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Record W2124405555 · doi:10.2516/ogst/2013111

Towards the Development of Bitumen Carbonates: An Integrated Analysis of Grosmont Steam Pilots

2013· article· en· W2124405555 on OpenAlexafffundabout
C. C. Ezeuko, J. Wang, Michael S. Kallos, Ian D. Gates

Bibliographic record

VenueOil & Gas Science and Technology – Revue d’IFP Energies nouvelles · 2013
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCarbonateAsphaltSteam-assisted gravity drainageSteam injectionPetroleum engineeringOil sandsGeologyEnvironmental scienceEngineeringArchaeologyGeographyMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

The Grosmont Formation in Alberta, Canada is a highly fractured, karstified and vuggy bitumen-rich carbonate reservoir located west of and below the Athabasca oil sands deposit. The bitumen carbonate platform extends about 500 km in length and up to 150 km in width and contains an estimated 64.5 billion m<sup>3<sup/> (406.5 billion barrels) of oil. The Grosmont Formation is larger than the combined total of all other known carbonate bitumen deposits in the world. Here, we analyze early Grosmont steam pilots to improve the design of future pilots and commercial development of this massive bitumen deposit. In agreement with the conclusions of earlier analysis of these Grosmont pilots, they were reasonably successful considering the heterogeneous nature of the Grosmont Formation. Operational factors such as poor steam quality, non-optimized high injection pressures and completion issues appear to have heavily impacted recovery performances. Clearly, steam-based recovery operations have good potential for Grosmont, especially considering that it is mature commercial technology. Following an integrated analysis of early Grosmont pilots, we posit that Cyclic Steam Stimulation (CSS) using horizontal wells exhibits greater potential for the development of Grosmont carbonate, compared with Steam-Assisted Gravity Drainage technology (SAGD).

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.009
GPT teacher head0.215
Teacher spread0.206 · 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 designBench or experimental
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

Citations9
Published2013
Admission routes3
Has abstractyes

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