A Novel Modeling Approach to Stochastically Evaluate the Impact of Pore Network Geometry, Chemistry and Topology on Fluid Transport
Bibliographic record
Abstract
Abstract Fine-grained sandstones, siltstones, and shales have become increasingly important to satisfy the ever-growing global energy demands. Of particular current interest are shale rocks, which are mudstones made up of organic and inorganic constituents of varying pore sizes. These materials exhibit high heterogeneity, low porosity, varying chemical composition and low pore connectivity. Due to the complexity and the importance of such materials, many experimental, theoretical and computational efforts have attempted to quantify the impact of rock features on fluids diffusivity and ultimately on permeability. In this study, we introduce a stochastic kinetic Monte Carlo approach developed to simulate fluid transport. The features of this approach allow us to discuss the applicability of 2D vs 3D models for the calculation of transport properties. It is found that a successful model should consider realistic 3D pore networks consisting of pore bodies that communicate via pore throats, which however requires a prohibitive amount of computational resources. To overcome current limitations, we present a rigorous protocol to stochastically generate synthetic 3D pore networks in which pore features can be isolated and varied systematically and individually. These synthetic networks do not correspond to real sample scenarios but are crucial to achieve a systematic evaluation of the pore features on the transport properties. Using this protocol, we quantify the contribution of the pore network’s connectivity, porosity, mineralogy, and pore throat width distribution on the diffusivity of supercritical methane. A sensitivity analysis is conducted to rank the significance of the various network features on methane diffusivity. Connectivity is found to be the most important descriptor, followed by pore throat width distribution and porosity. Based on such insights, recommendations are provided on possible technological approaches to enhance fluid transport through shale rocks and equally complex pore networks. The purpose of this work is to identify the significance of various pore network characteristics using a stochastic KMC algorithm to simulate the transport of fluids. Our findings could be relevant for applications that make use of porous media, ranging from catalysis to radioactive waste management, and from environmental remediation to shale gas production.
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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 | 0.001 |
| 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".