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Record W2080318937 · doi:10.2118/08-01-18-tn

A Model of a Combined Heat and Power System for SAGD Operations

2008· article· en· W2080318937 on OpenAlexafffundabout
B. Bowers, N. Leblanc, S. Jazayeri, A. Naini

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2008
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsCanadian Energy Research Institute
FundersUniversity of Alberta
KeywordsNatural gasCogenerationElectricity generationBoiler (water heating)EngineeringPetroleum engineeringSteam drumWaste managementElectricityFossil fuelCombined cycleEnvironmental scienceElectric powerSteam-electric power stationTurbineSuperheated steamMechanical engineeringPower (physics)

Abstract

fetched live from OpenAlex

Abstract This paper presents a model of a combined heat and power system, also known as cogeneration, for the generation of electricity and steam for in situ bitumen extraction operations. The model simulates the operation of a gas turbine-electric generation system with the hot exhaust gases from the turbine being directed to a boiler for steam production. Alternately, a topping steam turbine-electric generation system with exhaust steam directed to the in situ operations is simulated. The model provides estimates of the fuel requirements and electricity generated by the combined process. It enables the investigation of the relationships between electricity generation, steam production and fuel requirements for these operations. Included is an illustrative example of the application of the model for the assessment of the economics of the steam assisted gravity drainage (SAGD) process using alternate fuels, natural gas and petroleum coke. Introduction Bitumen mining and in situ bitumen extraction operations in northern Alberta are large users of natural gas and are projected to grow substantially over the next decade. However, since the turn of the century, the North American natural gas supply-demand balance has tightened and, consequently, gas market prices have risen substantially. Moreover, western Canadian supplies of natural gas are projected to decline in the near-term. Environmental concerns regarding the use of fossil fuels, such as natural gas, have recently become more acute. Consequently, there is a need to identify alternate fuels and heat generation systems that could, if implemented, reduce the cost and environmental impact of bitumen extraction operations. The Combined Heat and Power Model (CHPM) described in this paper was developed specifically for the economic assessment of combined heat and power systems for bitumen extraction using the steam assisted gravity drainage (SAGD) process. It calculates the fuel requirement to provide heat for the SAGD operations as well as the associated electricity generated, which are required for the economic assessment. The economic analysis is performed in a separate model-the SAGD Supply Cost Model (SSCM)-which calculates the supply cost of the extracted bitumen. Description of the CHPM The CHPM simulates the operation of the steam plant of a SAGD operation, both with and without electricity generation. Gas and steam turbine options for the generation of electricity are included (See Figures 1 and 2, respectively). As shown in the figures, mass and heat energy flows of the system are outputs of the model. The principles of conservation of energy and mass are used for the calculation of the flows. Figure 1 shows that, in the case of the gas turbine option, natural gas fuel is burned in a gas turbine to produce electricity. Waste heat in the exhaust gases from the turbine is recovered in a boiler, which produces steam for the bitumen extraction process. There is a steam-water separator, which produces 100% quality steam for this process. Water from the separator is returned to the boiler.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.087
Threshold uncertainty score0.172

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0030.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0210.002

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 head0.010
GPT teacher head0.197
Teacher spread0.187 · 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 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
Published2008
Admission routes3
Has abstractyes

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