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Record W2015742925 · doi:10.2118/2002-205

Pipeline Transportation of Emerging Partially Upgraded Bitumen

2002· article· en· W2015742925 on OpenAlexaboutno aff
R.W. Luhning, Aditya Anand, Tim Blackmore, D.S. Lawson

Bibliographic record

VenueCanadian International Petroleum Conference · 2002
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsPipeline (software)AsphaltPetroleum engineeringPipeline transportComputer scienceTransport engineeringEnvironmental scienceEngineeringMaterials scienceOperating systemEnvironmental engineering

Abstract

fetched live from OpenAlex

Abstract The potential for bitumen and heavy oil production in Canada over the coming decade is predicted to be constrained by existing pipeline capacity, diluent availability and refinery conversion capacity. Technology for partial upgrading of bitumen to produce pipeline specification oil, reduce diluent requirements and add sales value is under aggressive development. The partially upgraded bitumen will be attractive for additional upgrading to end user products in a wider range of refineries than raw bitumen. The transportation of partially upgraded crude in existing pipelines to USA and potentially in new pipelines to new overseas customers will present new opportunities and challenges. This paper provides an overview of emerging partial upgrading technologies, the current pipeline specifications and the procedures to transport partially upgraded product, number of existing refineries to potentially accept partially upgraded product and future predictions. Introduction Canada, mainly in the province of Alberta, holds one of the largest reserves of oil in the world. With current technology the recoverable reserves are estimated to be 335 billion barrels1a. The vast majority of these reserves are in the oil sands. In 2001 Canada was the largest import supplier of crude oil to the USA1. Saudi Arabia2 was the second largest supplier as shown in Table 1. As shown in Figure 1, oil sands production is predicted to increase to 50% of Canada's oil by 2011. Over the coming decade conventional oil production is predicted to decline with the increased production being provided by synthetic light oil and bitumen from Alberta oil sands. The announced oil sands projects are listed in Table 2. If all projects were to proceed, the oil sands production alone would reach 3,445,000 bbl/d by 2011 as shown in Figure 2. Besides the physical and financial hurdles, there are three main challenges related to the transportation and marketing of the new production. The first challenge is the physical capacity of the existing pipelines to deliver the oil to market. With the expansions underway, the capacity is projected to be adequate until the middle of the coming decade then the projected production will exceed the capacity as shown in Figure 2. The second challenge is the supply of low viscosity diluent, usually natural gas condensate, to reduce the bitumen viscosity and density to meet pipeline specifications. The limit of bitumen that can be shipped by pipeline with the anticipated diluent availability is less than the projected bitumen production. The third challenge is the projected refinery market constraint to process the bitumen and synthetic light oil into consumer fuel products. The market constraint is less than the anticipated projected bitumen and synthetic light oil production as shown in Figure 2. This is a particular concern to producers of bitumen that are not integrated oil companies. There are a variety of ways to address the increasing bitumen production challenge. These include:refinery modifications and increased Canadian access in PADD (Petroleum Administration Defense District) II and IV,development of a regional upgrader in Alberta,production of synthetic diluent for blending with bitumen,developing new markets andadding additional pipeline capacity.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score1.000

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)0.0010.000

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 head0.013
GPT teacher head0.198
Teacher spread0.185 · 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.

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

Citations5
Published2002
Admission routes1
Has abstractyes

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