SBIR Phase I: Paramagnetic Spin-Probes for Corrosion Inhibition

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project addresses a mechanism to optimize corrosion inhibition for projects covering the oil and gas industry and beyond. Corrosion costs US industries alone an estimated $170 billion a year. The oil and gas industry takes an above average share of these costs because of its complex and demanding production techniques, and the environmental threat should components fail. Today, the standard inhibition mechanism for the oil and gas industry is to inject a chemical inhibitor, typically a surfactant, that can coat the interior of the oilfield tubular and avoid corrosion. Because of the high cost of failure, it is extremely common to over-inject surfactant quantities resulting in excessive costs and the waste of unwanted chemicals sent to the refinery. Unfortunately, there has not previously been a good way to tell when too much surfactant was in fact too much. That is the specific new aspect brought by this innovation. It proposes a mechanism to detect in real-time that there is sufficient surfactant by taking advantage of quantum chemical properties of those surfactants when they are subject to very large magnetic fields and GHz radio-frequency excitation. This SBIR Phase I project proposes to extract a chemical spectrum from a specially designed reagent that can be added to a sample of produced fluid, where that chemical spectrum will change depending on whether there is sufficient surfactant to cause corrosion inhibition. More specifically it is going to measure some quantum chemical properties of the reagent called its hyperfine structure that will change in the presence of surfactant micelles. Essentially the micelles form a containment barrier around free radical electrons within that reagent and thereby change the way those electrons can resonate in the combination of the magnet and RF excitation. Phase 1 of the project will resolve multiple challenges in this approach, including the choice of reagent and the development of robust algorithms to convert hyperfine data into quantification of critical micelle concentration, where the algorithm needs to be able to tolerate typical impurities found in oilfield fluids. Phase 1 will also validate a spectroscopic approach that can result in the hyperfine data being sent to the cloud where AI-based algorithms can reduce the spectral information to actionable information for the operator. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.