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Record W3033531811 · doi:10.1002/9781119593324.ch7

Acid Gas Injection at SemCAMS Kaybob Amalgamated (KA) Gas Plant Operational Design Considerations

2020· other· en· W3033531811 on OpenAlexaboutno aff
Rinat Yarmukhametov, J. R. Maddocks, Jason Lui

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsSour gasGas compressorEngineeringWaste managementAcid gasCapital costEnvironmental sciencePetroleum engineeringProcess engineeringNatural gasMechanical engineering

Abstract

fetched live from OpenAlex

SemCAMS currently owns and operates the Kaybob Amalgamated (KA) sour gas processing facility, located near Fox Creek, Alberta. The gas plant takes gas from various sweet and sour inlets and processes these streams to produce sales specification gas, LPG and C5+ products. With reduced plant throughput and lower H2S concentration in the feed stream to the Sulfur Recovery Unit, the existing sulfur plant was operating below the design turndown rates, which impacts the performance and reliability of the entire facility. SemCAMS decided to decommission the sulfur plant in Q2 2018 and replace it with acid gas compression that will inject acid gas into a disposal well drilled on the plant lease. The GLE scope of work was to complete detailed engineering (process, civil, mechanical, electrical, instrumentation and controls) of the two acid gas injection compressors, including the suction piping from the existing acid gas knockout drum, the discharge piping to the wellhead, as well as utility piping to/from the compressors (instrument air, fuel gas, flare, drains, etc.). Major project decisions and design aspects are reviewed and discussed with a focus on capital & operational cost, challenges, issues, and lessons learned relating to process modelling and operational design. Process modelling topics discussed include: steady state and dynamic modelling for the AGI system; flare system design; compression capacity control; hydrate formation prevention during system blowdown; methanol injection for hydrate prevention; prediction of water content/hydrate formation temperatures within the AGI system and finally overpressure protection of compression equipment and the injection line. Operational design topics discussed include: Inherently Safety Design (ISD) strategies; material selection for compression equipment and the injection line, air cooler design, impact of leak scenarios on the existing plant and AGI system design; freeze protection; considerations for depressurization duration and acid gas flaring scenarios; and impact of the existing plant safety practises on the addition of the AGI system.

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)0.001
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: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.020
GPT teacher head0.212
Teacher spread0.191 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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