A Soft and Law-Abiding Framework for History Matching and Optimization under Uncertainty
Bibliographic record
Abstract
Abstract Current frameworks for optimization and assisted history matching lack the ability to control and guide the sampling engine and to incorporate geo-engineering knowledge. Defining the interactions between uncertain parameters and handling multiple constraints are also arduous tasks. Despite recent advances in adaptive population-based sampling algorithms and other gradient and ensemble-based methods, these specific drawbacks have left engineers with several history-matched models that are inconsistent with the physical and geological knowledge of the field. We introduce a novel rule-based framework based on fuzzy reasoning to integrate engineering knowledge with optimization and assisted history matching workflows. The system can handle multiple complex constraints both in parameter and objective function space. The use of fuzzy set theory in this workflow is a natural way to address uncertainty arising from imprecision of definition. This type of uncertainty is important in expressing the parameters of interest; however, it has been less addressed in existing workflows. The proposed system can be coupled with any algorithm used for assisted history matching, including gradient-based, population-based and particle filter approaches. The framework is coupled with differential evolution algorithm and is tested for three cases. The results show that fuzzy rule-based engine preserves the computational efficiency of the sampling engine, while allowing for definition of flexible rules in history matching and optimization that honor engineering knowledge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".