Characterization of complex fluvial architecture through outcrop studies – dealing with intrinsic data bias at multiple scales in the pursuit of a representative geomodel
Bibliographic record
Abstract
Abstract The practice of building analog models and training images from outcrop exposures is an important tool in better predicting subsurface facies distribution in the petroleum industry. As with subsurface data, however, incomplete information and data bias can lead to inaccurate characterization of outcrop geology at multiple scales. Cretaceous fluvial strata of Wyoming offers excellent exposure of two systems — the sand-rich and highly amalgamated Trail Member of the Ericson Sandstone and the sand-poor, isolated channels of the Dry Hollow Member of the Frontier Formation. For each system, multiple outcrops were characterized through the traditional means of stratigraphic column measurement, as well as through photogrammetric survey acquisition and interpretation. We saw in both studies that, despite an effort to measure sections that were representative of the entire outcrop, measured sections consistently overestimated the reservoir proportions. Ten measured sections within the Trail Member show a Net-to-Gross (NTG) ranging from 50–80% sandstone, with an average of 72%. A more complete spatial characterization of the entire outcrop through photogrammetric interpretation suggests a much lower NTG of 53%. Similarly, for the Dry Hollow Member fluvial strata, measured sections show NTG ranges of 8–50% with an average of 37% sandstone, while the photogrammetric model shows a NTG of only 16%. These differences are significant and lead to very different reservoir models. Further, the assumption is commonly made that the outcrop, if well characterized, is representative of the formation at a larger scale. Models of the Dry Hollow Member at Cumberland Gap show that this is a tenuous assumption and can lead to models that are not representative of the system. Outcrops of the Dry Hollow are sparse and often discontinuous, and extrapolation of calculated facies proportions between two well-exposed outcrops at Cumberland Gap led to significant placement of sands between the outcrops, where the lack of exposure leads to a lack of control data in the model. This resulted in increased reservoir connectivity that is not representative of the system, and shows that even on a sub-kilometer scale, the extrapolation of detailed, quantitative facies proportions can be inappropriate, and if done blindly can lead to an inaccurate characterization of the system. Through detailed characterization of the Trail and Dry Hollow fluvial systems, it is shown that building quantitative geomodels from outcrop exposures, even using modern techniques such as photogrammetric analysis, can be subject to significant bias and mischaracterization at multiple scales and for multiple reasons if care is not taken.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".