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Record W2903696031 · doi:10.32937/ijges.4.4.2018.35-55

Hydrocarbon Generation Potentials of Cenozoic Lacustrine Source Rocks: Gulf of Thailand, Southeast Asia

2018· article· en· W2903696031 on OpenAlexaff
Oladapo Akinlotan, Byami A. Jolly, Okwudiri A. Anyiam

Bibliographic record

VenueInternational Journal of Geology and Earth Sciences · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geophysical Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsCenozoicSource rockGeologySoutheast asiaGeochemistryOceanographyEarth sciencePaleontologyStructural basinAncient history

Abstract

fetched live from OpenAlex

Several hydrocarbon-rich Cenozoic basins are scattered across Southeast Asia and they present excellent opportunity to examine these rich but under-explored basins. These near-shore basins have petroleum potentials that are associated with the Oligocene-Miocene fluvio-lacustrine multipetroleum systems but are largely underexplored. This study evaluates the petroleum source potentials of some selected Cenozoic basins via maturation modelling analysis, burial and heat flow histories. ZetaWare’s Genesis modelling software was used for the study with burial history and thermal history parameters (vitrinite, TOC and HI). The maximum transformation of kerogen to hydrocarbon for source rocks in these basins may be as high as 80-100%. Analyses showed that threshold maturity for maximum hydrocarbon expulsion is around 1.0% vitrinite reflectance (Ro). Thermal maturation is accentuated with depth-especially southward and offshore. Source rocks in the Western (central) and Hua Hin (north) Basins are the most prolific (with up to 400 mgHC/gtoc); least prolific sources are in the Western (north and south) Basin with 96-120 mgHC/gtoc and 40-50 mgHC/gtoc respectively. These sources have expelled only secondary amount of gas (11-80mg/gtoc) because they are oil prone sources. Oil to gas expulsion ratio of 7:1 is estimated. Hydrocarbon expulsion started in the Late Miocene after the emplacement of all necessary traps. Deeply buried sources show good prospects for possible residual hydrocarbon generation. Moreover, all the modelled wells displayed source rocks that expelled significant quantity of hydrocarbon. All these basins show strong correlation with Pattani Basin, the most prolific hydrocarbon-bearing basin in the Gulf of Thailand.

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 score0.035
Threshold uncertainty score0.679

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 studies0.0000.001
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 head0.019
GPT teacher head0.228
Teacher spread0.210 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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