MétaCan
Menu
Back to cohort
Record W4291754996 · doi:10.1190/image2022-3751251.1

Clay composition and diagenesis in organic-rich shale: A case study of Duvernay Formation, West Canadian Sedimentary Basin

2022· article· en· W4291754996 on OpenAlexaffabout
Hui Li, Nina Zeyen, Nicholas B. Harris, Sasha Wilson

Bibliographic record

VenueSecond International Meeting for Applied Geoscience & Energy · 2022
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDiagenesisGeologyOil shaleStructural basinGeochemistrySedimentary rockSedimentary basinComposition (language)PetrologyGeomorphologyPaleontology

Abstract

fetched live from OpenAlex

Clay minerals such as illite and smectite are common constituents of sedimentary rocks, especially mudstones, and have a significant influence on the reservoir geomechanical and petrophysical properties (Boles, 1979; Brown et al., 2001; Tolhurst et al., 2002; Dong et al., 2018). The illite content of mixed-layer clays can be used to calibrate the thermal history of the basin (Pevear, 1999). These factors make clay mineral and related reactions important in petroleum development and exploration. Hower et al. (1976) showed that smectite-rich mixed-layer clay converts to illite-rich clay with increasing burial depth and temperature in Gulf Coast sediments. However, our examination of clay minerals in the organic-rich shale of the Upper Devonian Duvernay Formation in the Western Canada Sedimentary Basin reveals patterns of clay diagenesis that are inconsistent with Gulf of Mexico models. Our study examines whether the illite-smectite conversion model developed for organic-lean shales applies to organic- rich shales and considers other factors that may affect this conversion. Two alternative hypotheses are considered: that smectite never entered the basin, or the smectite converted to illite at abnormal shallow depth through microbial catalysis.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.244
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.011
GPT teacher head0.219
Teacher spread0.208 · 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
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueSecond International Meeting for Applied Geoscience & EnergySame topicHydrocarbon exploration and reservoir analysisFrench-language works237,207