Institute for Energy Technology

Understanding and quantifying the flow of multiple pore fluid phases is a key component to understanding many natural and engineered reservoir processes. A well-known example is the migration of hydrocarbon phases in sedimentary reservoirs, including natural migration, conventional production, and enhanced hydrocarbon recovery. As Carbon Capture and Storage (CCS) emerged as a potential strategy to combat climate change, understanding multiphase flow processes of supercritical CO2 and the various pore fluids is at the forefront of scientific interest.Our governing equations are based on the conservation of mass and momentum in two immiscible fluid phases. The fluid phases may be compressible or incompressible. We assume the inertial terms to be negligible in both phases and momentum transfer to happen in the Darcy-flow regime. Multiple fluid phase effects are introduced in the mathematical model by saturation-dependent relative permeabilities and fluid viscosities, which uniquely determine the total mobility, relative mobility, and fractional fluid flow curves. Here we consider capillary effects negligible. This results in two independent equations: an elliptic equation for fluid pressure and a hyperbolic equation for saturation.We choose a staggered-grid-based finite-difference discretization. The elliptic fluid pressure equation is solved using pseudo-transient iterations. The hyperbolic saturation equation is solved using a conservative first-order upwind scheme. Multiple coupling and time-stepping options (e.g., explicit/implicit, first order/higher order) are tested against an analytical solution. The numerical scheme can be implemented on GPUs in a straightforward manner using the ParallelStencil.jl package in Julia. We will provide some examples of various reservoir applications, which will also be used as performance benchmarks. Finally, we will discuss the potential of the presented numerical scheme to be implemented in a broader THMC framework.

The presence of liquid petroleum trapped within low-permeability vesicular basalt or mafic intrusions is more than a scientific curiosity, as it also challenges the conventional fluid migration and storage models in porous media. Its presence is significant because it may suggest complex primary magmatic porosity development, low temperature alteration and resulting fluid pathways, nano-scale deformation, or other processes leading to secondary porosity development. Thus the cases offer insights into processes controlling hydrocarbon and fluid flow systems in volcanic areas. In this contribution, we present the study of a Permian dolerite dyke at Tvedestrand in Norway and a vesicular basalt sample from West Greenland, both of which contain liquid oil. The Tvedestrand dyke is emplaced in Precambrian gneiss that was previously overlain by Cambrian black shales. The Greenland basalt is part of the North East Atlantic igneous province, underlain by petroleum-bearing sedimentary strata. Organic geochemistry indicates both oils to be immature, hence suggesting migration through these rocks after emplacement and cooling. We also show ultrahigh-resolution photographs in natural and ultraviolet light to show the distribution of petroleum compounds and image the microstructures controlling fluid migration. The parameters controlling fluid migration in tight crystalline rocks are important to constrain, as these are directly relevant to hydrocarbon exploration in unconventional settings and for hydrogen or carbon storage. We discuss the most likely mechanisms enabling fluid migration in these systems and their broader implications.

The ongoing energy transition and technological advancements present increasingly complex challenges for numerical modeling, necessitating the development of multi-physics, multi-scale approaches. Recent progress in high-performance computing has catalyzed the rapid evolution of a new generation of numerical codes designed to tackle these complex problems. However, this progress demands revisiting and refining constitutive models to ensure they are rigorous, thermodynamically consistent, and suitable for computational implementation. In this work, we present a thermodynamic framework for coupled thermo-hydro-mechano-chemical processes in porous media undergoing elastic, viscous, and plastic deformation. The formulation is developed in an Eulerian description and assumes local thermodynamic equilibrium for each phase. By enforcing the second law of thermodynamics through non-negative entropy production, we derive a complete and thermodynamically admissible set of governing equations and closure relations for visco-elasto-plastic porous materials with thermal and chemical coupling. A key result is the identification of the conjugate thermodynamic force associated with porosity, which provides a consistent basis for formulating viscous, plastic, and reaction-induced porosity evolution. The equilibrium (elastic) closure relations are derived from the symmetry properties of the thermodynamic potentials, yielding a compliance matrix that unifies poroelastic, thermoelastic, and thermo-porous couplings. Classical limits are recovered naturally, including Biot and Gassmann relations for homogeneous matrices, Brown–Korringa relations for heterogeneous solids, and Darcy’s law as the low-frequency limit of the dynamic momentum balance. The framework also clarifies the role of inertial (added-mass) effects and relates them to pore-scale tortuosity. The derived equations are implemented in a numerical code, and a numerical example illustrating the propagation of a porosity wave in a viscoelastic medium is presented.

