Random network modelling approach to investigate the single‐phase and quasi‐static immiscible two‐phase flow properties in the Mesaverde formation
Bibliographic record
Abstract
Abstract Understanding the microscopic flow behaviour of hydrocarbons and water in porous media gains importance as more and more reservoirs are being exploited. Network modelling techniques could be extended to tighter media as long as Darcy's law is applicable. 3D random networks are constructed in order to represent the Mesaverde formation which is located in north Wyoming, USA. The network modelling software solves the fundamental equations of single‐phase and two‐phase immiscible flow incorporating wettability and contact angle assuming a quasi‐static displacement mechanism. Macroscopic properties of the porous media network representation such as porosity, absolute permeability, and formation factor are calculated and whenever possible compared to experimental data. Subsequently, immiscible two‐phase flow properties such as capillary pressure, relative permeability, and resistivity curves are predicted and compared to available experimental data. The effect of interfacial tension alteration is also investigated as an attempt to demonstrate the capability of the network modelling technique to show physical fluid behaviour. It is observed that the capillary pressure curve obtained using MICP data can be used to calibrate and validate the network model generated to represent the sample. The study shows that the modified random network modelling technique is capable of modelling low permeable porous medium and predicting single‐phase and immiscible two‐phase flow properties assuming quasi‐static displacement mechanism.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".