From Kepler to Newton: Explainable AI for Science Discovery

The Observation--Hypothesis--Prediction--Experimentation loop paradigm for scientific research has been practiced by researchers for years towards scientific discoveries. However, with data explosion in both mega-scale and milli-scale scientific research, it has been sometimes very difficult to manually analyze the data and propose new hypotheses to drive the cycle for scientific discovery. In this paper, we discuss the role of Explainable AI in scientific discovery process by demonstrating an Explainable AI-based paradigm for science discovery. The key is to use Explainable AI to help derive data or model interpretations, hypotheses, as well as scientific discoveries or insights. We show how computational and data-intensive methodology -- together with experimental and theoretical methodology -- can be seamlessly integrated for scientific research. To demonstrate the AI-based science discovery process, and to pay our respect to some of the greatest minds in human history, we show how Kepler's laws of planetary motion and Newton's law of universal gravitation can be rediscovered by (Explainable) AI based on Tycho Brahe's astronomical observation data, whose works were leading the scientific revolution in the 16-17th century. This work also highlights the important role of Explainable AI (as compared to Blackbox AI) in science discovery to help humans prevent or better prepare for the possible technological singularity that may happen in the future, since science is not only about the know how, but also the know why.Comment: Presented at ICML-AI4Science 202

Subsurface multiphase reactive flow in geologic CO2 storage: Key impact factors and characterization approaches

Multiple measurements and data sets show unequivocally that levels of carbon dioxide (CO2) have been increasing in the Earth's atmosphere for the past several centuries, with the rate becoming  steeper in recent decades (Soeder, 2021). Carbon capture, utilization and storage (CCUS) has been regarded as an effective approach to swiftly cut CO2 emissions. Among the existing CCUS technologies, CO2 geological utilization and storage has the highest technological maturity, and is the most vital “sink” to consume the captured CO2. For CO2 geological utilization and storage, large amounts of CO2 need to be injected into the deep subsurface, and the CO2 flow in the subsurface is a very complicated process. The flow system is a two-phase or even a three-phase system, and flow in pores needs to be clearly distinguished from flow in fractures and wellbores. Most importantly, wettability, pore structure, geochemical reactions play very important roles in governing subsurface CO2 flow. Without a clear understanding of how the impact factors affect CO2 flow, it is difficult to predict the CO2 impairs the confidence of policy makers and investors to support large-scale geologic CO2 storage. To study CO2 flow, there is a need to develop effective approaches to characterize CO2 flow in subsurface system. This work discusses several key factors that have strong impact on subsurface CO2 flow, and an effective approach for CO2 flow characterization. Impact of wettability and pore structure on multiphase flow. The injection of CO2 into geological formations displaces brine from pore spaces, resulting in various CO2-brine displacement patterns, such as capillary fingering, viscous fingering, crossover, and compact displacement. These patterns also occur as the brine later flows back to displace supercritical CO2 when the injection stops. The CO2-brine displacement results in CO2 becoming trapped as droplets and ganglia in pore spaces, referred to as residual trapping or capillary trapping. Wettability and pore structure have significant effects on CO2-brine displacement patterns and capillary trapping. The wettability represents the affinity of fluid to the solid surface. By changing the capillary force governed by the Young-Laplace law, the wettability modifies the local porefilling events and thus impacts the displacement patterns. Increasing the wettability of the invading fluid from drainage to imbibition stabilizes the displacement front due to the cooperative pore-filling events at the pore scale (Holtzman and Segre, 2015). However, the displacement pattern will change extensively as a result of corner flow when the invading fluid is strongly wetting to the solid surface (Hu et al., 2018). On the other hand, the role of pore structure in displacement patterns may depend on the type of permeable media. The pore-scale disorder, which represents the randomness of pore size, changes the threshold capillary pressure and affects the local pore-filling paths. Increasing disorder promotes unstable displacement patterns for both drainage and imbibition conditions (Toussaint et al., 2005), but under certain wettability conditions, higher disorder may enhance cooperative porefilling events and thus smooth the displacement front. The roughness variations in the aperture between the two rough surfaces determines the flow path and controls the displacement patterns for a fractured medium. Therefore, the transition of CO2-brine displacement patterns under various wetting and pore structure conditions is an open challenge and a very active area of research.Impact of geochemical reactions on multiphase flow. Geochemical reactions play a key role in determining CO2 flow patterns. Though geochemical reaction-induced mineral trapping can only become vital after hundreds to thousands years of CO2 injection in reservoir scale, fast mineral dissolution and precipitation in micro-scale flow channels of host rocks and caprocks can change permeability of the rocks and thus influence the migration behaviour of injected CO2 (Zhang et al., 2019). For carbonate rocks, CO2 injection usually causes opening of flow channels due to dissolution of carbonates, which enhances CO2 injectivity and is beneficial for largescale CO2 storage (Yang et al., 2020). A sandstone reservoir that contains large amounts of feldspars and glauconite may have a strong CO2-sandstone interaction, which usually causes sealing of flow channels due to precipitation of secondary minerals (Xu et al., 2004). However, given different types of flow channels and varying reaction environments, it is very difficult to precisely predict if a given flow channel in a rock will open or close under the influence of geochemical reactions. Therefore, an important research direction in the future is to find out a criterion that can determine if a flow  channel will open or close under the influence of geochemical reactions, with the consideration of complicated reaction environments.Pore-scale modeling of multiphase reactive flow. Compared with continuum-scale models, pore-scale modeling, which directly reflects the realistic porous structures, provides a powerful tool for studying the multiphase flow, species transport, chemical reaction and mineral dissolution/precipitation processes (Chen et al., 2022). Effects of pressure, temperature, fluid properties, wettability, pore size and porous morphology on the supercritical CO2-water two-phase flow and distributions have been extensively studied by pore-scale modeling. Pore-scale modeling that reveals the mechanisms of nonequilibrium supercritical CO2 dissolution into the surrounding brine will be beneficial for enhancing CO2 solubility trapping. Recently, supercritical CO2 storage in the depleted oil reservoir has also drawn increasing attention, and the resulting supercritical CO2-oil-water three-phase flow are extremely complicated (Zhu et al., 2021). Pore-scale modeling is an ideal tool to study the effects of structure heterogeneity, mineral composition and reaction kinetics on the rock dissolution processes. Further pore-scale modeling work to investigate the effects of  twophase or three-phase flow on mineral dissolution/precipitation processes are helpful for better understanding the CO2 storage processes in saline formations or depleted oil reservoirs. AcknowledgementThis work was performed by the support of Key R&D Program of Inner Mongolia Province of China (No. 2021ZD0034-3) and the National Natural Science Foundation of China (Nos. 42172315, 42141011 and 52122905).Cited as: Zhang, L., Chen, L., Hu, R., Cai, J. Subsurface multiphase reactive flow in geologic CO2 storage: Key impact factors and characterization approaches. Advances in Geo-Energy Research, 2022, 6(3): 179-180. https://doi.org/10.46690/ager.2022.03.0

