In water-stressed regions such as California and Texas, groundwater is a critical resource to oil and gas operations that rely on stable subsurface conditions. Accelerating withdrawal rates are pushing major aquifers past their natural recharge capacity, increasing the likelihood of land subsidence, which is a hazard that threatens wells, pipelines, and surface infrastructure. As aquifers decline, understanding their behavior has become essential for managing risk across energy-producing regions.
Petroleum Engineering Professor Pejman Tahmasebi, SPE, created a global machine-learning model that maps and quantifies subsidence risk. His model compiles worldwide datasets to pinpoint where land is sinking and links subsidence to high volumes of groundwater extraction.
Separately, Professor of Anthropology Lucas Bessire studied the Ogallala Aquifer, a key water source for the Great Plains and nearby oil and gas activity.
“I’ve learned a lot about how groundwater operates in western Kansas. There’s a very small recharge rate, between 1/10 of an inch and one inch per year, because of the nature of how that groundwater is stored and where it’s located, deep below the surface, without a lot of interaction with the top,” Bessire said.
His findings emphasize the need for regional water-use reform to safeguard communities and industries that rely on the aquifer, including oil and gas operations in the High Plains.