Related papers: Uncertainty quantification for CO2 sequestration a…
Emerging carbon capture and storage (CCS) markets face critical challenges in developing systematic methodologies to assess geological CO2 storage potential under conditions of limited data availability, evolving regulatory frameworks, and…
CO2 capture and storage is an important technology for mitigating climate change. Design of efficient strategies for safe, long-term storage requires the capability to efficiently simulate processes taking place on very different temporal…
A quantitative analysis of model uncertainties for calculations of the maximum depth of proton-initiated extensive air showers (EAS) has been performed. Staying within the standard physics picture and using the conventional approach to the…
A Monte Carlo-based Bayesian inference model is applied to the prediction of reactor operation parameters of a PWR nuclear power plant. In this non-perturbative framework, high-dimensional covariance information describing the uncertainty…
Carbon dioxide (CO$_2$) sequestration in saline aquifers has been introduced as one of the most practical, long-term, and safe solutions to tackle a growing threat originating from the emission of CO$_2$. Successfully executing and planning…
The precision of contouring target structures and organs-at-risk (OAR) in radiotherapy planning is crucial for ensuring treatment efficacy and patient safety. Recent advancements in deep learning (DL) have significantly improved OAR…
The aim of this work was to predict the probability of the spread of rock formations with hydrocarbon-collecting properties in the studied coastal area using a stack of machine learning algorithms and data augmentation and modification…
When hydrocarbon reservoirs are used as a CO2 storage facility, an accurate uncertainty analysis and risk assessment is essential. An integration of information from geological knowledge, geological modelling, well log data, and geophysical…
This study presents new elastic velocity-effective stress laws for reservoir rocks. These models are grounded in previously established correlations between elastic modulus and porosity, which incorporate critical porosity. The accuracy of…
We have developed an Uncertainty Quantification process for multistep pipelines and applied it to the ACORN particle tracking pipeline. All our experiments are made using the TrackML open dataset. Using the Monte Carlo Dropout method, we…
Numerical simulations of geological CO2 storage in deep saline aquifers have demonstrated that vertical equilibrium (VE) models offer a robust and computationally efficient framework for reservoir optimization and upscaling. These studies…
Quantification of uncertainty in production/injection forecasting is an important aspect of reservoir simulation studies. Conventional approaches include intrusive Galerkin-based methods (e.g., generalized polynomial chaos (gPC) and…
Fiber orientation is decisive for the mechanical performance of composite materials. During manufacturing, variations in material and process parameters can influence fiber orientation. We employ multilevel polynomial surrogates to model…
Ensuring the runtime safety of autonomous systems remains challenging due to deep learning components' inherent uncertainty and their sensitivity to environmental changes. In this paper, we propose an enhancement of traditional uncertainty…
Predicting the spatial configuration of gas molecules in nanopores of shale formations is crucial for fluid flow forecasting and hydrocarbon reserves estimation. The key challenge in these tight formations is that the majority of the pore…
Carbon dioxide (CO2) trapping in capillary networks of reservoir rocks is a pathway to long-term geological storage. At pore scale, the CO2 trapping potential depends on injection pressure, temperature, and the rock's interaction with the…
Turbulent dynamical systems characterized by both a high-dimensional phase space and a large number of instabilities are ubiquitous among many complex systems in science and engineering. The existence of a strange attractor in the turbulent…
This paper represents the first effort to quantify uncertainty in carbon intensity forecasting for datacenter decarbonization. We identify and analyze two types of uncertainty -- temporal and spatial -- and discuss their system…
Understanding interfacial phenomena in confined systems is important for optimizing CO2 capture technologies. Here, we present a comprehensive investigation of CO2 adsorption in hydrated amorphous silica nanopores through an integrated…
To improve the theoretical prediction power for synthesizing superheavy elements beyond Og, a Bayesian uncertainty quantification method is employed to evaluate the uncertainty of the calculated evaporation residue cross sections (ERCS) for…