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Record W1972567118 · doi:10.2118/171561-pa

Thermal Recovery of Bitumen From the Grosmont Carbonate Formation—Part 2: Pilot Interpretation and Development Strategy

2014· article· en· W1972567118 on OpenAlexaffabout
Daniel Yang, Moslem Hosseininejad Mohebati, Steve Brand, Caralyn Bennett

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2014
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsLaricina Energy (Canada)
Fundersnot available
KeywordsSteam injectionCarbonateImbibitionSteam-assisted gravity drainagePetroleum engineeringAsphaltDrainageGeologySaturation (graph theory)Carbonate rockOil sandsMaterials science

Abstract

fetched live from OpenAlex

Summary Bitumen production from the Grosmont formation is enabled by bitumen-viscosity reduction caused by heating with steam, and is driven by three processes: thermal expansion, gravity drainage, and spontaneous imbibition. Gravity drainage is the dominant recovery mechanism. Maintaining a balance of injected and produced fluid is indicative of good performance. The projected steam/oil ratio (SOR) for the carbonate Grosmont formation is comparable to that of the clastic Clearwater formation; the impact of lower porosity is compensated by lower water saturation. On the basis of the experience from the pilot project, a followup development of the Grosmont formation relies on cyclic operation of injection and production. Saleski Phase 1, approved by the Alberta Energy Regulator, is designed for 1700-m3/d oil capacity from the Grosmont formation. For the first time, probable undeveloped reserves have been assigned to a fractured-carbonate bitumen reservoir. The cyclic-to-continuous steam-assisted-gravity-drainage drainage (C2C-SAGD) concept, where initial cyclic operation of individual wells is converted into continuous injection and production with well pairs as the reservoir depletion matures, intends to maximize recovery in future exploitation projects.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.829
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.008
GPT teacher head0.182
Teacher spread0.174 · 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 designOther design
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

Citations16
Published2014
Admission routes2
Has abstractyes

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