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Record W4410577368 · doi:10.1071/ep24260

Towards an improved understanding of fault systems behaviour in a CCS project

2025· article· en· W4410577368 on OpenAlexaff
Ludovic Ricard, Ziqiu Xue, J. Dautriat, Tsutomu Hashimoto

Bibliographic record

VenueAustralian Energy Producers journal. · 2025
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsKensington Health
Fundersnot available
KeywordsFault (geology)Computer scienceSystems engineeringProcess managementRisk analysis (engineering)EngineeringBusinessGeologySeismology

Abstract

fetched live from OpenAlex

A key uncertainty when investigating potential carbon capture and storage (CCS) projects is the role of faults and their interactions with the injected CO2. While it is anticipated that CCS projects will, at least in the short-term, focus on avoiding the interaction between the injected CO2 and fault systems, the likelihood of a storage projects encountering a fault system would increase, as the number of storage projects increases. While faults have been significantly studied for petroleum production, the role of faults for CCS projects is yet to be better understood: friend or foe? In the later context, analytical and desktop studies have been performed, yet limited understanding exists at pilot scale. In this work, we focus on field scale investigation at the CSIRO In-Situ Laboratory research facility (ISL), Western Australia, where an extensive fault system, the F10 fault, has been drilled through. A shallow CO2 controlled release test suggested that the fault did not significantly affect CO2 migration. More recently, two new 2D seismic lines were acquired and existing 3D seismic survey was reprocessed in 2023 and the Harvey 6 well intersecting fully the fault was drilled in March 2024. In March 2025, a vertical injection well will be drilled with the intent to inject fluid into the fault. This new data provides great insights on the fault structure and the planning of a deeper injection experiment which is discussed.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.795

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.050
GPT teacher head0.319
Teacher spread0.269 · 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 designSimulation or modeling
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

Citations0
Published2025
Admission routes1
Has abstractyes

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