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Record W4306291336 · doi:10.2113/2022/1232390

Quantifying Centimeter- to Microscale Heterogeneities in Sedimentary, Compositional, and Geomechanical Properties of Siltstone Deposits in the Lower Triassic Montney Formation, Northeastern British Columbia, Canada

2022· article· en· W4306291336 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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueLithosphere · 2022
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSiltstoneGeologySedimentary rockSedimentary structuresGeochemistryFaciesSedimentary depositional environmentMineralogyGeomorphologyStructural basin

Abstract

fetched live from OpenAlex

Abstract Geological and geomechanical heterogeneities exist at multiple scales in fine-grained rocks; however, the complexity of characteristics at the centimeter- to microscale heterogeneities remains poorly understood. In this study, 10 representative samples composed of three centimeter-scale sedimentary fabrics (massive siltstone (F1), stratified siltstone (F2), and bioturbated siltstone (F3)) were analyzed from the Lower Triassic Montney Formation in the Western Canada Sedimentary Basin to describe sedimentological heterogeneity based on sedimentary fabric, compositional, and geomechanical properties. Sedimentary fabric was determined based on grainsize and the distribution of bedforms, which subdivide the facies into four μm- to mm-scale microfacies (massive siltstone (MF1), pinstriped laminated siltstone (MF2), planar- to cross-stratified siltstone (MF3), and bioturbated siltstone (MF4)). Microscale analysis using a scanning electron microscope was used to characterize microfacies and their respective mineralogical makeup (matrix, cement, and framework grains). To quantify heterogeneity, sedimentary fabric was assessed using a CT scan complemented by elemental composition (using X-ray fluorescence), and geomechanical hardness (using Equotip Piccolo Bambino handheld microhardness tool) was collected within a 1 cm by 1 cm grid within each sample. Datasets were compared using a discriminant analysis (DA) to recognize trends between multiple properties and suggest that sedimentary fabric with the highest centimeter-scale aluminum content from XRF (avg. 11%) comprises microfacies that are comparatively matrix-rich consisting of micas, negligible calcite cement, and exhibit the lowest handheld hardness values (<770). Alternatively, sedimentary fabric with a higher elemental calcium component (avg. 18%) comprises microfacies that are matrix-poor, cemented by carbonate (calcite and dolomite) and quartz, and overall exhibit a positive trend with hardness measurement (770–850). Furthermore, to relate the elemental and geomechanical proxies to controls on rock mechanics, natural calcite-filled fractures within the studied core intervals were characterized. Fractures were subdivided into three types—brecciated, bed-parallel, and vertical to subvertical fractures with each type being constrained to a specific sedimentary fabric. Based on centimeter gridding, microscale analysis and the degree of fabric interbedding play a primary role on the variability in mechanical hardness and the geometry and termination of natural fractures. Collectively, this dataset provides insight into the influence that sedimentary fabric and the distribution of elemental composition has on mechanical properties and natural fractures below well log resolution. These findings can be used to better model and predict fine-grained deposit characteristics before undergoing hydraulic stimulation.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.392
Threshold uncertainty score0.408

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 head0.015
GPT teacher head0.194
Teacher spread0.179 · 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