Microfluidics

Today, microfluidics remains a cornerstone of our research strategy. As energy systems evolve and new subsurface applications emerge, these experimental platforms continue to provide critical insights into the complex processes that determine the success of carbon management, sustainable energy production, and environmental stewardship. SUETRI-A has been a pioneer in the development and application of microfluidic technologies for subsurface energy and environmental research. Over the past two decades, our group has helped establish microfluidics as a powerful experimental framework for investigating the pore-scale mechanisms that govern fluid flow, transport, reaction, and storage in geological materials.

The central premise of our work is that understanding and predicting subsurface behavior requires direct observation of the processes occurring within the pore space. To achieve this goal, we develop advanced microfluidic platforms that replicate the complex architecture of natural porous media while providing unparalleled access to dynamic physical and chemical phenomena. These systems allow researchers to visualize and quantify multiphase flow, capillary trapping, wettability effects, mineral dissolution and precipitation, reactive transport, and interfacial processes under conditions relevant to real subsurface environments.

A distinguishing feature of our research has been the development of realistic rock-based micromodels derived from high-resolution images of geological materials. These devices bridge the gap between idealized laboratory experiments and the complexity of natural rock formations, enabling rigorous investigation of the mechanisms that control carbon storage, hydrogen storage, geothermal energy production, enhanced oil recovery, and groundwater remediation. The resulting observations provide fundamental insights that improve predictive models across a wide range of subsurface applications.

Our microfluidics program integrates experimentation with advanced imaging, digital rock analysis, machine learning, and numerical simulation. By combining these capabilities, we create quantitative workflows that connect pore-scale observations to continuum-scale behavior, providing a pathway from fundamental discovery to field-relevant prediction. The resulting datasets and models help guide the design and optimization of emerging energy technologies while improving confidence in long-term subsurface performance.