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Clay–rock fracturing risk assessment under high gas pressures in repository systems

2023· article· en· W4313908226 on OpenAlexaff
Mostafa Mollaali, Jörg Buchwald, Vanessa Montoya, Olaf Kolditz, Keita Yoshioka

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsHydraulic fracturingNatural gasRadioactive wasteEnvironmental sciencePetroleum engineeringFracture (geology)Geotechnical engineeringGeologyWaste managementEngineering

Abstract

fetched live from OpenAlex

Abstract At the interface between the steel canister and the bentonite in a nuclear waste repository, we expect generation of hydrogen gas because of corrosion processes. The pressurized gas might fracture the engineered or natural clay barrier system, enhancing radionuclide transport into the geosphere. To assess the long-term integrity of the clay host rock under various conditions and scenarios, we need a large number of numerical simulations. However, a simulation tool for complex fracture propagation is often prohibitively expensive to run many realizations. Here, we developed a risk analysis tool based on the Design of Experiments to overcome the computational challenges by generating a computationally inexpensive proxy fracture model using a set of critical factors known as heavy hitters. We provided parameters and their probability distributions that are subject to uncertainty, as well as an objective function that assesses the risk of fracturing due to high gas pressures. Through various scenarios, we found that the fracture toughness dominates the impact on the risk.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score0.722

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 studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.010
GPT teacher head0.211
Teacher spread0.201 · 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 designObservational
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

Citations3
Published2023
Admission routes1
Has abstractyes

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