Deposits of the sandy braided South Saskatchewan River: Implications for the use of modern analogs in reconstructing channel dimensions in reservoir characterization
Bibliographic record
Abstract
Abstract Estimation of the dimensions of fluvial geobodies from core data is a notoriously difficult problem in reservoir modeling. To try and improve such estimates and, hence, reduce uncertainty in geomodels, data on dunes, unit bars, cross-bar channels, and compound bars and their associated deposits are presented herein from the sand-bed braided South Saskatchewan River, Canada. These data are used to test models that relate the scale of the formative bed forms to the dimensions of the preserved deposits and, therefore, provide an insight as to how such deposits may be preserved over geologic time. The preservation of bed-form geometry is quantified by comparing the alluvial architecture above and below the maximum erosion depth of the modern channel deposits. This comparison shows that there is no significant difference in the mean set thickness of dune cross-strata above and below the basal erosion surface of the contemporary channel, thus suggesting that dimensional relationships between dune deposits and the formative bed-form dimensions are likely to be valid from both recent and older deposits. The data show that estimates of mean bankfull flow depth derived from dune, unit bar, and cross-bar channel deposits are all very similar. Thus, the use of all these metrics together can provide a useful check that all components and scales of the alluvial architecture have been identified correctly when building reservoir models. The data also highlight several practical issues with identifying and applying data relating to cross-strata. For example, the deposits of unit bars were found to be severely truncated in length and width, with only approximately 10% of the mean bar-form length remaining, and thus making identification in section difficult. For similar reasons, the deposits of compound bars were found to be especially difficult to recognize, and hence, estimates of channel depth based on this method may be problematic. Where only core data are available (i.e., no outcrop data exist), formative flow depths are suggested to be best reconstructed using cross-strata formed by dunes. However, theoretical relationships between the distribution of set thicknesses and formative dune height are found to result in slight overestimates of the latter and, hence, mean bankfull flow depths derived from these measurements. This article illustrates that the preservation of fluvial cross-strata and, thus, the paleohydraulic inferences that can be drawn from them, are a function of the ratio of the size and migration rate of bed forms and the time scale of aggradation and channel migration. These factors must thus be considered when deciding on appropriate length:thickness ratios for the purposes of object-based modeling in reservoir characterization.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".