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Record W1982881199 · doi:10.2118/137711-ms

An Innovative Cementing Solution for the Grosmont Carbonate

2010· article· en· W1982881199 on OpenAlexaffabout
S. E. Arseniuk

Bibliographic record

VenueCanadian Unconventional Resources and International Petroleum Conference · 2010
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsLaricina Energy (Canada)
Fundersnot available
KeywordsAsphaltLost circulationDrilling fluidPetroleum engineeringSteam-assisted gravity drainageCementOil fieldDrillingEngineeringOil sandsEnvironmental scienceMechanical engineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Abstract Alberta's oil sands contain an estimated 286 billion m3 (1.8 trillion barrels) of bitumen, including more than 64.5 billion m3 (406 billion barrels) in the Grosmont Carbonate. The Grosmont at Saleski represents a significant resource, which will be developed from pilot to commercial development. The operating company plans to recover bitumen from the Grosmont using solvent-cyclic, steam-assisted gravity drainage (SC-SAGD), beginning with a pilot in the Saleski field. The Grosmont has been noted for drilling challenges relating to lost circulation, including difficulty in cement placement. To address these challenges, lightweight, foamed, thermal cement was specifically engineered, tested, and qualified for use at the pilot. This blend addresses placement challenges and was designed to achieve the mechanical properties required for zonal isolation and well integrity throughout the life of the well. This paper discusses the design and development of the blend using finite-element analysis (FEA) software to evaluate blend suitability, laboratory testing results to confirm basic properties, installation in the pilot wells, and results using ultrasonic cement-inspection tools.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score0.993

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 head0.014
GPT teacher head0.222
Teacher spread0.208 · 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

Citations2
Published2010
Admission routes2
Has abstractyes

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