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Record W1965231672 · doi:10.2118/2006-046

Calculations of the Effect of Boiling Water on Bitumen Production

2006· article· en· W1965231672 on OpenAlexfundaboutno aff
J. Wang, Bruce C. W. McGee, Apostolos Kantzas

Bibliographic record

VenueCanadian International Petroleum Conference · 2006
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsPorous Media Laboratory
KeywordsBoilingAsphaltProduction (economics)Petroleum engineeringEnvironmental scienceProcess engineeringWaste managementMaterials scienceEngineeringThermodynamicsComposite materialPhysics

Abstract

fetched live from OpenAlex

Abstract Thermal methods for heavy oil and bitumen recovery include the injection of steam in the form of SAGD, CSS, and steam flooding, whereby thermal energy is given to the oil, reduces its viscosity and allows it to flow towards a production spot. Latent heat from the condensing steam carries considerable amounts of energy into the bitumen and helps in heating it up. In novel electromagnetic heating processes, it is proposed that all the latent heat of this steam is to be replaced by electrical heating and boiling of the water in-situ. This technology was proven to be very effective in treatment of contaminated sites but it has yet to be proven in the recovery of bitumen. The first part of the paper deals with the possible displacement mechanisms of such process. A combination of drainage, imbibition, viscosity reduction and gas expansion are considered to be the primary contributors of this process. Some of the possible problems for such technology are:is oil at boiling water temperatures mobile enough to flow to a production site via a simple mechanism such as gravity drainage?Is the detonation that expands water to steam at boiling point enough to push only oil or will it deform the sand?If water is the source of heating, and phase change eliminates the continuity of heating, how can we continuously keep heating the reservoir? This work is a very fundamental study of the physics of boiling water in porous media as a potential displacement agent for heavy oil and bitumen. Very simple calculations indicated that the expansion of water into steam could flush oil out of the pore space extremely efficiently. The objective of this paper is to demonstrate through a theoretical approach the feasibility of this mechanism and through very simple illustrative tests the possible realization of the presented theory. The work offers potential alternatives to steam injection, which in turn can offer energy savings for the recovery process. Introduction With the depletion of the conventional oil resources, heavy oil and bitumen play an increasingly important role as the main resource for crude oil. This is particularly true in Alberta since it has in excess of 400 × 109m3 of heavy oil and bitumen reserves [1]. However, the production of heavy oil and bitumen requires more advanced technologies compared to conventional production techniques. To date, the most widely used heavy oil recovery method is the injection of steam into the reservoir. The steam is injected in the forms of SAGD, CSS and steam flooding, whereby thermal energy is given to the oil, reduces its viscosity and allows it to flow towards a production spot. Latent heat from the condensing steam provides considerable energy into the bitumen and helps in heating it up. One of the potential alternatives to steam injection is the electromagnetic heating method for heavy oil and bitumen reservoirs. Electromagnetic heating is a method that can transfer heat to heavy oil reservoirs based on electromagnetic energy [2].

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.632
Threshold uncertainty score0.988

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 head0.009
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 designSimulation or modeling
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

Citations2
Published2006
Admission routes2
Has abstractyes

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