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Record W1988038200 · doi:10.2118/03-09-tn1

Costs for CO2 Capture and Sequestration in Western Canada

2003· article· en· W1988038200 on OpenAlexaffabout
L Fisher, Thomas Sloan, Peter Mortensen

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsCanadian Energy Research Institute
Fundersnot available
KeywordsCarbon sequestrationEnhanced oil recoveryGreenhouse gasEnvironmental scienceCarbon capture and storage (timeline)Discounted cash flowNet present valueFossil fuelNatural resource economicsCash flowCarbon creditCarbon dioxideWaste managementBusinessProduction (economics)Climate changeEngineeringEconomicsGeologyChemistry

Abstract

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Introduction Since the beginning of the Industrial Revolution anthropogenic activities in general, and fossil fuel combustion in particular, have contributed to an appreciable increase in atmospheric CO2 concentrations, among other greenhouse gases (GHGs)(1). With Canada's ratification of the Kyoto Protocol, the potential for sequestration of CO2 is worth serious consideration. This paper, which summarizes the results of a comprehensive three-volume study(2), provides an overview of the costs to capture, transport, and geologically sequester CO2 in Western Canada. In general sequestration means storing CO2 which has been removed either directly from anthropogenic sources or from the atmosphere, for geologically-significant time periods, if not permanently. Used herein sequestration refers to taking carbon dioxide which has been extracted from an exhaust or vented gas stream and placing it in long-term storage in depleted western Canadian oil and gas reservoirs, referred to as sinks. This study deliberately excludes CO2 used for enhanced oil recovery (EOR) projects, which may be economically attractive, but are volumetrically limited in comparison to pure storage projects. Methodology CERI used net discounted cash flow (DCF) models to estimate costs for CO2 capture, transportation, and storage. Discounted cash flow calculations generate the present value of a future stream of net cash flows. In this application. CERI models solve for a CO2 " price" that would make a CO2 capture. Transportation, and/or storage operation profitable. The model results therefore.; tre the prices that a company specializing in CO2 mitigation would have to charge per unit of CO2 sequestered to recover all of its costs including taxes and a return on investment. The methodology used to arrive al capture and sequestration costs analyzed CO2 sources and sinks in a similar way. Establishing the locations and characteristics of the major point sources and eligible sinks was a logical first step. However performing detailed cost analyses on every source and sink was not feasible. Instead, prototypes representing a range of different characteristics were selected for detailed analysis, from which the results were scaled to the remaining population. Unit costs for CO2 capture and storage were then generated from the population data using the economic (DCF) models. Finally to link the sources to the sinks, unit costs were developed for a common-carrier pipeline network in the basin. CO2 Capture Any large-scale CO2 capture program must first establish an inventory of potential capture candidates, including the volumes, characteristics, and locations of the most significant sources. For this study. CERI compiled an inventory of 192 discrete CO2 sources found at 115 sites throughout the Western Canadian sedimentary Basin, with total annual CO2 emissions of 141 Mt. Figure 1 illustrates the distribution of assessed emissions according to the industry from which they are emitted. The significance of coal-fired power plants in Western Canada's emission picture is evident. Oil sands mines and in situ projects contribute another large quantity, one that is expected to increase dramatically in the future. Based on projected emissions in 2005(3), CERI's inventory accounts for over 75% of industrial and power generation emissions in the four western provinces and 50% of total CO2 emissions.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.886
Threshold uncertainty score0.245

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 head0.006
GPT teacher head0.211
Teacher spread0.204 · 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 designObservational
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

Citations3
Published2003
Admission routes2
Has abstractyes

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