ABSTRACT The digital economy has established data as a fundamental asset, shaping industries, governance, and technological innovation while driving competitive advantage across sectors. The proliferation of data‐generating technologies like IoT and social media has transformed data into a strategic resource, particularly benefiting Big Tech companies. This study examines data's dual nature as both asset and commodity, analyzing market structures, monetization challenges, and emerging technology impacts on the data economy. Employing a multivocal literature review (MLR) approach, this research systematically reviewed academic literature and gray sources including white papers and industry reports. Comprehensive searches across five major databases such as ACM digital library, IEEE Xplore, ScienceDirect, SpringerLink, and Wiley Online Library yielded 764 records, with an additional 86 from other sources (i.e., gray‐literature), totaling 850 screened studies. Ultimately, 64 studies addressed five key research questions on data's economic characteristics, market structures, monetization challenges, technological impacts, and public‐commercial balance frameworks. Analysis reveals data's distinctive economic properties like non‐rivalry, non‐excludability, and scalability necessitating novel valuation approaches. Market dynamics are significantly influenced by regulatory frameworks like GDPR, while emerging technologies such as AI and blockchain reshape monetization strategies. Key challenges include accurate valuation, data quality assurance, and privacy compliance. Data's economic essence defies conventional valuation frameworks due to its unique properties. AI and blockchain are transforming data markets through enhanced value extraction and secure transactions. However, robust governance frameworks like data commons and trusts are essential for balancing monetization with ethical use, requiring future research on comprehensive valuation models integrating technological advancements with public interest alignment. This article is categorized under: Application Areas > Business and Industry

Software Fault Prediction (SFP) is the process of predicting fault-prone software constructs during the initial phases of software development. Deep Neural Networks (DNN) have been hugely successful in the field of computer vision, audio, etc., where the input data is correlated spatially and temporally. In contrast, SFP operates on tabular data, rows of software metrics that lack the inherent spatial structure exploited by convolutional architectures in image domains. The authors have tried to remodel the most successful Convolutional Neural Network (CNN) for tabular data. A novel framework is proposed employing a tree-based feature selection technique, LSelect, to find the most significant features and an adaptive 1-dimensional Convolutional Neural Network (ACNN) for the classification task, which selects an optimal learning rate automatically. ACNN converts the tabular data (1-D) into 2-D using adaptive pooling layers, thereby forming an image from 1-D data. The framework classification results (Area under Curve) are compared with nine state-of-the-art algorithms, such as XGBoost, LightGBM, etc., and performance is validated using the Bayesian Signed Rank Test. It is found that the proposed framework performs comparably with the state-of-the-art methods with reduced model complexity. Also, the LSelect feature selection technique improves average model performance by 1.3%.

The correlation between hydrogen content and boron-oxygen related degradation is under debate. In this study surface passivated boron doped silicon wafers with different hydrogen content were illuminated at room temperature with three different light intensities. Their lifetime degradation was monitored with quasi-steady state photoconductance. A characteristic degradation that is separated into a fast and a slow decay characteristic for all light intensities is expected for such wafers. However, with increasing hydrogen content, we observe that the initial lifetime prior to illumination decreases, reducing the fast decay part of the degradation curves. For low intensity illumination the samples with reduced initial lifetimes follow the predicted degradation curve and show only the slow decay. For higher light intensities the degradation curves differ considerably; there is first a temporary lifetime increase before the decay sets in.

Metal hydrides are known for their outstanding performance as materials for hydrogen storage and processing. These materials find applications for short- and long-term energy storage, compression and supply of hydrogen gas, thermal energy storage, as electrodes and electrolytes in rechargeable batteries, for the microstructural optimisation of functional materials, in thin film technologies, as catalysts, getters and in many other uses. After the discovery of the first binary metal hydrides back in the 19 th century, their studies covered all possible binary M-H systems and expanded rapidly into the field of ternary hydrides following the recognition of the excellent hydrogen storage performance of LaNi 5 - and TiFe-based materials, which operate efficiently at room temperature and at near-ambient H 2 pressures. This review aims to provide an overview of the early works, as well as selected recent results on various classes of metal hydrides. It also covers the recent activities from the major contributing countries and continents, including USA, Europe, Japan, China and Australia. These studies relate to achieving the hydrogen storage systems goals set by the Department of Energy in the United States which inspired the research activities at the national and international level, through execution of the tasks on hydrogen-based energy storage managed by the International Energy Agency. The review is prepared by international experts in the field and covers the most important past developments and also presents the recent achievements in the field. • Discovery of binary hydrides and early works on ternary hydrides • LaNi 5 H 6.7 , TiFeH 1.9 , Mg 2 NiH 4 • Developments of metal hydrides in US, Europe, Australia, Japan, China • International Energy Agency activities on hydrogen storage • Goals in the development of hydrogen storage materials and systems

ABSTRACT The European Union's commitment to increasing renewable energy use emphasizes the relevance of solar thermal collectors. A key component in flat‐plate solar collectors is the absorber plate. Compared to traditional metal‐based solar collectors, polyphenylene sulfide (PPS) offers several advantages including lower weight, easier installation, and strong thermal and corrosion resistance. However, extrusion of defect‐free PPS plates is challenging. Accurate control of die exit temperatures and process conditions is crucial for producing high‐quality PPS absorber plates, as even small deviations can cause slag deposition at the die exit. Detached slag deposits can lead to serious surface defects and costly production interruptions. A digital representation of the die allows estimation of how process‐related factors affect PPS at the die exit. In this study, a detailed 3D model predicts temperatures that closely align with thermocouple data from a specific production run, revealing that measured die temperatures are about 10°C lower than the PPS die exit temperature. Melt inlet temperature and heater settings are shown to have significant impact on the die exit temperatures, while the effect of the ambient temperature is small. A non‐intrusive POD‐based reduced‐order model enables real‐time prediction of the die exit temperature, combining CFD data, interpolation, and sensor integration to support quality control and waste reduction in the extrusion process.