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Record W2021398099 · doi:10.4043/20385-ms

Towards Sustainable and Environmentally Friendly Enhanced Oil Recovery in Offshore Newfoundland, Canada

2010· article· en· W2021398099 on OpenAlexaffabout
Brandon George Thomas, Abduljelil Iliyas, Thormod E. Johansen, Kelly Hawboldt, Faisal Khan

Bibliographic record

VenueOffshore Technology Conference · 2010
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsMemorial University of NewfoundlandHusky Energy (Canada)
Fundersnot available
KeywordsEnvironmentally friendlySubmarine pipelineEnhanced oil recoveryFlue gasFossil fuelPetroleum engineeringWork (physics)Sustainable energyEnvironmental scienceOil fieldPetroleum industryEngineeringWaste managementEnvironmental engineeringRenewable energyMechanical engineering

Abstract

fetched live from OpenAlex

Abstract A consortium of university-industry researchers are developing sustainable and environmentally friendly enhanced oil recovery (EOR) technology for oil fields off the east coast of Newfoundland, Canada. This paper is foundational work on potential implementation of air and flue gas injection techniques. The paper discusses reservoir and facility considerations of air and flue gas injection and provides recommendations for project evaluation. The paper presents screening level results for Husky Energy's White Rose Field as a case study. Newfoundland offshore fields contain light oil (30-37 oAPI, 0.5-0.8 cP) making the fields potential targets for gas based EOR. However, with the oil fields located 310-350 km off the coast, availability of injection gas and logistical problems present barriers to EOR. Air injection has the advantages of an unlimited supply of injectant, success in laboratory and field applications, years of safe operation, and potential for an estimated 10% incremental oil recovery in waterflooded reservoirs. The challenges toward implementation of both techniques considering field characteristics and infrastructure are discussed along with practical solutions to aid implementation. The evaluation of sustainable and environmentally friendly EOR technologies is inline with long-term regulatory requirement and is timely as oil production from existing fields is beginning to decline. Moreover, with only 3 of over 20 discovered fields off the coast of Newfoundland currently developed, the conclusions and recommendations may also be valuable in the near future for evaluation of EOR techniques for the remote fields offshore Newfoundland. Introduction When evaluating the potential for enhanced oil recovery offshore Newfoundland, two key themes must be addressed;Is the process suitable for the reservoir properties and reservoir fluids?Is the process suitable for implementation offshore? The challenges of implementing enhanced oil recovery (EOR) in an offshore environment are much greater than in an equivalent onshore field (Bondor et al., 2005). While EOR processes tend to be reservoir and reservoir fluid specific, the project must be commercially feasible within the high cost offshore environment. Beyond normal offshore conditions, Newfoundland's offshore oil fields occur in one of the harshest offshore environments in the world, with severe weather and ice encroachment for long periods during the spring and summer. The two enhanced oil recovery techniques discussed within this document are high pressure air injection (HPAI) and flue gas injection. Both processes supply additional energy to the reservoir by the injection of non-hydrocarbon gases with the aim of recovering incremental oil.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.005
GPT teacher head0.210
Teacher spread0.205 · 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 designSimulation or modeling
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

Citations4
Published2010
Admission routes2
Has abstractyes

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