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Record W1980655939 · doi:10.2118/2008-133

From Steam Towards Sustainability! Possible Transition Technologies For the Heavy Oil And Bitumen Industry

2008· article· en· W1980655939 on OpenAlexfundaboutno aff
Steve Larter, Ian D. Gates, Jennifer J. Adams

Bibliographic record

VenueCanadian International Petroleum Conference · 2008
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAsphaltSustainabilityPetroleum engineeringPetroleum industryEnvironmental scienceBusinessEngineeringMaterials scienceEnvironmental engineering

Abstract

fetched live from OpenAlex

Abstract HOTS (heavy oil and tar sands) oils and bitumens are becoming significant in world and Canadian production, yet current employed recovery technologies (CSS, SAGD, mining) are inefficient in terms of recovery, energy and water intensity, and cost to the environment. Reservoir and reservoir fluid heterogeneities are ubiquitous in HOTS reservoirs and impactreservoir processes that depend on uniform oil mobility to work effectively e.g. SAGD or that are limited to reservoirs that can withstand high pressure processes e.g. CSS. Concerns about greenhouse gas emissions and water usage, combined with societal pressure to implement more sustainable energy recovery procedures require the development of much more effective recovery processes. An understanding of the geological and fluid heterogeneity typically found across heavy oil and bitumen provinces will assist in the transition from current processes (SAGD, CSS) to Reduced Emission to Atmosphere Recovery (REAR) processes, to Zero Emission To Atmosphere Recovery (ZETAR) processes. We discuss REAR processes initially based on optimizing current recovery processes to complex oil mobility distributions (geotailoring) by improving reservoir and fluid description processes and linking this to improved engineering solutions by refining well placement and operating conditions. The next step in recovery process evolution involves processes designed upfront to be geotolerant of complex and discontinuous geological facies but not requiring high pressure steam and reservoir fracturing strategies. Finally we review progress towards achieving efficient energy recovery from HOTS and possible routes ZETAR processes. These include the acceleration of microbial processes in reservoir to recover energy as methane, or ashydrogen as an intermediate product of biodegradation, which may be feasible under special conditions, and the capture or recycling of carbon using biological processes. Introduction Heavy oil and tar sand (HOTS) oils and bitumens are becoming larger portions of world and Canadian production, yet technologies employed are, despite great advances, inefficient in recovery terms (e.g. CHOPS or waterfloods), expensive in energy, water and environmental costs (SAGD, CSS, mining), ineffective in the bulk of reservoirs that have restricted vertical permeability (VAPEX, SAGD), or are limited to reservoirs that can withstand high pressures (CSS). Hydrogen requirements for upgrading impose additional environmental and economic premiums. Concerns related to greenhouse gas emission and water usage, combined with societal pressure to implement more sustainable energy recovery procedures all require the quick development of much more effective recovery processes. However, sustained high oil prices and profitable existing technologies plus restricted R&D capacity in the energy sector may encourage the business status quo. There is thus a need for quickly deployable, more sustainable transition recovery methods that can quickly effect large reductions in the environmental footprint while maintaining perceived short term economic needs. The transition from current processes e.g. SAGD and CSS via Reduced Emission to Atmosphere Recovery (REAR) processes, to Zero Emission To Atmosphere Recovery (ZETAR) processes will combine advances in fuelingystems, carbon capture, in-situ heat and gas generation, in-situ and surface upgrading and the business/regulatory environment, as well as recovery process design itself.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.999
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.018
GPT teacher head0.247
Teacher spread0.229 · 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 designTheoretical or conceptual
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
Published2008
Admission routes2
Has abstractyes

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