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Impact of Maturation on the Validity of Paleoenvironmental Indicators: Implication for Discrimination of Oil Genetic Types in Lacustrine Shale Systems

2020· article· en· W3025460035 on OpenAlexaff
Hong Zhang, Haiping Huang, Zheng Li, Mei Liu

Bibliographic record

VenueEnergy & Fuels · 2020
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Calgary
FundersNational Natural Science Foundation of China
KeywordsHopanoidsOleananeOil shaleMaturity (psychological)GeologyTerrigenous sedimentSource rockVitriniteOrganic matterSteraneKerogenGeochemistryMineralogySedimentary rockPaleontologyChemistryStructural basinOrganic chemistry

Abstract

fetched live from OpenAlex

Organic geochemistry analyses were carried out on twenty-two shale samples from the Eocene Shahejie (E s ) Formation in two wells at the Dongying Depression, East China, aiming to investigate the evolution of the gammacerane index (GI) and oleanane index (OI) and the impact of maturation on the validity of these paleoenvironment indicators. While high GI and OI ratio values in some samples do indicate the occurrence of a hypersaline interval and higher contribution of a terrigenous organic matter input, respectively, a positive correlation between both molecular markers and known maturity level suggests dual controls from the source and maturation. A component concentration profile in a maturation sequence offers mechanism interpretation for effectiveness of these commonly used molecular parameters. The invalidity as geochemical diagnosis for paleoenvironment indicators in some highly mature samples is attributed to faster depletion of C 30 17α(H), 21β(H)-hopane against gammacerane and oleanane in the main oil generation window when the vitrinite reflectance value reaches about 0.7%. The present study sheds light on potential risks for discrimination of oil genetic types by those indicators in the complex orogenic region. Hence, with limited validity of these indicators in high maturity regime, caution must be taken while conducting geochemical interpretation on source rocks and crude oils.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.022
GPT teacher head0.238
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 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

Citations7
Published2020
Admission routes1
Has abstractyes

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