Uncertainties - Extension of Smart Waterflooding from Core to Field Scale
Bibliographic record
Abstract
Summary Smart waterflooding has emerged as an EOR process of much interest in recent years. Much research is being reported, along with a few successful field applications, notably clastics. In most cases, there are undeniable inconsistencies in results between lab and field cases. This leads to unpredictable outcomes and misleading profit prediction of smart waterflooding projects. The objective of this work is to evaluate uncertainties in smart waterflooding from core- to field-scale. Kozaki (2012) experiment is mimicked by 1-D numerical model that couples with geochemical reactions. Validation results show that there are many combinations of matching parameters that can describe coreflooding results. Each realization may lead to different results when extended to 3-D heterogeneity model. Hence, to cover ranges of uncertainties, many realizations should be tested before summarizing smart waterflooding performance. Full-field heterogeneity model also shows that smart waterflooding is sensitive to grid size and heterogeneity. With different grid volume settings, results vary dramatically. This may contribute towards smart waterflooding misinterpretation. Furthermore, heterogeneity alters smart waterflooding within a particular range by affecting cation exchange capacity, and subsequently interpolant value, which is used to represent system wettability. Therefore, these parameters should be accounted in field-scale simulation to obtain smart waterflooding true potential.
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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 | 0.000 | 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 | 0.000 |
| 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 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".