The Impact of Normalized Relative Permeability Data on Estimated Ultimate Recovery
Bibliographic record
Abstract
Abstract A typical hydrocarbon reservoir consists of multiple rock types or facies. In reservoir modelling, capturing the properties of each rock type is essential. Relative permeability data, which describes rock-fluid and fluid-fluid interactions is normally developed for each rock types and is often a tuning parameter in History Match (HM); a very important element of reservoir simulation. The objective of this paper is to analyze the effect of applying normalized relative permeability data to model a reservoir. The effect is examined using both manual and automatic History Match, and then predicting the Estimated Ultimate Recovery (EUR) in two scenarios. Relative permeability curves for each rock type are prepared using corey saturation function equations. The normalization phase requires using separate sets of equations, after which the normalized curve will be denormalized for input into the simulation model. The resulting relative permeability curve is then used in HM by applying manual and automatic methods, after which the resulting matches are used to predict recovery. The reservoir simulation output using the normalized relative permeability curve is then compared to the base case scenario, in which case, individual rock types relative permeability curves were utilized. Reservoir simulation outputs from normalized relative permeability curves were found to compare very well with outputs using individual rock type relative permeability, with improved efficiency, for the reservoir under study. Normalized relative permeability data, when used with sound engineering judgement, can be very efficient. Mostly desired as an essential part of History Match, having a single representative set of relative permeability data for the reservoir can improve efficiency and save costs.
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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 | 0.003 | 0.013 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".