Cornell University Technology Overview
The system comprises temperature sensors, a network, and a processing platform that computes a cross-correlation of temperature data over several windows of time and finds the correlation as a function of time lag. This correlation is a unique function dependent on the distance between sensors and hydraulic diffusivity. This functional form is compared with predictions for the sensor spacing allowing the hydraulic diffusivity between the two sensors to be determined (). By performing computations for each sensor pair, the system produces estimates of the hydraulic diffusivity as a function of depth and can evaluate the dependence on spatial scale.
Patrick Fulton and Emily Brodsky, “A System for Determining Reservoir Properties from Long-term Temperature Monitoring”, In preparation.
Uses ambient noise temperature data to infer hydraulic diffusivity
Passive rather than invasive method to determine subsurface flow properties
Cheaper and less risky than invasive methods
System allows variability in temperature sensor, network, processor and memory storage selection for the user