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Record W1974847648 · doi:10.2118/1207-0051-jpt

A Heavy- to Light-Crude-Oil Upgrading Process

2007· article· en· W1974847648 on OpenAlexaboutno aff
Karen Bybee

Bibliographic record

VenueJournal of Petroleum Technology · 2007
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsAsphaltCrude oilEnvironmental scienceWaste managementOil fieldLight crude oilPetroleumSynthetic crudeFossil fuelEngineeringPetroleum engineeringShale oilGeologyArchaeologyGeography

Abstract

fetched live from OpenAlex

This article, written by Assistant Technology Editor Karen Bybee, contains highlights of paper SPE 108678, "Performance of a Heavy- to Light-Crude-Oil Upgrading Process," by E.J. Veith, Ivanhoe Energy, prepared for the 2007 SPE International Oil Conference and Exhibition in Mexico, Veracruz, Mexico, 27–30 June. A proprietary heavy- to light-oil (HTL) upgrading technology is designed to process heavy oil cost effectively in the field and provide a stable, significantly upgraded synthetic-oil product along with byproduct energy that can be used to generate steam or electricity. Since the commissioning of a commercial demonstration facility (CDF) for upgrading heavy oil in 2005, a number of crude oils and vacuum-tower-bottoms (VTBs) feedstocks have been tested. Analysis of CDF performance shows that the HTL process is capable of delivering high yields of significantly upgraded product. Introduction In mid-2005, Ivanhoe Energy acquired a new patented process, called rapid thermal (RT) processing, for the field-located upgrading of heavy oil and bitumen. Included in the acquisition was a new CDF in the San Joaquin Valley in southern California that demonstrates a processing capacity of approximately 1,000 B/D of heavy crude oil. Fig. 1 shows the CDF in the Belridge oil field. There are significant accumulations of heavy crude and bitumen throughout the world that can be targeted by this technology. Both Canada and Venezuela have extensive heavy-oil reserves that compare in size to current reserves in the Middle East. As conventional lighter-crude-oil supplies decline, they will need to be replaced by heavier crudes. New residue-processing capacity could be added to existing refineries, or it could be built in separate, standalone upgrading facilities. If the oil is too heavy to transport by pipeline, and/or there is the need for heat or energy at the production site, heavy-oil upgrading in the field is attractive and may avoid extensive modifications of existing refineries. Traditional residue processing such as coking or hydrocracking are very expensive processes and require a large scale to be viable. The HTL technology would provide a lower-cost, simpler residue-processing option compatible with field development. HTL-Technology Development The development of the RT processing technology began in the early 1980s when it was discovered that a broad array of carbonaceous feedstocks (e.g., wood and heavy oil) could be thermally cracked to obtain valuable products at residence times of a few seconds. The initial commercial focus of the technology, beginning in 1989, was aimed at conversion of wood and wood residues to value-added fuels and chemicals. Seven commercial biomass plants based on this technology have been in operation for many years. As the biomass side of the business grew and operational and design parameters were optimized, the focus turned toward petroleum feedstocks. The petroleum application of the technology was demonstrated in a pilot plant in Ottawa, Canada, on more than 90 experimental runs using a number of different crude oils and bitumen between 1999 and 2002. Because it was believed that the technology had relatively low capital and operating costs compared to conventional carbon rejection technologies, such as delayed coking, commercialization of the HTL process was initiated.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.267
Teacher spread0.261 · 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 designBench or experimental
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
Published2007
Admission routes1
Has abstractyes

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