Advances in multiscale rock physics for unconventional reservoirs

The multiscale rock physics of unconventional reservoirs have drawn increasing attention in recent years, which involves several essential issues, including measuring method, transport property, physics model, characteristic scale, and their application. These issues vastly affect science and engineering regarding the exploration and development of unconventional reservoirs. To encourage communication on the advances of research on the rock physics of unconventional reservoirs, a conference on Multiscale Rock Physics for Unconventional Reservoirs was jointly organized by the journals Energies and Advances in Geo-Energy Research. Due to the limitations of movement caused by COVID-19, 21 experts introduced their work online, and the conference featured the latest multiscale theories, experimental methods and numerical simulations on unconventional reservoirs.Cited as: Cai, J., Zhao, L., Zhang, F., Wei, W. Advances in multiscale rock physics for unconventional reservoirs. Advances in Geo-Energy Research, 2022, 6(4): 271-275. https://doi.org/10.46690/ager.2022.04.0

Advances in Modelling of Heat and Mass Transfer in Porous Materials

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High-speed photon correlation monitoring of amplified quantum noise by chaos using deep-learning balanced homodyne detection

Precision experimental determination of photon correlation requires the massive amounts of data and extensive measurement time. We present a technique to monitor second-order photon correlation $g^{(2)}(0)$ of amplified quantum noise based on wideband balanced homodyne detection and deep-learning acceleration. The quantum noise is effectively amplified by an injection of weak chaotic laser and the $g^{(2)}(0)$ of the amplified quantum noise is measured with a real-time sample rate of 1.4 GHz. We also exploit a photon correlation convolutional neural network accelerating correlation data using a few quadrature fluctuations to perform a parallel processing of the $g^{(2)}(0)$ for various chaos injection intensities and effective bandwidths. The deep-learning method accelerates the $g^{(2)}(0)$ experimental acquisition with a high accuracy, estimating 6107 sets of photon correlation data with a mean square error of 0.002 in 22 seconds and achieving a three orders of magnitude acceleration in data acquisition time. This technique contributes to a high-speed and precision coherence evaluation of entropy source in secure communication and quantum imaging.Comment: 6 pages, 6 figure