Coupled Capillary Pressure and Relative Permeability Using an Equation-of-State Approach
Bibliographic record
Abstract
Abstract Accurate and continuous capillary pressure (Pc) and relative permeability (kr) models are key relations in modeling of enhanced oil recovery (EOR) processes. Current commercial reservoir simulators tune empirical models for relative permeabilities and capillary pressures to experimental data based solely on a limited set of data under immiscible conditions. These empirical models attempt to represent very complex compositional processes, even though they are only a function of phase saturation and type. Thus, "fully" compositional models that use these empirical relations are not fully composition and discontinuities in compositions and saturations result. These discontinuities lead to failed simulations, significant inaccuracies and increased computational time. This paper develops a coupled equation-of-state (EoS) kr-Pc model that can reproduce important features of the current empirical models, but also yield physically consistent predictions that cannot generate discontinuities. The model parameters use the same inputs for both relative permeability and capillary pressure and are tuned simultaneously. We focus here on capillary hysteresis and understanding the components of the EoS from measured data using saturation, phase distribution (Euler characteristic or phase contact area), and wettability as inputs. The new EoS Pc model maintains a similar functional form as the common Brooks-Corey correlation, and can predict capillary pressure away from the tuned experimental data. The results using CT scans of imbibition and drainage processes show excellent agreement once contact angle hysteresis is included. A quadratic response surface is used to understand better the functional form of the EoS, i.e. partial derivative expressions. The new coupled kr-Pc approach could improve compositional simulation by making it faster, more robust, and accurate since these key parameters are more continuous and physical.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".