MétaCan
Menu
Back to cohort
Record W2792415678 · doi:10.1071/aseg2018abt7_2b

Sedimentary Characteristics and Lithological Trap Identification of Distant Braided Delta Deposits: A Case on Upper Cretaceous Yogou Formation of Termit Basin, Niger

2018· article· en· W2792415678 on OpenAlexaff
Ning Zhao, Jiangqin Huang

Bibliographic record

VenueASEG Extended Abstracts · 2018
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsPetro-Canada
Fundersnot available
KeywordsGeologyLithologyFaciesStratigraphySequence stratigraphySedimentary depositional environmentSedimentary rockStructural basinCretaceousGeomorphologyDeposition (geology)Oil shalePetrologyGeochemistryPaleontologyTectonics

Abstract

fetched live from OpenAlex

Lithological trap identification in thin sand and thick shale layers is still a challenge for hydrocarbon exploration. Based on the high-resolution sequence stratigraphy theory and the establishment of high resolution sequence stratigraphy framework with seismic-well tie, the dynamic deposition process of braided river delta sands on late Cretaceous Yogou formation has been analyzed on 62 wells in passive rift Termit basin with multi-stages depressions and reversals. (1) Six kinds of sedimentary microfacies and three major reservoir sands with multi-stages stacking and lateral migration are in Yogou formation; (2) Based on Accommodation space/Sediments supply change and the deposition progress, sedimentary facies distribution in each member of YS3 sub-formation has been done according to sands thickness statistics of sedimentary micro-facies, narrow-time seismic attributes and slices analysis, multi-sources braided river delta depositional model has been concluded; (3) Based on source rock and caprock evaluation, with reservoir sands distribution and faults impact on Yogou formation of Termit basin, four types of traps, including structure-lithology, Structure-strata, stratigraphic and lithology are concluded. Traps influencing factors, i.e., structure geometry, sands distribution, paleotopography, stratigraphy cycling, sand/shale lateral connection, reservoir quality and so on, have different impacts on these traps, and different lithologic-stratigraphy traps have different exploration risks. Structure geometry and sands distribution are very important for the structure-lithology traps; structure geometry and paleotopography are the key factors in Structure-strata traps. Sands distribution and reservoir quality can be focused on lithology traps. Moreover, paleotopography and sand/shale lateral connection are significant on stratigraphic traps. Therefore, different hydrocarbon accumulation types of lithological traps have been established.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.367
Threshold uncertainty score0.539

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 studies0.0000.000
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 head0.013
GPT teacher head0.241
Teacher spread0.228 · 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 designBench or experimental
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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueASEG Extended AbstractsSame topicHydrocarbon exploration and reservoir analysisFrench-language works237,207