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Record W3128541065 · doi:10.1155/2021/8853639

Effect of Lamination on Shale Reservoir Properties: Case Study of the Montney Formation, Canada

2021· article· en· W3128541065 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueGeofluids · 2021
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsGeological Survey of Canada
FundersKorea Institute of Energy Technology Evaluation and PlanningMinistry of Trade, Industry and EnergyNational Research Foundation of KoreaMinistry of EducationNational Research Foundation
KeywordsLaminationGeologySiltPorosityMineralogySedimentary rockFaciesDiagenesisOil shaleGrain sizeGeotechnical engineeringGeochemistryComposite materialGeomorphologyMaterials science

Abstract

fetched live from OpenAlex

The presence of lamination on sedimentary rocks is a distinct characteristic, particularly in shales. They are distinct due to the contrast between successive layers with regard to grain size, composition, color, and sedimentary structures, such as graded beds. Typically, the degree of lamination is controlled by the sedimentation rate and flow regime. Herein, we developed a mudstone classification scheme in terms of lamination because lamination-based shale facies are related to differing features in mineral composition, porosity, and Young’s modulus. This study also attempts to verify whether wireline log patterns are relevant to shale lithofacies. The relationship between the porosity and lamination of the Montney Formation can be used to estimate reservoir properties. Our results show that an increased silt lamina in mudstone leads to an increase in the quartz and calcite contents and a decrease in the clay content, which increases the porosity, permeability, and Young’s modulus. However, reservoir quality is not solely dependent on lamination because of the complex interaction between components. The degree of lamination affected the neutron, density, and sonic log responses. Furthermore, the presence of lamination tends to decrease the neutron percentage, with similar trends in density and sonic log box plots in the study area. When the percentage of clay or cement material decreases, the neutron and density log responses diminish. Meanwhile, when the rock texture variation increases with an increase in the degree of lamination, the sonic log response decreases.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.945

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 head0.010
GPT teacher head0.209
Teacher spread0.199 · 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