Three-Phase Pore-Network Modelling for Mixed-Wet Carbonate Reservoirs
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Bibliographic record
Abstract
Abstract Carbonate reservoirs have structural heterogeneities (triple porosity: pore-vug-fracture) and are mixed-to oil-wet. The interplay of structural and wettability heterogeneities impacts the sweep efficiency and oil recovery. The choice of an IOR or EOR process and the prediction of oil recovery requires a sound understanding of the fundamental controls on fluid flow in mixed-to oil-wet carbonate rocks and physically robust flow functions, i.e. relative permeability and capillary pressure functions. Obtaining these flow functions is a challenging task, especially when three fluid phases coexist. In this work we use pore-network modelling, a reliable and physically-based simulation tool, to predict three-phase flow functions. We have developed a new pore-scale network model for rocks with variable wettability. Unlike other models, this model comprises a novel thermodynamic criterion for formation and collapse of oil layers. The new model hence captures film/layer flow of oil adequately which impacts the oil relative permeability at low oil saturation and hence the accurate prediction of residual oil. Pore-networks extracted from pore-space reconstruction methods and CT images have been used as input for our simulations and the model comprises a constrained set of parameters that can be tuned to mimic the wetting state of a given reservoir. We have validated our model with available experimental data for a range of wettabilities. A sensitivity analysis has been carried out to investigate the dependency of relative permeabilities on layer collapse and film/layer flow under various wetting conditions. Additionally, WAG injection has been simulated with different lengths of so-called multi-displacement chains and different flood end-points. The flow functions generated by our model can be passed to the next scales (upscaling) to predict the oil recovery at the reservoir scale and we demonstrate this using a proof-of-concept study.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it