Conveners
MS15: 1.2
- Marwan Fahs (ENGEES-LHYGES)
- Hongkyu Yoon (Sandia National Laboratories)
MS15: 2.2
- Saeid Sadeghnejad (Institute for Geosciences, Applied Geology, Friedrich-Schiller-University Jena, 07749 Jena, Germany)
- Marwan Fahs (ENGEES-LHYGES)
MS15: 3.3
- Hongkyu Yoon (Sandia National Laboratories)
- Saeid Sadeghnejad (Institute for Geosciences, Applied Geology, Friedrich-Schiller-University Jena, 07749 Jena, Germany)
MS15: 4.1
- Serveh Kamrava (Colorado School of Mines)
- Ahmed H. Elsheikh (Heriot-Watt University)
MS15: 4.3
- Ahmed H. Elsheikh (Heriot-Watt University)
- Serveh Kamrava (Colorado School of Mines)
Geological CO₂ sequestration efficiency relies on pore-scale structural parameters. However, the complex, non-linear coupling among these parameters is difficult to quantify using traditional experimental correlations alone. In this study, we apply an explainable machine learning (ML) framework to uncover the dominant governing factors of CO₂ saturation, utilizing a high-fidelity dataset...
Capturing dynamic processes like pore-filling and snap-off using fast synchrotron X-ray micro-tomography enables time-resolved quantitative and qualitative analysis. However, time-resolved imaging often generates noisy, low-contrast images, and the resulting datasets are often large. These factors present challenges for effective and accurate 4D image segmentation. Frame-by-frame segmentation...
Deep geothermal energy exploitation relies heavily on predicting heat transport within highly heterogeneous porous formations. The multi-scale nature of subsurface geology (ranging from pore-scale variances to reservoir-scale fracture networks) coupled with the non-linear interaction between Darcy flow and advective-diffusive heat transfer, renders traditional numerical solvers computationally...
Machine learning is increasingly used to accelerate or replace pore-scale simulations of multiphase flow in porous media, yet a clear understanding of how different model classes perform—and fail—under realistic flow conditions remains limited. In particular, it is often unclear which modelling choices are most appropriate for capturing interfacial dynamics, geometry–flow coupling, and...
Coastal protection structures face increasing challenges from sea level rise, extreme weather events, and material degradation [1]. Innovative approaches such as cathodic protection, which induces the precipitation of calco-magnesian deposits (e.g., brucite and aragonite), provide promising strategies for reinforcing marine infrastructures in a sustainable manner [2-3]. However, the long-term...
The retention of suspended particles in porous media plays a critical role in a wide range of subsurface processes, including filtration, contaminant transport in environmental applications, and formation damage in subsurface energy applications. As flow with suspended particles flow through porous media, they may deposit or clog flow pathways, changing local porosity, and ultimately impacting...
Abstact:
The gas diffusion layer (GDL) of high-temperature proton exchange membrane fuel cells plays a critical role in regulating the coupled transport of species, heat, and charge. The simulation accuracy of these transport phenomena directly affects the predictive reliability of fuel cell performance. However, conventional computational fluid dynamics (CFD) simulations suffer from...
Immiscible two-phase flow in porous media exhibits different flow regimes depending on driving parameters like the capillary number and viscosity ratio. In the steady state, these regimes correspond to characteristic pore-scale flow patterns, such as ganglion flow and drop-traffic flow. By considering pairwise fluid-fluid correlations in the pores and maximizing the entropy, we derive a...
Accurate evaluations of thermodynamic equilibria are essential for pore-network modeling (PNM) of gas-condensate flow in porous media. However, repeated equation-of-state (EOS) calculations impose a significant computational burden, limiting the feasibility of large-scale, dynamic simulations. This work presents a neural network–based data-driven proxy framework, implemented using JAX, for...
High-fidelity pore-scale flow simulations are indispensable for characterizing transport phenomena in complex porous media. Techniques like the Lattice Boltzmann Method (LBM) and direct Stokes solvers explicitly resolve three-dimensional pore-space flow fields, capturing essential effects of pore connectivity, multiscale heterogeneity, and intricate boundary conditions. However, their...
Groundwater flow modeling in aquifers is a fundamental problem in hydrogeology, traditionally addressed using numerical or data driven models that require sufficient observational data and well-defined boundary conditions and high computational demands. However, in many real-world groundwater systems, available observation data are sparse, and boundary conditions are often poorly known or...
