Uncertainty Analysis of Smart Waterflood Recovery Performance in Clastic Reservoirs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent years, numerous laboratory studies have documented the benefits of smart waterflooding as an emerging enhanced oil recovery (EOR) process, along with a few successful field applications, notably clastic reservoirs. In most cases, there are undeniable inconsistencies between lab and field results. This process has led to unpredictable outcomes and misleading prediction of smart waterflooding projects. Hence, this work is conducted to evaluate uncertainties in smart waterflooding from laboratory to field-scale. An one-dimensional (1-D) reactive transport model was developed and validated with flooding experiments. Validation shows that combinations of various matching parameters can be used to interpret the experiment. Different realizations lead to different results when extended to 3-D heterogeneous model. The sensitivity of parameters like grid size and heterogeneity in full-field model majorly influences smart waterflooding performance, which is responsible for the inconsistencies. Therefore, these parameters should be considered in field-scale simulation to fully demonstrate the potential of smart waterflooding.
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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 | 0.001 |
| 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 it