Characterizing heterogeneity in a glaciofluvial deposit using architectural elements, Limehouse, Ontario, Canada
Bibliographic record
Abstract
Major hydrocarbon and groundwater reservoirs are commonly hosted within coarse-grained alluvial deposits that contain a high degree of sedimentary heterogeneity. This paper presents a detailed characterization of the sedimentary heterogeneity of fluvial–deltaic deposits using architectural element analysis (AEA). Sedimentological data collected from outcrop faces exposing Late Quaternary glaciofluvial deposits in southern Ontario, Canada, is recorded in 31 sedimentary logs. These logs are used to identify nine different facies types, including gravel facies (Gm, Gp, Gt), sand facies (Sr, Sp, St, Ss), and fine-grained facies (Fl and Fd). Variations in facies associations and geometries are defined by five architectural elements (AEs): sand complex (SC), gravel sheet (GS), fine-grained sheet (FS), gravel foreset body (GFB), and concave fill (CF) elements. The spatial arrangement of bounding surfaces (first- to fifth-order) and AEs allows the classification of six EAs, which, in this study, are defined as the largest-scale architectural subunits that allow for architectural-based mapping over a large area. EAs delineated in this study are sandy braided-river (EA1), delta-front (EA2), gravelly braided-river to delta-top (EA3), delta-front to lacustrine (EA4), braided-river to deltaic (EA5), and sand-dominated fluvial (EA6). AEA is utilized here to capture three levels of heterogeneity, which allow detailed reservoir characterization based on geometric objects and can be readily used for computer-based modelling. Outcrop analogue studies such as this one provide insight to the geometries of more deeply buried coarse-grained deposits that form potential reservoirs and enhance paleoenvironmental reconstruction of subsurface alluvial deposits in Canada and elsewhere.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".