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Record W1977610796 · doi:10.4043/24091-ms

Development Options for North American LNG Export:The Merits of Inshore Deployed FLNG for Liquefaction of Onshore Shale Gas and Examination of Principal Technology Drivers

2013· article· en· W1977610796 on OpenAlexaboutno aff
Joe Thottungal Verghese, Nancy Ballout

Bibliographic record

VenueOffshore Technology Conference · 2013
Typearticle
Languageen
FieldEnergy
TopicOil, Gas, and Environmental Issues
Canadian institutionsnot available
Fundersnot available
KeywordsLiquefied natural gasContext (archaeology)LiquefactionHydrothermal liquefactionSubmarine pipelinePipeline transportFossil fuelEnvironmental scienceEngineeringNatural gasWaste managementEnvironmental engineeringBiofuelGeology

Abstract

fetched live from OpenAlex

Abstract The advent of shale gas onshore United States and Canada, the resulting overhang of gas supplies and downward pressure on gas prices have led Operators both upstream and midstream to consider the possibilities of LNG liquefaction plants for the export of LNG from coastal locations to international markets. Several proposals for onshore liquefaction submitted to DoE and FERC, e.g. by Cheniere Energy relate to the conversion of existing under-utilized LNG regasification sites. The development of LNG liquefaction at any greenfield onshore site is, however, anticipated to come under rigorous ‘permitting’ scrutiny. From this standpoint, LNG FPSO provides an attractive development option for the monetization and export of shale gas, a growing feature of interstate and intrastate pipeline networks. The paper assesses the technology implications of migrating LNG FPSO concepts hitherto developed internationally to this inshore service. The LNG FPSO topsides processing, including feed pre-treatment and liquefaction systems are evaluated for the processing of feed gas sourced from either pipeline grids or from onshore gas plants with partial or full stripping of NGLs. The technology options for LNG storage, product offloading, and hull forms are also assessed for simplification that results from inshore deployment. The authors draw on insights from a recent FEED execution for a floating liquefaction unit destined for an inshore Asia Pacific location, and other FLNG studies to analyze the merits of inshore deployments in the North American context. While schedule and project cost are conjecturally viewed as potential benefits, the paper addresses the following:Does feed gas partially or fully conditioned onshore, materially alter the processing required on the FPSO, e.g. in acid gas removal, dehydration, and NGL extraction?What are the implications for choice of LNG liquefaction cycles, train capacity, and for refrigeration compression power, as a result of processing conditioned gas?Do atshore/inshore deployments widen the choice of technology options for LNG containment and for LNG offloading?What are the potential hull forms that may be considered candidates for these applications? The paper critically assesses the above issues and choices in formulating a technology roadmap for LNG FPSO developments for North American onshore gas. Market Context The energy markets are witness to a remarkable evolution in gas developments, driven by a number of factors including the relative abundance of this energy resource, its global availability, its flexibility in use and its low carbon number. According to the international Energy Agency (IEA), gas will increase its share of the global energy mix from 21% in 2009 to to reach 25% by 2035. Underpinning this acceleration in gas use is the increasingly traded position of LNG. In 2012 traded volumes exceeded 240 million t/y, which is a fivefold increase over the 1990 level of just 53 million t/y(1). The industry and the trades have been truly global, with LNG liquefaction and regasification plants widely distributed, and cargoes routinely transacted between Atlantic, mid Asian and Pacific regions. The character of the market has also seen structural changes where short term trades (i.e.with contract durations of 4 years or less) have grown from 4% in 1990 to 25% today, further stimulating the growth of this market.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.677

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 studies0.0000.001
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 head0.020
GPT teacher head0.237
Teacher spread0.217 · 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
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

Citations2
Published2013
Admission routes1
Has abstractyes

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