemistryArchaeologyEngineeringGeographyWaste managementPaleontology

Abstract

fetched live from OpenAlex

Abstract Grand Rapids is one of the major oil sand formations in Western Canada. As in-situ production of bitumen using Steam Assisted Gravity Drainage (SAGD) continues to grow, oil sands in the Grand Rapids formation is beginning to attract more industry attention. This paper focuses on the performance of two well pairs at the Blackrod SAGD Pilot Project operated by BlackPearl Resources Inc. The geology of the Lower Grand Rapids formation, sand control selection, and operational practices will be discussed using both laboratory and field data. Petrographic data of oil sand core before and after steam injection and the potential thermal formation damage mechanisms are discussed. Differences in the performance and the key learnings of the two well pairs are presented. The findings of the work are to be implemented during the next phase of the project and are thought to be useful to other operators developing oil sands located in the Grand Rapids formation or similar oil sand and heavy oil deposits.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.006
GPT teacher head0.193
Teacher spread0.187 · 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 designObservational
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

Citations17
Published2016
Admission routes1
Has abstractyes

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