A dual porosity model of high-pressure gas flow for geoenergy applications
Bibliographic record
Abstract
This paper presents the development of a dual porosity numerical model of multiphase, multicomponent chemical–gas transport using a coupled thermal, hydraulic, chemical, and mechanical formulation. Appropriate relationships are used to describe the transport properties of nonideal, reactive gas mixtures at high pressure, enabling the study of geoenergy applications such as geological carbon sequestration. Theoretical descriptions of the key transport processes are based on a dual porosity approach considering the fracture network and porous matrix as distinct continua over the domain. Flow between the pore regions is handled using mass exchange terms and the model includes equilibrium and kinetically controlled chemical reactions. A numerical solution is obtained with a finite element and finite difference approach and verification of the model is pursued to build confidence in the accuracy of the implementation of the dual porosity governing equations. In the course of these tests, the time-splitting approach used to couple the transport, mass exchange, and chemical reaction modules is shown to have been successfully applied. It is claimed that the modelling platform developed provides an advanced tool for the study of high-pressure gas transport, storage, and displacement for geoenergy applications involving multiphase, multicomponent chemical–gas transport in dual porosity media, such as geological carbon sequestration.
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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 | 0.001 |
| 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) | 0.002 | 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".