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Record W4205784207 · doi:10.1021/acsomega.1c06399

A Materials Science Perspective of Midstream Challenges in the Utilization of Heavy Crude Oil

2022· review· en· W4205784207 on OpenAlexaboutno aff
Lacey D. Douglas, Natalia Rivera-González, Nicholas I. Cool, Aayushi Bajpayee, Malsha Udayakantha, Guan-Wen Liu, Anita Anita, Sarbajit Banerjee

Bibliographic record

VenueACS Omega · 2022
Typereview
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
FundersDivision of Industrial Innovation and PartnershipsDivision of Graduate Education
KeywordsMidstreamFossil fuelUnconventional oilEnvironmental scienceOil refineryOil sandsPeak oilPopulationSteam-assisted gravity drainagePetroleum engineeringPetroleumAsphaltEarth scienceWaste managementGeologyEngineeringGeographyClimate change

Abstract

fetched live from OpenAlex

An increasing global population and a sharply upward trajectory of per capita energy consumption continue to drive the demand for fossil fuels, which remain integral to energy grids and the global transportation infrastructure. The oil and gas industry is increasingly reliant on unconventional deposits such as heavy crude oil and bitumen for reasons of accessibility, scale, and geopolitics. Unconventional deposits such as the Canadian Oil Sands in Northern Alberta contain more than one-third of the world's viscous oil reserves and are vital linchpins to meet the energy needs of rapidly industrializing populations. Heavy oil is typically recovered from subsurface deposits using thermal recovery approaches such as steam-assisted gravity drainage (SAGD). In this perspective article, we discuss several aspects of materials science challenges in the utilization of heavy crude oil with an emphasis on the needs of the Canadian Oil Sands. In particular, we discuss surface modification and materials' design approaches essential to operations under extreme environments of high temperatures and pressures and the presence of corrosive species. The demanding conditions for materials and surfaces are directly traceable to the high viscosity, low surface tension, and substantial sulfur content of heavy crude oil, which necessitates extensive energy-intensive thermal processes, warrants dilution/emulsification to ease the flow of rheologically challenging fluids, and engenders the need to protect corrodible components. Geopolitical reasons have further led to a considerable geographic separation between extraction sites and advanced refineries capable of processing heavy oils to a diverse slate of products, thus necessitating a massive midstream infrastructure for transportation of these rheologically challenging fluids. Innovations in fluid handling, bitumen processing, and midstream transportation are critical to the economic viability of heavy oil. Here, we discuss foundational principles, recent technological advancements, and unmet needs emphasizing candidate solutions for thermal insulation, membrane-assisted separations, corrosion protection, and midstream bitumen transportation. This perspective seeks to highlight illustrative materials' technology developments spanning the range from nanocomposite coatings and cement sheaths for thermal insulation to the utilization of orthogonal wettability to engender separation of water-oil emulsions stabilized by endogenous surfactants extracted during SAGD, size-exclusion membranes for fractionation of bitumen, omniphobic coatings for drag reduction in pipelines and to ease oil handling in containers, solid prills obtained from partial bitumen solidification to enable solid-state transport with reduced risk of damage from spills, and nanocomposite coatings incorporating multiple modes of corrosion inhibition. Future outlooks for onsite partial upgradation are also described, which could potentially bypass the use of refineries for some fractions, enable access to a broader cross-section of refineries, and enable a new distributed chemical manufacturing paradigm.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score0.637

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.109
GPT teacher head0.349
Teacher spread0.240 · 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 designOther design
Domainnot available
GenreReview

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

Citations37
Published2022
Admission routes1
Has abstractyes

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