Ultrafast microfluidic solvent extraction and machine learning-assisted impedimetric sensor for multidetermination of scaling ions in crude oils

Abstract

Flow assurance plays an important role in designing safe and efficient operation techniques along oil and gas explorations. In this way, approaches able to provide fast and field-deployable monitoring of scaling ions in crude oils may benefit the petrochemical industry in its long-standing mission to devise ideal flow conditions by aiding the adoption of optimum dosages of off-the-shelf products. We first describe a scalable platform toward rapid (∼10 min) determination of multiple scaling ions in crude oils. Our platform is based on turbulence-mediated ultrafast and efficient microfluidic solvent extraction (μSE) combined with impedimetric sensors and machine learning to determine different ions from a single impedance plot. 3D-printed μSE devices were able to afford ultra-fast (residence time of ∼1.0 s) extractions. Nanocellulose-based foams allowed us for rapidly separating the water-oil phases, with the aqueous phase being sampled for posterior detection. Scalable sensors obtained by distinct prototyping methods provided the multidetermination of ions in 50 produced water samples (Mg2+, Ca2+, Sr2+, and Cl⁻) and 49 crude oils (Ca2+ and Cl⁻). The accuracies ranged from ∼96 to 100%. Importantly, ML models trained on standard solutions delivered poor accuracy, showing the relevance of learning supervised algorithms with real samples to deliver accurate capacitive analyses.