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Data Science for Prediction and Decision Support

Data science and machine learning have received significant attention in recent years, primarily due to unprecedented new trends in data acquisition and advances in computing technologies, together with the reported success stories about their deployment and applications from industry, academia, and research communities. In traditional science and engineering disciplines, physical laws and principles are the main pillars for deriving predictive model, whereas data is often used to tune model parameters (e.g., material properties of the system). While in physical systems data come from the true physics, data science tools focus on identifying and exploiting statistical patterns and relations in the available data, whereas physics-based methods derive their predictions by establishing causal relations among the variables in any given physical system. Moreover, data science approaches find their origin in applications where (physical) models either do not exist or are not trivial to build. Nonetheless, the success and promise of data science in recent years has inspired many scientists to investigate the application of data science tools to analysis of physical systems.

At the SEES lab, we integrate cutting-edge machine learning and predictive analytics tools with fluid flow, geological, and engineering insights to develop efficient prediction models for a number of subsurface energy and environmental applications. Examples of our current projects include deep neural networks for dimension reduction and data conditioning, novel neural network architectures for characterizing subsurface flow connectivity, direction integration of dynamic flow data under uncertain geologic scenarios using low-dimensional manifolds (latent spaces), development of efficient fit-for-purpose proxy models for prediction and uncertainty quantification in subsurface flow systems, deep neural networks for geothermal reservoir operations, and deep dynamic neural networks for anomaly detection and model-predictive control in subsurface operations. Our current projects are sponsored by the Department of Energy, Industry, and Research Foundations. Please visit our publication page for some of our recent papers in these areas.