Presentation materials
Reactive dissolution of solid minerals in porous media is a critical process underlying numerous subsurface applications, including carbon capture and storage (CCS), geothermal reservoir management, and oil & gas recovery. However, direct numerical simulators for modelling reactive flow and mineral dissolution often prove computationally prohibitive. To address this challenge, deep-learning...
Porous media, such as soils and aquifers, play a crucial role in various environmental and industrial processes, including groundwater management, pollution control, and resource extraction. Modeling the transport of reactive contaminants within these media involves complex interactions between physical properties (e.g., porosity and permeability) and chemical reactions. Traditional numerical...
Integration of indirect monitoring measurements into subsurface flow models often leads to nonlinear and non-Gaussian data assimilation problems. Practical data assimilation methods, such as the ensemble Kalman filter, rely on Monte-Carlo approximation with small sample sizes for error propagation in dynamical systems. The resulting sampling errors introduce additional data assimilation...
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. It has been under long-standing investigation for prospective applications, such as the capture of excess heat from industrial processes or storing energy in concentrated solar power...
Reinforcement Learning (RL) has recently gained traction as a promising tool for solving challenging control and optimization problems in porous media. In particular, subsurface reservoir management is a critical application domain, where optimal injection and production strategies can significantly enhance recovery while minimizing operational costs. However, purely model-free RL approaches...
As computational strength keeps growing, deep learning has emerged as a powerful technique for addressing complex tasks and solving problems with intricate logic. Researchers are starting to leverage deep learning methods to tackle all kinds of challenges, including inversion problems in different materials. However, training deep neural networks (DNNs) for such tasks requires extensive...
Forecasting reservoir pressure and CO₂ plume distribution in geological carbon storage (GCS) demands the efficient integration of monitoring data with reservoir simulations. Traditional inverse modeling methods often rely on restrictive linear or Gaussian assumptions, limiting their predictive accuracy for complex state variables. Moreover, simulating large-scale three-dimensional (3D) GCS...
As the deadline to reach the Net Zero pledges outlined in the Paris Accords approaches, the need for each country to find economically viable renewable energy sources is a priority. Since 2018, the Netherlands has been involved in expanding its knowledge of its geothermal potential through the SCAN (Seismische Campagne Aardwarmte Nederland) project, which aims to expand the data coverage to...
Packed-bed reactors are widely used due to their efficient heat and mass exchange. The hydrodynamics of these reactors is largely influenced by particle-fluid interactions. However, a unified theory representing the effect of particle shape on flow distribution and global parameters such as pressure drop is not well-described in the literature. Studying the hydrodynamics in a packed bed using...
Fluid flow in porous media plays a crucial role in many environmental and energy sciences applications, including groundwater management, hydrogen and carbon storage, and fuel cells. However, the numerical modeling of such processes remains a very challenging task. Traditional numerical simulation methods often struggle to rapidly and accurately predict pore-scale flow processes in porous...
Fluid flow and transport phenomena in heterogeneous porous media have diverse applications in science and engineering fields. These processes are dominated by steep gradients, non-linear interactions, and multi-scale phenomena, rendering the governing equations, aka PDEs, exceedingly complex and computationally demanding to solve. Although decades of research have led to several breakthroughs...
Accurately predicting fluid flow through fractured media remains a major challenge due to the disparity between simple modeling assumptions and the complex reality of fracture geometry. Fractures at the continuum-scale are represented as very simple geometries, but in reality, fractures are quite complex. Assuming a parallel plate-like geometry in our numerical models can yield very high...
Capillary pressure plays a crucial role in multiphase transport and has applications in carbon dioxide sequestration and underground hydrogen storage. Characterizing it is challenging when rock samples are unavailable; thus, it is often estimated using the J function, but the scaled results are scattered. This presentation discusses a new approach for estimating capillary pressure using the...
The Pajarito Plateau shallow aquifer is a crucial resource for Los Alamos town, New Mexico. Changes of the shallow aquifer level could have a large environmental impact where water scarcity can concentrate pollutants and negatively affect the local ecosystem. The shallow aquifer recharge is poorly understood because of the area's complex geology, and is extremely sensitive to climate change....
Deep neural networks have been explored in predicting single-phase flow properties within pore-scale porous media domains. However, their application to two-phase flow scenarios is limited in the literature.
The complexity of two-phase flow arises from fluid-fluid interactions, domain geometry, and the non-linear behavior at the fluid interface. Additionally, porous media domains are not...
Real-time, high-resolution estimates and predictions of soil moisture (SM) data could significantly enhance the forecasting of SM- and precipitation-related extreme events, such as floods, droughts, and wildfires. Current estimates and short-term predictions of SM data can serve as leading indicators for upcoming anomalies in vegetative growth and productivity, improve irrigation scheduling,...
A key task in the oil industry is the accurate characterization of pre-salt carbonate reservoir rocks, which display complex heterogeneity at multiple scales. These rocks’ intricate geological structure, shaped by a range of diagenetic processes—such as cementation, dissolution, and fracturing—significantly influences their petrophysical properties [1]. These characteristics demand for...
The microstructure of (composite) materials is essential in assessing their performance in applications such as fuel cells, hydrogen storage and batteries. High-resolution microstructural data plays a critical role in optimizing the properties and functionalities of these materials. However, conventional imaging methods, such as CT scanning and FIB-SEM, are sometimes limited by their high...
Accurate identification or segmentation of multiple minerals within digital rock images is critical for ensuring the reliability of subsequent analyses. Recently, deep learning models have remarkably improved the accuracy and efficiency of segmentation. However, supervised learning methods necessitate a large volume of segmented labels for training, while unsupervised learning methods lack the...
Non-destructive characterization of printed circuit boards (PCBs) is crucial for ensuring the reliability of electronic components. Defects like cracks and pores in solder joints significantly influence the performance of PCBs. Microcomputed tomography (µCT) has proven effective in detecting such pores and quantifying relevant characteristics like diameter, volume, and shape. The current study...
Machine learning (ML) models have been widely used as efficient surrogates for costly molecular simulations to predict gas adsorption in nanoporous materials for gas storage and separation applications. The “black box” nature of ML, however, often limits its ability to guide the discovery and design of novel nanoporous materials. In this work, we introduce PoroNet, a new graph neural network...
