Estimation of Relative Permeability by Assisted History Matching Using the Ensemble Kalman Filter Method
Bibliographic record
Abstract
Summary An ensemble-based history technique has been applied to implicitly estimate three-phase relative permeability curves from production data. A power law representative of relative permeability curves is used. Both endpoints and shape factors of relative permeability curves are included in state vectors that are updated sequentially by assimilating observation data. This method has been validated by accurately evaluating relative permeability in a synthetic reservoir with 2D, three-phase flow. It is shown from the synthetic case that good estimation of relative permeability curves can be obtained by assimilating the observed oil rates, gas/oil ratios, and bottomhole pressures of production wells. Both shape factors and endpoints of relative permeability curves are accurately evaluated; however, a larger ensemble size is needed to avoid filter divergence. Compared with the existing implicit methods, the ensemble-based history matching technique does not require the gradient of the objective function, which makes the technique easy to implement.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".