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Record W2199989128 · doi:10.1306/13391705m1023583

FIB-SEM and TEM Investigations of an Organic-rich Shale Maturation Series from the Lower Toarcian Posidonia Shale, Germany

2013· book-chapter· en· W2199989128 on OpenAlexaff
Sylvain Bernard, Leon D Brown, Richard Wirth, Anja Schreiber, Hans-Martin Schulz, Brian Horsfield, Andrew C. Aplin, Eliza Mathia

Bibliographic record

VenueAmerican Association of Petroleum Geologists eBooks · 2013
Typebook-chapter
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsGeological Survey of Canada
Fundersnot available
KeywordsOil shaleGeologySubtitleGeochemistryPaleontology

Abstract

fetched live from OpenAlex

Abstract Although shale gas systems constitute a new target for commercial hydrocarbon production, only a little attention has been paid to the evolution of these unconventional systems with increasing thermal maturation. This study reports the characterization of samples of the Lower Toarcian (Lower Jurassic) Posidonia Shale from northern Germany at varying levels of thermal maturity. Observations were made using an original combination of focused ion beam-scanning electron microscopy (FIB-SEM) and transmission electron microscopy (TEM). The paper documents the formation of microfracture-filling bitumen in close assotiation with kerogen residues with increasing maturity. Porosity evolves from mostly submicrometric interparticle pores in immature samples to intramineral and intraorganic pores in overmature (gas mature) samples. This intraorganic nanoporosity has most likely come about by the exsolution of gaseous hydrocarbon and been hydrocarbon wet during the thermal maturation processes. Although FIB-SEM and TEM images are small compared to field size, this study emphasizes the need for nanoscale imaging to better constrain hydrocarbon generation processes in gas shale systems.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.190
Teacher spread0.184 · 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.

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

Citations69
Published2013
Admission routes1
Has abstractyes

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