Analysis of Uncertainty Introduced by Scaleup of Reservoir Attributes and Flow Response in Heterogeneous Reservoirs
Bibliographic record
Abstract
Summary Reservoir heterogeneities occur over a wide range of length scales, and their interaction with various transport mechanisms controls the performance of subsurface flow and transport processes. Modeling these processes at large scales requires proper scaleup of petrophysical properties that are autocorrelated or heterogeneously distributed in space, and analyzing their interaction with underlying transport mechanisms. A method is proposed to investigate and quantify the uncertainty in reservoir models introduced by scaleup. It is demonstrated that when the volume support of the measurement is smaller than the representative elementary volume (REV) scale of the attribute to be modeled, there is uncertainty in the conditioning data because of scaleup and that uncertainty has to be propagated to spatial models for the attribute. This important consideration is demonstrated for mapping total porosity for a carbonate reservoir in the Gulf of Mexico. The results demonstrate that in most cases, the uncertainty distributions obtained by accounting for the scaleup procedure successfully characterize the variability in the actual core and log data observed along new wells. Conventional reservoir models considering the well data as "hard" conditioning data fail to predict the "true" values. Following this discussion on scaling of reservoir attributes, a conceptual understanding of the scaling characteristics of flow responses such as recovery factor (RF) is provided, in terms of the mean and variance of RF at different length scales. Finally, a new technique is presented to systematically quantify the scaling characteristics of transport processes by accounting for subscale heterogeneities and their interaction with various transport mechanisms based on the volume averaging approach. The objective is to provide a tool for understanding the scaling relationships for RF using detailed fine-scale compositional reservoir simulations over a subdomain of the reservoir.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".