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Record W2740125724 · doi:10.5539/esr.v6n2p138

Digital Rock Approaches to Estimate the Impact of Early Quartz Cementation in Miocene Deepwater Sands Niger Delta Basin, Nigeria

2017· article· en· W2740125724 on OpenAlexvenueno aff
Ibukunoluwa S. Adeola, Jim Buckman, Gary Douglas Couples, Adewole John Adeola

Bibliographic record

VenueEarth Science Research · 2017
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsCementation (geology)QuartzCementScanning electron microscopeGeologyPorosityStructural basinMineralogyPermeability (electromagnetism)Geotechnical engineeringMaterials scienceGeomorphologyComposite material

Abstract

fetched live from OpenAlex

Quartz cement forming as syntaxial overgrowths is one of the most abundant cement type in sandstones. The rim and occluding cements develop around the surfaces of frame work grains and fill up pore spaces with no preferred orientation with grain surfaces. Imaging the various forms of quartz cement generation and development in 3D as it increases through time will help in further evaluation and better understanding of a reservoir in deep water sands in Niger Delta Basin.Petrographic analysis was performed on 10 Samples with micron resolutions of 0.675 and 0.337 per pixel. Scanning Electron Microscopy (SEM) using the Cathoduluminiscence (CL) and Back Scattered Electron (BSE) was employed in delineating the detrital quartz from the syntaxial quartz cements. Image J Software and Scandium Software were employed in binarizing the BSE image samples and study the iteration porosity, final porosity and permeability. 3DSlicer software was employed to produce 3D images for better understanding of the impact of early cement in the deep water sands. Two Phase Flow Models was also generated for each samples to outlines the effect of cementation.Quartz cement reduces porosity and peremability significantly at early stages of quartz cementation. Small local quartz overgrowths do join and link together with increasing cement precipitation hence gradually impeding porosity and drastically reducing permeability. Modelled results showed similar trends and this is an indication that when analysing a top reservoir unit, once the cementation is more than 6% the possibility of it being a good reservoir is relatively low.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.160
GPT teacher head0.431
Teacher spread0.271 · 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 designSimulation or modeling
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
Published2017
Admission routes1
Has abstractyes

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