Coupling of Stress Dependent Relative Permeability and Reservoir Simulation
Bibliographic record
Abstract
Abstract Geomechanics is increasingly being considered for inclusion in reservoir simulation, since conventional simulators do not honor deformation resulting from the interaction between stress and fluid flow response in a porous medium. When a reservoir responds to changes in effective stress, the bulk volume adjusts, changing the pore geometry and dependent parameters like porosity, absolute permeability and effective permeability, and phase saturations. Most of the recently developed sequentially coupled approaches for coupling flow and geomechanics have focused on updating porosity and absolute permeability while changes in relative permeability (due to geomechanics) is ignored. For multiphase flow systems, relative permeability functions are one of the most influential parameters controlling fluid movement and distribution. To examine how geomechanically-influenced relative permeability may impact flow, a sequentially coupled reservoir geomechanical simulation study was conducted. The simulation workflow incorporated automatic updates of the relative permeability table for each grid block in the model at every time-step. Data for populating the geomechanical relative permeability tables was extracted from recent experimental test results reported in the literature. Results from the simulation studies showed a significant difference in recovery factors when the impact of geomechanics on relative permeability functions was integrated into coupled simulation compared to when only changes in porosity and absolute permeability were used. Coupled models which incorporate not only the change in permeability and porosity but also the changes in relative permeability can lead to more realistic production forecast especially for reservoirs under improved or enhanced oil recovery scheme as found in heavy oil and oil sands projects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".