Estimation of relative permeability and capillary pressure for tight formations by assimilating field production data
Bibliographic record
Abstract
A novel EnKF technique together with its detailed workflow has been developed and successfully applied to simultaneously evaluate relative permeability and capillary pressure for tight formations by history matching the field production data. The power-law model is firstly used to represent the relative permeability and capillary pressure curves, while its parameters are tuned automatically and finally determined once the production data have been assimilated completely. This technique has been validated by using a synthetic 2D reservoir model with two scenarios, where two-phase and three-phase relative permeabilities together with capillary pressure curves are evaluated, respectively. The estimated relative permeability and capillary pressure have been found to improve progressively and their corresponding uncertainties are mitigated gradually as more production data are assimilated. Finally, there exists an excellent agreement between both the updated relative permeability and capillary pressure curves and their corresponding reference curves, leading to excellent history matching results. As such, the uncertainties associated with both the updated relative permeability and capillary pressure curves and the updated production profiles are reduced significantly. The capillary pressure cannot be determined as accurately as the relative permeability due to its less sensitivity to the production data.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".