What Machine Learning Could Mean for the Future of Cementing Engineering
Machine learning is becoming increasingly common across oil and gas, but the more important question is not whether the industry can apply machine learning. It is where it can actually add engineering value.
Cementing is one area where that question is particularly relevant.
Displacement efficiency depends on a combination of fluid properties, pumping conditions, pipe geometry, inclination, and movement. These variables do not operate independently, and small changes in one condition can affect how fluids behave throughout the displacement process.
That complexity creates an opportunity for data-driven approaches to complement established engineering methods.
Recent research by Hu Dai, Ph.D. and Zhibin Sun, published in the Journal of Energy Engineering, examines that opportunity by applying machine learning to the prediction of non-Newtonian fluid displacement efficiency in vertical and inclined pipes. The paper was published online September 10, 2026, and is included in the journal’s December 2026 issue.