Simultaneous Estimation of Relative Permeability and Capillary Pressure for Tight Formations from Displacement Experiments
Bibliographic record
Abstract
Abstract An ensemble-based technique has been developed and successfully applied to simultaneously estimate the relative permeability and capillary pressure in a tight formation by history matching the conventional measurement data from displacement experiments. Relative permeability and capillary pressure curves are represented by the power-law model. Then, the to-be-estimated coefficients of the power-law model are tuned automatically and finally determined once the measurement data have been assimilated completely and history matched. This new technique has been validated by a synthetic coreflooding experiment and then extended to a real coreflooding experiment. Simultaneous estimation of relative permeability and capillary pressure has been found to improve, while standard deviation of the estimated coefficients is reduced gradually as more measurement data is assimilated. There exists an excellent agreement between both the updated relative permeability and capillary pressure and their corresponding reference values, once all the measurement data are assimilated. The relative permeability can be determined more accurately than the capillary pressure owing to the fact that it is more sensitive to the conventional measurement data. This newly developed technique has good computation efficiency and is suitable for performing uncertainty analysis under the same framework.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".