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Record W2591264424 · doi:10.1306/01111715108

Quantitative characterization of shales within tidally influenced fluvial valley fill deposits of the Ferron Sandstone, eastern Utah: Implications for hydrocarbon exploration

2017· article· en· W2591264424 on OpenAlexaff
Kivanc Biber, Shuhab D. Khan, Janok P. Bhattacharya, Mark D. Barton, Craig Glennie

Bibliographic record

VenueAAPG Bulletin · 2017
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsGeologyFluvialHydrocarbon explorationGeochemistryGeomorphology

Abstract

fetched live from OpenAlex

ABSTRACT This study evaluates the proportion, length, and effective properties of thin (0.003–0.7 m [0.01–2.3 ft]) shale beds and drapes in tidally influenced channels within a compound valley fill with a focus on estimating geologically based effective rock properties. The Cretaceous Ferron Sandstone is an outcrop analog for fluvial–tidal systems with primary reservoirs being deposited as tidally influenced valley filling point bars. The study outcrops expose three valley systems in Neilson Wash of Utah. Light detection and ranging–derived digital outcrop models have been used to characterize shale length, width, thickness, and frequency of each valley fill succession. Long, uncommon, and anisotropic shales in valley 1 (V1) were deposited in a braided setting with little tidal influence. In contrast, shales in valley 2 (V2) were abundant, short, common, and equidimensional, suggesting deposition by more tidally influenced meandering rivers. Short, frequent, and equidimensional shales in valley 3 (V3) were deposited in single-thread meandering rivers with less tidal influence. A sandstone–shale model was used to estimate the effects of shales on vertical to horizontal permeability ratio (). The unique character of each depositional unit was reflected in resultant distributions. The valley fill deposits, V1, V2, and V3, had average ratios of 0.11, 0.09, and 0.17, respectively. More tidally influenced reservoirs such as the studied V2 had short but frequent shales, which resulted in low estimates. Estimates of for valleys that predominantly contained fluvial point bar deposits with lesser tidal influence (V1 and V3) were higher. The results of this study highlight the link between shale heterogeneity, reservoir architecture, and inferred flow parameters.

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 score0.408
Threshold uncertainty score0.486

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 head0.031
GPT teacher head0.266
Teacher spread0.236 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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