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The interaction of multiple fluids through a heterogeneous pore space leads to complex pore-scale flow dynamics, such as intermittent pathway flow. The non-local nature of these dynamics, and the size of the 4D datasets acquired to capture them, presents challenges in identifying key fluctuations controlling fluid connectivity. To address these challenges, this work employs Dynamic Mode Decomposition (DMD), a data-driven algorithm that decomposes complex nonlinear systems into dominant spatio-temporal structures without relying on prior system assumptions. We present a workflow that identifies critical spatio-temporal regions exhibiting intermittent flow dynamics. This workflow is validated through three test cases, each exploring the impact of viscosity ratio on flow dynamics while maintaining a constant capillary number. Our findings demonstrate DMD's potential in analyzing extensive experimental datasets and identifying crucial intermittent flow structures, offering a powerful tool for understanding complex fluid behaviors in heterogeneous pore spaces. Using our method, we can quickly identify the timescales and locations of interest in an objective manner, providing a valuable diagnostic tool for analysing large synchrotron datasets.

Sodium-ion batteries have garnered significant attention as a potentially low-cost alternative to lithium-ion batteries, which have experienced supply shortages and pricing volatility of key minerals. Here we assess their techno-economic competitiveness against incumbent lithium-ion batteries using a modeling framework incorporating componential learning curves constrained by minerals prices and engineering design floors. We compare projected sodium-ion and lithium-ion price trends across over 6,000 scenarios while varying Na-ion technology development roadmaps, supply chain scenarios, market penetration, and learning rates. Assuming substantial progress can be made along technology roadmaps via targeted R&D, we identify many sodium-ion pathways that might reach cost-competitiveness with low-cost lithium-ion variants in the early 2030s. Additionally, we show timelines are highly sensitive to movements in critical minerals supply chains -- namely that of lithium, graphite, and nickel. Modeled outcomes suggest increasing sodium-ion energy densities to decrease materials intensity to be among the most impactful ways to improve competitiveness., Comment: 20 pages, 6 figures, 1 table

Carbon capture and storage (CCS) plays an essential role in global decarbonization. Scaling up CCS deployment requires accurate and high-resolution modeling of the storage reservoir pressure buildup and the gaseous plume migration. However, such modeling is very challenging at scale due to the high computational costs of existing numerical methods. This challenge leads to significant uncertainties in evaluating storage opportunities, which can delay the pace of large-scale CCS deployment. We introduce Nested Fourier Neural Operator (FNO), a machine-learning framework for high-resolution dynamic 3D CO2 storage modeling at a basin scale. Nested FNO produces forecasts at different refinement levels using a hierarchy of FNOs and speeds up flow prediction nearly 700,000 times compared to existing methods. By learning the solution operator for the family of governing partial differential equations, Nested FNO creates a general-purpose numerical simulator alternative for CO2 storage with diverse reservoir conditions, geological heterogeneity, and injection schemes. Our framework enables unprecedented real-time modeling and probabilistic simulations that can support the scale-up of global CCS deployment.

Numerical simulation of multiphase flow in porous media is essential for many geoscience applications. Machine learning models trained with numerical simulation data can provide a faster alternative to traditional simulators. Here we present U-FNO, a novel neural network architecture for solving multiphase flow problems with superior accuracy, speed, and data efficiency. U-FNO is designed based on the newly proposed Fourier neural operator (FNO), which has shown excellent performance in single-phase flows. We extend the FNO-based architecture to a highly complex CO2-water multiphase problem with wide ranges of permeability and porosity heterogeneity, anisotropy, reservoir conditions, injection configurations, flow rates, and multiphase flow properties. The U-FNO architecture is more accurate in gas saturation and pressure buildup predictions than the original FNO and a state-of-the-art convolutional neural network (CNN) benchmark. Meanwhile, it has superior data utilization efficiency, requiring only a third of the training data to achieve the equivalent accuracy as CNN. U-FNO provides superior performance in highly heterogeneous geological formations and critically important applications such as gas saturation and pressure buildup "fronts" determination. The trained model can serve as a general-purpose alternative to routine numerical simulations of 2D-radial CO2 injection problems with significant speed-ups than traditional simulators.

Numerical simulation is an essential tool for many applications involving subsurface flow and transport, yet often suffers from computational challenges due to the multi-physics nature, highly non-linear governing equations, inherent parameter uncertainties, and the need for high spatial resolutions to capture multi-scale heterogeneity. We developed CCSNet, a general-purpose deep-learning modeling suite that can act as an alternative to conventional numerical simulators for carbon capture and storage (CCS) problems where CO$_2$ is injected into saline aquifers in 2d-radial systems. CCSNet consists of a sequence of deep learning models producing all the outputs that a numerical simulator typically provides, including saturation distributions, pressure buildup, dry-out, fluid densities, mass balance, solubility trapping, and sweep efficiency. The results are 10$^3$ to 10$^4$ times faster than conventional numerical simulators. As an application of CCSNet illustrating the value of its high computational efficiency, we developed rigorous estimation techniques for the sweep efficiency and solubility trapping.

To encourage and guide decarbonization efforts, better tools are needed to monitor real-time CO2 and criteria air pollutant emissions from electricity consumption, production, imports, and exports. Using real-time data from the electricity system is especially challenging for quantitative applications requiring high quality and physically consistent data. Until now, time-intensive, ad-hoc and manual data verification and cleaning strategies have been used to prepare the data for quantitative analysis. As an alternative to existing techniques, here we provide a physics-informed framework to greatly accelerate and automate data processing to enable internally consistent electric system consumption, production, import, and export data in near real-time. A key component of this framework is an optimization program to minimize the data adjustments required to satisfy energy conservation equations. The effectiveness of this method is demonstrated by applying it to the continental United States electricity network. The resulting publicly-available data set, which provides in near-real time, hourly updates on electricity generation, consumption, imports, exports and associated emissions, is the first of this nature., Comment: A short version of this paper was presented at Applied Energy Symposium: MIT A+B, August 13-14, 2020, Boston. This paper is a substantial extension of the short version of the conference paper

This paper proposes a deep neural network approach for predicting multiphase flow in heterogeneous domains with high computational efficiency. The deep neural network model is able to handle permeability heterogeneity in high dimensional systems, and can learn the interplay of viscous, gravity, and capillary forces from small data sets. Using the example of carbon dioxide (CO2) storage, we demonstrate that the model can generate highly accurate predictions of a CO2 saturation distribution given a permeability field, injection duration, injection rate, and injection location. The trained neural network model has an excellent ability to interpolate and to a limited extent, the ability to extrapolate beyond the training data ranges. To improve the prediction accuracy when the neural network model needs to extrapolate, we propose a transfer learning (fine-tuning) procedure that can quickly teach the neural network model new information without going through massive data collection and retraining. Based on this trained neural network model, a web-based tool is provided that allows users to perform CO2-water multiphase flow calculations online. With the tools provided in this paper, the deep neural network approach can provide a computationally efficient substitute for repetitive forward multiphase flow simulations, which can be adopted to the context of history matching and uncertainty quantification.