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Record W1656034229 · doi:10.2172/1025123

North Dakota Refining Capacity Study

2011· report· en· W1656034229 on OpenAlexaboutno aff
Dennis Hill, Kurt Swenson, Carl Tuura, Jim Simón, Robert Vermette, Gilberto Marcha, Steve Kelly, David A. Wells, E.S. Palmer, K. Yu, Tram Nguyen, Juliam Migliavacca

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsnot available
Fundersnot available
KeywordsNaphthaRefineryOil refineryRefining (metallurgy)GasolinePetroleumDiesel fuelRevenuePetroleum industryBusinessAgricultural economicsEngineeringWaste managementFinanceEconomicsChemistryEnvironmental engineering

Abstract

fetched live from OpenAlex

According to a 2008 report issued by the United States Geological Survey, North Dakota and Montana have an estimated 3.0 to 4.3 billion barrels of undiscovered, technically recoverable oil in an area known as the Bakken Formation. With the size and remoteness of the discovery, the question became 'can a business case be made for increasing refining capacity in North Dakota?' And, if so what is the impact to existing players in the region. To answer the question, a study committee comprised of leaders in the region's petroleum industry were brought together to define the scope of the study, hire a consulting firm and oversee the study. The study committee met frequently to provide input on the findings and modify the course of the study, as needed. The study concluded that the Petroleum Area Defense District II (PADD II) has an oversupply of gasoline. With that in mind, a niche market, naphtha, was identified. Naphtha is used as a diluent used for pipelining the bitumen (heavy crude) from Canada to crude markets. The study predicted there will continue to be an increase in the demand for naphtha through 2030. The study estimated the optimal configuration for the refinery at 34,000 barrels per day (BPD) producing 15,000 BPD of naphtha and a 52 percent refinery charge for jet and diesel yield. The financial modeling assumed the sponsor of a refinery would invest its own capital to pay for construction costs. With this assumption, the internal rate of return is 9.2 percent which is not sufficient to attract traditional investment given the risk factor of the project. With that in mind, those interested in pursuing this niche market will need to identify incentives to improve the rate of return.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.675
Threshold uncertainty score0.654

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0320.005

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 head0.138
GPT teacher head0.336
Teacher spread0.198 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2011
Admission routes1
Has abstractyes

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