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Record W2026309888 · doi:10.2118/2009-204

Heat Transfer Fundamentals for Electro-thermal Heating of Oil Reservoirs

2009· article· en· W2026309888 on OpenAlexaboutno aff
Bruce C. W. McGee, R.D. Donaldson

Bibliographic record

VenueCanadian International Petroleum Conference · 2009
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsHeat transferPetroleum engineeringThermalHeat transfer fluidMaterials scienceEnvironmental scienceMechanicsThermodynamicsGeologyPhysics

Abstract

fetched live from OpenAlex

Abstract Electro-thermal methods are being used for extraction of bitumen from the oil sands. Several processes have been tested or are being proposed. Shell has proposed the use of electro-thermal methods in the carbonates and have tested a process at their Shell Peace River operation. E-T Energy is using an electro-thermal process in the Athabasca Oil Sands. Other institutions and companies, for example, the Alberta Research Council has also developed electro-thermal approaches for bitumen recovery. The heat transfer mechanisms, either from horizontal or vertical wells, associated with the electro-thermal approach distinguishes the various methods. In some of the approaches, heat transfer by conduction is the dominant method of transferring heat to the reservoir. In other methods, heat is generated within the reservoir electrically and transferred conductively, and in other processes convection is a key heat transfer mechanism in combination with the others. The purpose of this paper is to present a model for radial heat transfer that can be used to compare different electro-thermal heating methods. The model compares the resulting temperature distribution, time to achieve a heated volume at some distance away from the wellbore, and the power density in the reservoir between the different electro-thermal methods. Also, insight into design issues, such as well spacing and input power requirements, as well as practical matters related to efficiency, near wellbore heating, and water vaporization are presented. Introduction Oil reservoirs are a mixture of sand, bitumen and water. In Alberta, most of the oil is heavy or bitumen (from the oil sands) and cannot be produced easily from the reservoir. Electro-thermal methods are presently being considered for mobilizing bitumen from the oil sands. Bitumen is defined as oil that is less than 10 API and will not flow to a well in its naturally occurring state. Steam assisted gravity drainage (SAGD) is a promising in-situ thermal recovery method, having the advantages of lower energy requirements and higher recovery factors over other steam injection methods. However, about two thirds of the total deposit is too deep for surface mining and too shallow for steam injection [4] as depicted in Figure 1. These shallow resources may be well suited for electro-thermal processes. Also, electro-thermal methods have the potential to produce bitumen from oil sands that are at the mineable depths [2]. All 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 the bitumen to be recovered from the formation when driving agents are externally injected or when autogenous processes, such as gravity drainage come into play. Conduction Methods that use electro-thermal energy to increase the temperature of the wellbore without current flow in the reservoir have been also been developed. This is the first configuration shown in Figure 2. Electric heater elements are placed within the wellbore and are operated at very high temperatures.

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 categoriesInsufficient payload (model declined to judge)
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 score0.241
Threshold uncertainty score0.999

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 science0.0010.000
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 head0.019
GPT teacher head0.255
Teacher spread0.237 · 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.

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

Citations10
Published2009
Admission routes1
Has abstractyes

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