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Record W1978661265 · doi:10.2118/07-01-03

The Mechanisms of Electrical Heating For the Recovery of Bitumen From Oil Sands

2007· article· en· W1978661265 on OpenAlexaffabout
Bruce C. W. McGee, F.E. Vermeulen

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOil sandsAsphaltPetroleum engineeringHeat transferElectric heatingMass transferEnhanced oil recoveryProcess (computing)Electric potential energyMechanicsEnvironmental scienceMaterials scienceGeologyThermodynamicsComposite material

Abstract

fetched live from OpenAlex

Abstract Electrical heating of the Alberta oil sands for the recovery of bitumen has been studied since the early 1970's(1–5). The technology has evolved as an additional technology to SAGD and surface mining. This paper describes the heat and mass transfer mechanisms associated with a specific application of electrical heating, the Electro-Thermal Dynamic Stripping Process (ETDSP ™), for the production of bitumen from the oil sands. Given that heat is created in the oil sand as a current flow through the connate water and that initially all the fluids are immobile, the end result is a pressure and temperature distribution that is characteristic of an electrical heating process. To effectively recover the heated bitumen from the oil sand requires an understanding of the heat and mass transfer mechanisms associated with the pressure and temperature distribution, as well as gravity forces. The electrical heating process changes as the oil sand increases in temperature and the bitumen is produced. This results in a dynamic process whereby the heat, mass and electromagnetic fields are strongly coupled and in a transient state throughout the entire recovery process. The dominant mechanisms of the electrical heating recovery process are presented in terms of fundamental equations and solved numerically. A 3D quasi-harmonic finite element electromagnetic model is coupled to the mass and energy equations and solved in time. A recovery strategy based on an understanding of the recovery mechanisms is presented in terms of electrode spacing, duration of heating, energy supply and favourable operating requirements. Introduction Oil sands are a mixture of sand, bitumen and water. The bitumen is defined as oil that is less than 10 API and will not flow to a well in its naturally occurring state. The Alberta Energy & Utilities Board (AEUB) estimates that given current technology, over 300 billion barrels are expected to be recovered from the Alberta oil sands. There are presently two techniques used to produce bitumen; open pit mining and in situ thermal recovery, which involves drilling wells and injecting steam to heat the bitumen allowing it to flow and be produced from a well. Of the in situ methods now used, steam assisted gravity drainage (SAGD) is the most promising, having the advantages of lower energy requirements and higher recovery factors over other steam injection methods. In-situ thermal recovery methods as applied in oil sand deposits have the common objective of accelerating the hydrocarbon recovery process. Raising the temperature of the host formation reduces the bitumen viscosity allowing the near solid material at original temperature to flow as a liquid. These effects assist in sweeping much of the bitumen from the formation when driving agents are externally injected or when autogenously processes, such as gravity drainage, come into play. Transferring electromagnetic energy to the deposit is proving to be an effective means of supplying the necessary heat. In the electro-thermal process, electromagnetic energy is converted to heat in situ using a system of electrodes from which a current flows through the formation.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.216
Teacher spread0.207 · 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 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

Citations54
Published2007
Admission routes2
Has abstractyes

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