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Record W3046966567 · doi:10.2118/0720-0070-jpt

Novel Solutions to Atmospheric Carbon Maintain Advantages of Petroleum

2020· article· en· W3046966567 on OpenAlexaboutno aff
Chris Carpenter

Bibliographic record

VenueJournal of Petroleum Technology · 2020
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsnot available
Fundersnot available
KeywordsFossil fuelRenewable energyCarbon neutralityPetroleumAtmospheric carbon cycleNatural resource economicsRenewable fuelsEnvironmental scienceGreenhouse gasWaste managementCarbon sequestrationCarbon dioxideEngineeringEconomicsChemistryEcology

Abstract

fetched live from OpenAlex

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 196109, “Promising Pathways to Lower Atmospheric Carbon Without Sacrificing the Petroleum Advantage,” by Subodh Chandra Gupta, Cenovus Energy, prepared for the 2019 SPE Annual Technical Conference and Exhibition, Calgary, 30 September-2 October. The paper has not been peer reviewed. The traditional advantages of petroleum-based transport fuels are challenged by the need to lower atmospheric carbon. Despite significant research, development, and investment during the last few decades, humanity still seeks carbon-neutral alternatives to petroleum that can be commercially viable. This paper presents novel approaches to carbon abatement using petroleum that have a strong chance to succeed in fulfilling technological and economic goals. Comparing Renewable Fuel With a Fossil-Fuel/Carbon-Offset Combination Typically, renewable liquid fuels are more expensive than their fossil-fuel counterparts on a unit-energy basis, but they result in lower carbon dioxide (CO2) addition to the atmosphere. Fossil fuels could also be used in such a way that produced CO2 is sequestered at a cost. With carbon-offset costs added to fossil fuel, both can be considered to provide carbon-neutral energy and can be compared on bases of cost and availability. Commodity prices fluctuate because of a variety of factors. Aside from the higher costs of renewables, reading too much into exact crossover points is not useful. Nonetheless, a general statement could be made that, at carbon-abatement costs of approximately $80-100/tonne of CO2, a renewable energy source can compete with fossil fuels. The cited costs of carbon capture and storage also have varied in the range of $100-150/tonne of CO2. The costs depend on emission sources and locations and the technology used for carbon capture. Even at reduced carbon costs, one would still be better off using gasoline - that is, if both commodities (renewable fuels and fossil fuels) were equally available at needed amounts. Technology for food-based bio fuels (e.g., corn-based ethanol) has advanced sufficiently and mostly is used commercially in the US for making bio ethanol for blending with gasoline. However, food-based biofuels compete with the availability of food and result in rising food prices. Data suggest that food-based biofuels are neither cost-competitive nor quantitatively sufficient to supplant fossil fuels. While they can play a role in carbon abatement to an extent if the carbon offset costs rise above $100/tonne of CO2, at present, they do not appear to be the solution to reduce emissions on their own. Can Useful Products Be Monetized From CO2 Conversion? Little other than energy is consumed in proportions greater than 4 billion tonnes/year (e.g., cement). Even if produced CO2 is converted to something that can replace cement, for example, the supply of that substance will be far greater than the amount of cement the world needs and, hence, it will not be able to retain its market - or any - price. If CO2 is converted to other substances, the quantity of products or byproducts will be on the order of 10-plus billion tonnes. This means that no prospective revenues from the useful byproducts can be relied upon, and, thus, it is prudent to only include costs involved in carbon abatement.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.640
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.013
GPT teacher head0.261
Teacher spread0.247 · 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 designNot applicable
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
Published2020
Admission routes1
Has abstractyes

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