Accurate characterization of porous media at the pore scale is fundamentally challenged by two critical limitations: the scarcity of core data available only at discrete well locations, and the high spatial heterogeneity inherent in rock formations that renders small, randomly sampled sub-images non-representative of bulk core properties. This work introduces PCP-GAN, a tailored...
This study proposes an artificial intelligence (AI)-based framework for upscaling single-phase and two-phase quasi-static simulation results from small subsamples to larger porous media domains. Several simulation methods, including direct numerical simulation (DNS) and pore-network modeling (PNM), are employed to elucidate the transport phenomena within the pore space. While in DNS, the pore...
Three-dimensional multiphase segmentation of pore-scale X-ray CT imagery in porous media faces a fundamental bottleneck that extends beyond achieving high in-domain accuracy on individual volumes. A key limitation lies in the absence of artificial intelligence methods that can function as unified segmentation models across multiple samples. Existing deep learning approaches for porous media...
Flow and reactive transport in porous media are very important to improve our understanding of physical and chemical processes related to various geoscience and environmental applications such as enhanced geothermal systems, in-situ critical mineral and element recovery, unconventional resources recovery, and environmental fate and transport. One of the overarching challenges in improving...
The digital characterization of porous media is undergoing a profound transformation driven by Artificial Intelligence (AI). However, the adoption of deep learning in Digital Rock Physics (DRP) is often hindered by the fragmentation of scientific workflows requiring separate, disconnected tools for image visualization, data annotation, and model training. We present GeoSlicer, an open-source,...
Understanding the saturated and unsaturated flow in porous media by producing ceramic porous model samples with controlled morphology. By controlling the morphology over a large range of microstructure, the study aims to isolate the parameters influencing resin impregnation and permanent flow in porous media. This subject has been treated by the community with many different approaches [1]....
Across a wide range of energy and engineering applications, the performance of porous materials is strongly governed by their microstructure. In batteries, fuel cells, and hydrogen storage systems, microstructural features control key transport pathways and thus critically influence overall functionality. Accurate characterization therefore requires high-resolution (HR) three-dimensional (3D)...
Inverse microstructure design is a persistent challenge in materials engineering because structure-property relations are high-dimensional, stochastic, and expensive to evaluate. As a result, conventional optimization and surrogate-driven workflows often become impractical when the design space is large, and microstructures must satisfy multiple constraints. Here we present PoreFlow, a...
Relative permeability curves are one of the fundamental parameters in multiphase flow modelling, supporting applications that now extend into Carbon Capture and Storage and Underground Hydrogen Storage. These curves are traditionally obtained experimentally using sophisticated special core analysis instruments, resulting in a workflow that relies on a limited number of core plugs that cannot...
Accurate three-dimensional (3D) representations of porous microstructures are essential for predicting transport, mechanical, and reactive behavior in natural and engineered porous media. However, acquiring 3D datasets remains costly, technically demanding, and often infeasible for fragile or fine-grained materials such as clay-based systems. Recent deep generative approaches attempt to infer...
Thermochemical energy storage (TCES), where thermal energy is stored in a reversible chemical reaction in a porous powder bed, is a promising technology for large-scale and long-term thermal energy storage. Extensive research has been conducted on the subject for potential applications, including the capture of excess heat from industrial processes and the storage of energy in concentrated...
The need to increase experimental throughput and support time-resolved imaging of dynamic laboratory experiments motivates the reduction of acquisition time in X-ray microtomography. However, faster acquisitions inevitably lead to lower signal-to-noise ratios, since fewer photons contribute to each projection, resulting in reconstructions with increased noise levels and degraded structural...
This paper investigates the application of machine learning (ML) to model and predict drilling mud loss in subsurface formations with conductive natural fractures. Mud loss during drilling is a complex and costly issue that disrupts operations and increases non-productive time. The goal of this study is to develop an ML-based tool that leverages type-curves and physics-informed models to...
Porous monolith reactors are attractive supports for heterogeneous catalysis due to their high surface-to-volume ratios and intricate pore networks, which promote efficient contact between reactants and catalyst. However, performance indicators such as productivity, pressure drop, selectivity, and operational safety depend strongly on the underlying pore geometry and are often tightly coupled....
