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Record W1969478139 · doi:10.2118/05-08-02

Pipeline Backbone for Carbon Dioxide for Enhanced Oil Recovery in Western Canada

2005· article· en· W1969478139 on OpenAlexaboutno aff
R.W. Luhning, J.H. Glanzer, Peter G. Noble, H.-S. Wang

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsnot available
FundersUniversity of Pennsylvania
KeywordsPipeline transportEnhanced oil recoveryCrude oilPipeline (software)Petroleum industryFossil fuelEnvironmental scienceCarbon dioxideBusinessNatural resource economicsWaste managementPetroleum engineeringEngineeringEnvironmental engineeringEconomics

Abstract

fetched live from OpenAlex

Abstract Carbon dioxide (CO2 injection into oil reservoirs is a proven technology that is emerging in Canada as an economic method to increase oil production from mature fields while sequestering CO2 emissions. Two commercial CO2 floods are currently operating in Alberta and Saskatchewan with a number of new projects identified for development. The Alberta and Saskatchewan governments have updated their royalty regulations to promote enhanced oil recovery and the industrial CO2 sources have been identified. The "building blocks" for a CO2 industry are being addressed, including the need for pipeline infrastructure to transport the CO2 from the industrial emission source to the commercial user. In the USA, the use of CO2 for enhanced oil recovery has resulted in the construction of about 2,500 km of pipeline. An ultimate network of about 2,000 km of pipelines for CO2 collection and transport has been proposed for Alberta alone. This paper will overview aspects related to the design and potential routes for a CO2 pipeline backbone system that could develop to collect CO2 supplies from a variety of industrial sources for transportation to commercial markets. Introduction Canada, mainly in the province of Alberta, holds one of the largest reserves of oil in the world. With current technology, the recoverable reserves are estimated(2) to be 53 billion m3. In recent years, Canada was the largest import supplier of crude oil to the USA(1). Saudi Arabia has been the second largest supplier of crude oil to the USA(2). While the oil sands are a major and rapidly growing source of oil in Canada, the oil industry in Canada was founded on light sweet conventional crude oil. Although conventional oil in Canada represents less than 2% of the world oil resource, it has provided the basis for the industry in Canada. Billions of cubic metres of light oil remain in known oil reservoirs in Canada that are currently under production. The primary production levels are declining and new technology approaches are needed to continue economic production. Enhanced oil recovery via CO2 injection in conventional light oil pools is a well developed technology that has been practiced in the USA for more than 40 years. Canada, and the province of Alberta in particular, have many light oil pools that are suitable for enhanced oil recovery using CO2 injection. Alberta is also fortunate in that oil sands projects, refineries, petrochemical plants, and natural gas processing plants produce large amounts of CO2 with the purity required for use for Enhanced Oil Recovery (EOR). To achieve the economic, environmental, and social benefits of enhanced oil recovery with CO2 injection, the technology needs to be demonstrated in the specific oil pool and the CO2 needs to be transported to the injection point. This paper will describe the unfolding plan to achieve the benefits of new clean energy from the use of CO2 for EOR in Alberta and Western Canada. CO2 Enhanced Oil Recovery in Canada In Canada, commercial use of CO2 for EOR is limited to only two projects.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.795
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.007
GPT teacher head0.218
Teacher spread0.211 · 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

Citations3
Published2005
Admission routes1
Has abstractyes

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