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Record W4232199680 · doi:10.2523/iptc-17463-ms

Post-Production Heavy Oil Operations: A Case For Partial Upgrading

2014· article· en· W4232199680 on OpenAlexaboutno aff
María A. Barrufet, Taher Mansoor Lokhandwala

Bibliographic record

VenueInternational Petroleum Technology Conference · 2014
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPipeline transportProduction (economics)Capital costPartial equilibriumDownstream (manufacturing)Environmental economicsEngineeringOperations managementEnvironmental engineeringEconomics

Abstract

fetched live from OpenAlex

Abstract This paper investigates the economic viability of post-production partial upgrading, specifically visbreaking, of heavy oil. We evaluate the potential technical and economic advantages and disadvantages of using visbreaking as a tool for reducing diluent use. Additionally, we identify the cost and energy drivers affecting the economic performance of this partial upgrading operation through a probabilistic Net Present Value (NPV) analysis. We have created a screening tool that can be used for a preliminary evaluation of visbreaking for transportation of heavy-oil solvent systems. Partial upgrading at the wellsite can potentially reduce operational costs to producers and transportation companies. A comparative economic study offered by this test can be used to make decisions on whether to proceed to a more detailed investigation of the process. Importantly, we have created a first-pass standardization tool for the screening of heavy oils for partial upgrading. Visbreaking can prove to be an economically favorable addition or, perhaps, even an alternative to blending heavy oil prior to pipeline transportation. In fact, operational costs of visbreaking are offset by the reduced cost of diluent and capital expenditures that can be mitigated within a reasonable time frame. Finally, we conclude that the altered fluid properties of a visbroken product require less energy per barrel for transportation, which translates to a more attractive economic scenario for transportation for the two baseline cases presented. Introduction Post-production transportation of heavy oil through commercial pipelines requires the oil to conform to certain specifications. The specifications in question depend on the lease agreement between the producer and the transportation company. However, the typical lease agreement includes cutoffs on product viscosity, API gravity, and sulfur content. Generally, the viscosity, gravity, and sulfur content cutoffs for transportation purposes are in the range of 350 cSt, 19o API, and <2 % by weight, respectively (Rahimi 2009). Consequently, the problematic high viscosity and low gravity of heavy oils requires certain post-production operations that make the feedstock conform to transportation guidelines. Most common among these operations are blending with a diluent or upgrading/partial upgrading on site. The World Energy Council (2010) report on heavy oils states that, in Canada alone, about 40% of the heavy oil produced undergoes blending with diluent for transportation; worldwide, this figure is close to 60%. Conversely, post-production upgrading is mainly done on a scale of hundreds of thousands of barrels in Canada, where even the smaller facilities are able to handle over 30,000 B/D. This accounts for a majority of post-production operations in Canada (around 60%), but leaves a sizable percentage of operations, especially operators of facilities, that produce 10,000 B/D or less. The question we are investigating involves replacing diluent operations and/or lowering the diluent usage with small upgrading units at the wellsite. We analyze the economic ramifications of this decision to the producer, as well as along the heavy oil value chain, from wellhead to refinery. Given that there are 6,000 billion bbl of heavy oil in place worldwide (Clark et. al. 2007), there is an enormous potential, now and in the future, for these resources to be a vital part of the energy landscape. However, because of the litany of additional challenges faced in transporting these hydrocarbons to market and converting them into marketable products, the viability of development often depends on the crude oil prices. Table 1 shows some of the additional costs required to make heavy oils into a viable, saleable product.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.522
Threshold uncertainty score0.809

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.016
GPT teacher head0.272
Teacher spread0.255 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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