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Record W2015959087 · doi:10.2118/170037-ms

Induction and Radio Frequency Heating Strategies for Steam-Assisted Gravity Drainage Start-Up Phase

2014· article· en· W2015959087 on OpenAlex
Sahar Ghannadi, Mazda Irani, Rick Chalaturnyk

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSPE Heavy Oil Conference-Canada · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsSteam-assisted gravity drainageInjectorAsphaltPetroleum engineeringSteam injectionOil sandsInduction heatingNatural circulationDielectric heatingRadio frequencyEnvironmental scienceNuclear engineeringSteam drumBoiler (water heating)Superheated steamElectromagnetic coilEngineeringWaste managementMaterials scienceElectrical engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Steam-assisted gravity drainage (SAGD) is the method of choice to extract bitumen from Athabasca oil sand reservoirs in Western Canada. Bitumen at reservoir condition is immobile due to high viscosity and its saturation is typically large that limits the injectivity of a steam at in-situ condition. In a current industry practice, steam is circulated within injection and production wells. Operators keep the steam circulation till mobile bitumen breaks through the producer and communication is established between the injector and the producer. The "start-up" (or "circulation") phase is ranging between three to several months. A variety of processes are used to minimize time of start-up phase such as: electro-magnetic (EM) heating either induction (medium frequency) or radio frequency (RF) ranges. Knowing the hot-zone size formed by steam circulation and benefit of simultaneous EM-heating techniques help better understand the start-up process and how to minimize the start-up duration. The aim of the present work is to introduce an analytical model to predict start-up duration for only steam circulation and also for with EM-heating. The results obtained from this study reveal that induction slightly decrease start-up time for frequencies smaller than 10 kHz, and it can reduce start-up time to 30% of original steam circulation for 100 kHz frequency.

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.

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 score0.992
Threshold uncertainty score0.746

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 head0.035
GPT teacher head0.255
Teacher spread0.220 · 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