Evaluating covariance-based geostatistical methods with bed-scale outcrop statistics conditioning for reproduction of intra-point bar facies architecture, Cretaceous Horseshoe Canyon Formation, Alberta, Canada
Bibliographic record
Abstract
Geostatistical characterization of petroleum reservoirs typically suffers from problems of sparse data, and modelers often draw key parameters from analogous outcrop, numerical, and experimental studies to improve predictions. While quantitative information (bed-scale statistical distributions) from outcrop studies is available, translating the data from outcrop to models and generating geologically-realistic realizations with available geostatistical algorithms is often problematic. The overarching goal of this thesis is to test the capacity of covariance-based geostatistical methods to reproduce intra-point bar facies architecture while guiding those algorithms with bed-scale outcrop statistics from the Late Cretaceous Horseshoe Canyon Formation in southeastern Alberta. First, general facies architecture reproduction is tested with 2- and 3-facies synthetic and outcrop-based experiments with variable hard data, soft data weight, and soft data reliability. Next, 3-D sector models compare performance of different geostatistical simulation methods: sequential / co-sequential indicator, plurigaussian, and nested truncated gaussian. Findings show that despite integration of outcrop statistics, all conventional covariance-based geostatistical algorithms struggle to reproduce complex facies architecture that is observed in outcrop. Specifically, problems arise with: 1) low-proportion facies and 2) a weak statistical relationship between hard data (measured sections) and soft data (probability models). Nested modeling partially mitigates low-proportion issues and performs better as a result.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".