Alex Pines, Scott Seltzer, Paul Ganssle and I have won a 2013 R&D 100 award for “Optically-Detected Oil Well Logging by MRI (OWL-MRI).” You can read about it on the R&D 100 site, in Julie Chao’s news release at LBNL, or on UC Berkeley‘s news site. We also won an R&D 100 award for “Magnetic Resonance Microarray Imaging (MRMI)” in 2011.
Optically-Detected Oil Well Logging by MRI: peering deep into the earth
Optically-Detected Oil Well Logging by MRI (OWL-MRI) is a magnet-free MRI tool that measures the spatial distribution and quality of oil and gas in large geological formations and the physical properties of surrounding rock that most influence the economic and environmental expense of its extraction. The tool was developed by Berkeley Lab scientists Alex Pines and Vikram Bajaj, a Project Scientist in Pines’ laboratory, in collaboration with Scott Seltzer and Paul Ganssle; this is Pines’ fifth R&D 100 award and the second for Pines and Bajaj in the last three years.
By using Earth’s magnetic field, rather than an array of permanent magnets, OWL-MRI probes many meters—rather than centimeters—deep into an oil formation, and provides information about the porosity of the rock and the chemistry of fluids it contains with a specificity previously accessible only in laboratory experiments. OWL-MRI can increase the accuracy and speed of oil well logging measurements (an $11.2 billion market) by a factor of 10 to 100, reducing the cost of oil exploration. When used to calibrate oil extraction, OWL-MRI will also reduce its environmental impact, particularly where hydraulic fracturing is necessary.