‘Realistic’ new model points the way to more efficient and profitable fracking

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A new computational model could potentially boost efficiencies and profits in natural gas production by better predicting previously hidden fracture mechanics.

A new computational model could potentially boost efficiencies and profits in natural gas production by better predicting previously hidden fracture mechanics. It also accurately accounts for the known amounts of gas released during the process.

“Our model is far more realistic than current models and software used in the industry,” said Zdeněk Bažant, McCormick Institute Professor and Walter P. Murphy Professor of Civil and Environmental Engineering, Mechanical Engineering, and Materials Science and Engineering at Northwestern’s McCormick School of Engineering. “This model could help the industry increase efficiency, decrease cost, and become more profitable.”

Despite the industry’s growth, much of the fracking process remains mysterious. Because fracking happens deep underground, researchers cannot observe the fracture mechanism of how the gas is released from the shale.

“This work offers improved predictive capability that enables better control of production while reducing the environmental footprint by using less fracturing fluid,” said Hari Viswanathan, computational geoscientist at Los Alamos National Laboratory. “It should make it possible to optimize various parameters such as pumping rates and cycles, changes of fracturing fluid properties such as viscosity, etc. This could lead to a greater percentage of gas extraction from the deep shale strata, which currently stands at about 5 percent and rarely exceeds 15 percent.”

Read more at Los Alamos National Laboratory

Image: Branching into densely spaced hydraulic cracks is essential for effective gas or oil extraction from shale. It is suspected to occur, but the existing mathematical models and commercial software fail to predict it. A new paper from Northwestern University and Los Alamos National Laboratory presents a method to predict when the branching occurs, and how to control it.  CREDIT: Northwestern University