Scale Up of Pore-Network Models into Reservoir Scale: Optimization of Inflow Control Devices Placement
Bibliographic record
Abstract
Summary Improvements to more advanced tools, such as inflow control devices (ICDs), create a high drawdown regime close to wellbores. Gas liberation within the formation occurs when the drawdown pressure is reduced below the bubblepoint pressure, which in turn reduces oil mobility by reducing its relative permeability, and potentially reducing oil flow. The key input in any reservoir modeling to compare the competition between gas and liquid flow toward ICDs is the relative permeability of different phases. Pore-network modeling (PNM) has been used to compute the relative permeability curves of oil, gas, and water based on the pore structure of the formation. In this paper, we explain the variability of pore structure on its relative permeability, and for a similar formation and identical permeability, we explain how other factors, such as connectivity and throat radius distribution, can vary the characteristic curves. By using a boundary element method, we also incorporate the expected relative permeability and capillary pressure curves into the modeling. The results show that such variability in the pore network has a less than 10% impact on production gas rates, but its effect on oil production can be significant. Another important finding of such modeling is that providing the PNM-created relative permeabilities may provide totally different direction on setting the operational constraints. For example, in the case studied in this paper, PNM-created relative permeability curves suggest that a reduction of flowing bottomhole pressure (FBHP) increases the oil rate, but for the case modeled with a Corey correlation, changes in FBHP will not create any uplift. The results of such work show the importance of PNM in well completion design and probabilistic analysis of the performance, and can be extended based on different factors of the reservoir in future research. Although PNM has been widely used to study the multiphase flow in porous media in academia, the application of such modeling in reservoir and production engineering is quite narrow. In this study, we develop a framework that shows the general user the importance of PNM simulation and its implementation in day-to-day modeling. With this approach, the PNM can be used not just to provide relative permeability or capillary pressure curves on a core or pore- scale, but to preform simulations at the wellbore or reservoir scale as well to optimize the current completions.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".