A measure of facies mixing in data upscaling to account for information loss in the estimation of petrophysical variables
Bibliographic record
Abstract
Blocking facies information to a constant length prior to three-dimensional (3D) modelling is necessary with current 3D geostatistical modelling techniques. The high-resolution information from core and well logging must be upscaled to unify the scale to a target scale considered in building the 3D numerical models. A downside is the inevitable loss of information when the majority facies is assigned to each upscaled interval. The loss of such information could become problematic when dealing with small shale barriers in the middle of the reservoir or at the boundary of the facies transitions. This paper addresses the information loss by retaining as much information as possible in the upscaling process and proposes a metric to account for small-scale information that is mixed during the process: such a metric is referred to as facies mixing measure (FMM). Retaining more information in the upscaling process and utilizing that information to better model petrophysical properties is an important contribution. FMM is calculated during the upscaling step and is treated as a secondary property during petrophysical property modelling. Cross-validation with two different datasets demonstrates improvements in porosity estimation.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".