MétaCan
Menu
Back to cohort
Record W2027069799 · doi:10.2118/2009-129

Air Quality Monitoring in the Canadian Oil Sands: Tests of New Technology

2009· article· en· W2027069799 on OpenAlexaboutno aff
U. Platt, KH Seitz, Joelle Buxmann, H.F. Thimm

Bibliographic record

VenueCanadian International Petroleum Conference · 2009
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsOil sandsPetroleum engineeringQuality (philosophy)Environmental scienceAir quality indexMining engineeringComputer scienceGeologyMeteorologyArchaeologyGeographyAsphalt

Abstract

fetched live from OpenAlex

Abstract Relatively new bitumen recovery processes, such as SAGD, minimize the environmental footprint in terms of land disturbance and water demands. However, as a corollary, air monitoring becomes more difficult. In particular, monitoring for sulphur and nitrogen oxides, as currently practiced, suffers from significant limitations in remote regions, such as the Canadian oil sands areas. Current techniques require the placement of monitoring trailers in accessible locations, but the electrical power or even access for optimal location for trailers is not always given. In addition, the trailers are capable of monitoring air quality only at the location of their deployment. There would be an advantage in deploying monitoring techniques that require minimal power (e.g. car battery, solar cell) and are capable of measuring air quality at a distance from the place of deployment. In the autumn of 2008, a trial of DOAS (Differential Optical Absorption Spectroscopy) was undertaken in northern Alberta and northern Saskatchewan, at four SAGD plants in various stages of development. Results of this study, and a discussion of the technology, will be given. Advantages and limitations of DOAS for deployment in Athabasca will be discussed. In general it was found that SO2 results showed remarkably low degrees of contamination, while NO2 concentrations were more noticeable. Introduction Conventional techniques for air quality monitoring in Western Canada have, for several decades, relied on monitoring trailers deployed in strategic locations downwind from major emitters. Typically, sulphur dioxide, for example, is measured by pulsed fluorescence spectroscopic techniques. These systems have proven to be quite reliable in the past. The northern Canadian heavy oil and oil sands areas, however, present unusual challenges that limit the applicability of the conventional techniques. Most projects are located in the boreal forest areas of the Canadian west, and it is therefore necessary to minimize the environmental footprint of such projects. The more recently developed in-situ recovery techniques, primarily Steam Assisted Gravity Drainage (SAGD) are in fact capable of significantly reduced footprints by virtue of their reliance on horizontal well technology. Horizontal sections of such wells are now of the order of 800 meters, and multiple wells can be drilled from a single location. It is this reduction in land disturbance that, as a corollary, makes extensive air quality monitoring more difficult, for the following reasons:Monitoring stations of the conventional type require building of access roads or clearings.Electrical power lines need to be installed to the trailer sites chosen, because permanent mains power is required for trailer operation.Any trailer so deployed is capable of measuring air quality only at the point of deployment; air quality assessment at a distance is not possible. Given that the oil sands area in northern Alberta alone is of the order of 140,000 square kilometers in area (and is therefore larger than most European countries and just under half the size of Germany or France) adequate monitoring of air quality in the region may well become part of the footprint problem if the current technology is not at least augmented by other techniques.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score0.595

Codex and Gemma teacher scores by category

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

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 head0.028
GPT teacher head0.290
Teacher spread0.263 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueCanadian International Petroleum ConferenceSame topicPetroleum Processing and AnalysisFrench-language works237,207