Predictive Numbers: Labor, Data, and Power in the U.S. Oil Industry

Histories of data and computing have rarely treated extractive industries as foundational, yet this article argues that the U.S. oil industry was a critical site in the rise of predictive, numerical ways of knowing. Tracing the transformation of subsurface knowledge from embodied, sensory labor to increasingly abstracted, numerical, and computational forms between the late nineteenth and mid-twentieth centuries, the article centers oilfield services and geophysical work. Drawing on labor history, the history of science and technology, and the history of data visualization, it shows how seismic techniques reorganized workplace hierarchies, displaced experiential expertise, and expanded white-collar analytical labor. In doing so, the article reframes oil as an institutional foundation for numerical authority and early computational practice.