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Record W4410577051 · doi:10.1071/ep24132

A novel way to achieve early commercialisation of tight-sand and shale gas fields: small-scale modularised liquefied natural gas

2025· article· en· W4410577051 on OpenAlexaboutno aff
Hongfeng Wu, Fengxu Jian, Mark C. Nicholas, William Walton

Bibliographic record

VenueAustralian Energy Producers journal. · 2025
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum engineeringShale gasNatural gasScale (ratio)Liquefied natural gasNatural gas fieldOil shaleTight gasNatural (archaeology)GeologyEnvironmental scienceWaste managementHydraulic fracturingEngineeringGeographyCartography

Abstract

fetched live from OpenAlex

Based upon published reports, tight-sand and shale gas are one of the largest sources of natural gas under development globally, with annual production increasing dramatically from 2008 to 2023. This was particularly so in China, driven by advancements in drilling and completion technology such as multi-stage hydraulic fracturing in long horizontal wells. Given Australia’s geological setting and industrial environment, which have some similarities with USA and Canada, the country has potential to become a major player in commercially viable tight sand/shale gas production. An estimated 12.93 TCF of 2C contingent gas resources have been identified in Australia, primarily located in the Beetaloo Sub-basin within the greater McArthur Basin, as well as in the Cooper Basin, Canning Basin and Bowen-Surat Basins. However, developing tight-sand and shale gas resources in Australia presents numerous challenges, including their remote location, lack of existing gas export infrastructure, and well productivity constraints due to restrictions on the use of hydraulic fracturing. Additionally, a high-cost environment further hinders the path to commercial production. In China, a small-scale modularised LNG production approach has been successfully applied to tight sand/shale gas developments in the Sichuan Basin, demonstrating how early cash flow and extended production information can provide key support for an operator’s financial position in the market, increasing the chance of development. Lessons from these case studies could be instrumental in overcoming the challenges faced by Australia’s operators for development of this resource type, potentially paving the way to successful commercialisation.

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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.015
GPT teacher head0.231
Teacher spread0.216 · 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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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