A beach setting with palm trees being blown in the wind and the sky is overcast with an imminent storm.

With the help of a grant from the Office of Naval Research, Dr. Wencheng Jin of Texas A&M is developing AXBeach, a computer model that can predict real-time changes to shorelines during storms.

Illustration of underground rock layers in different colors with two trees on grassy ground above.

Texas A&M research teams have received seed grant funding to advance critical research into the integrity and interactions of subsurface storage.

Headshot of Dr. Rita Esuru Okoroafor.

Dr. Rita Esuru Okoroafor is developing new best practices and frameworks that support alternative energy storage technologies through her research in porous media.

3D molecules and bubbles with a blue background.

New research explores nanobubble stability and its implications across a variety of real-world applications.

A woman looks at beaker of oil in laboratory.

Dr. Berna Hascakir is part of a $17 million grant from the Department of Energy, which will support university-driven projects dedicated to exploring alternative energy solutions while providing educational and research opportunities to students from minority-serving institutions.

digital 3D rendering of a mountain-filled landscape geomodel

Texas A&M researchers are developing generative AI technology to create realistic 3D models of the Earth’s subsurface.

Female in a pink suit.

Dr. Rita Esuru Okoroafor in the petroleum engineering department earned funding from the U.S. Department of Energy Advanced Research Projects Agency-Energy to study geologic hydrogen.

Smiling female student sitting outdoors wearing glasses, a hijab and a long-sleeve shirt.

A graduate student in the petroleum engineering department garners attention with research showing carbon dioxide storage in geothermal systems could be a highly effective future power source.

Graphic image of many decorative lines and arrows flowing down to a single arrow.

Petroleum engineering researchers achieve fast, low-error subsurface reservoir production forecasts by combining machine learning with geomodel compression and a neural network.

Dirty fluid background with a magnifying circle inset showing clean close-ups of several mineral compounds.

Leading a two-fold research project, Dr. Hamidreza Samouei is working to extract critical minerals unclaimed in produced water, often considered an oil and gas operations waste product, while also purifying the water for agricultural use.