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Record W2738380912 · doi:10.5006/c2012-01716

Material Selection for Designing SAGD Plants

2012· article· en· W2738380912 on OpenAlexaffabout
Dilip Kumar, Zhu Jiang, Yongoh Shin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsJacobs (Canada)
Fundersnot available
KeywordsSelection (genetic algorithm)Petroleum engineeringComputer scienceEngineeringProcess engineeringMaterials scienceEnvironmental scienceBiochemical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract SAGD (Steam Assisted Gravity Drainage) is one of the most important processes being used for extracting oil from Alberta’s Oil Sands field. A typical SAGD facility in Alberta has several units that require careful potential corrosion evaluation and materials selection to ensure safety and integrity of the plant for full design life. These are (but not restricted to) well pads, bitumen treatment, deoiling, produced gas and evaporator systems. Many of these units are typical of SAGD and enough data on corrosion rates and materials performance may or may not be available. The major concerns that affect material selection in SAGD operations are carbon dioxide (CO2) and hydrogen sulfide (H2S) corrosion, high chlorides and total dissolved solids (TDS), oxygen ingress and operating temperature. For example, both well pads and bitumen treatment units are likely to see significant amounts of CO2 and H2S (with variation in percentages), high temperatures as well as considerable content of water and oil. High chlorides may also be present. Produced gas units will also have high levels of CO2 and H2S. Hence these units can witness high corrosion rates for carbon steel components particularly wherever conditions of two phase flow and/or high temperatures exist. High velocities may further complicate the condition. Deoiling systems may also have high levels of CO2, particularly, where there is a possibility of gas breakout due to pressure drop. Oxygen ingress in deoiling systems can also cause increased corrosion rate for carbon steel components. Evaporator systems pose another challenge in material selection due to the presence of extremely high levels of total dissolved solids (TDS) up to 100,000 ppm and very high chlorides up to or greater than 40,000 ppm and normal materials of choice such as austenitic stainless steels may not have an acceptable life. This paper discusses some of the corrosion risks involved in various units and elaborates on authors’ experience in materials selection for several new projects on designing SAGD plants.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.020
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.008

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 head0.021
GPT teacher head0.223
Teacher spread0.202 · 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
GenreMethods

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

Citations1
Published2012
Admission routes2
Has abstractyes

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