Oil Displacement in Calcite-Coated Microfluidic Chips via Waterflooding at Elevated Temperatures and Long Times.

In microfluidic studies of improved oil recovery, mostly pore networks with uniform depth and surface chemistry are used. To better mimic the multiple porosity length scales and surface heterogeneity of carbonate reservoirs, we coated a 2.5D glass microchannel with calcite particles. After aging with formation water and crude oil (CRO), high-salinity Water (HSW) was flooded at varying temperatures and durations. Time-resolved microscopy revealed the CRO displacements. Precise quantification of residual oil presented some challenges due to calcite-induced optical heterogeneity and brine-oil coexistence at (sub)micron length scales. Both issues were addressed using pixel-wise intensity calibration. During waterflooding, most of the ultimately produced oil gets liberated within the first pore volume (similar to glass micromodels). Increasing temperature from 22 °C to 60 °C and 90 °C produced some more oil. Waterflooding initiated directly at 90 °C produced significantly more oil than at 22 °C. Continuing HSW exposure at 90 °C for 8 days does not release additional oil; although, a spectacular growth of aqueous droplets is observed. The effect of calcite particles on CRO retention is weak on flat surfaces, where the coverage is ~20%. The calcite-rich pore edges retain significantly more oil suggesting that, in our micromodel wall roughness is a stronger determinant for oil retention than surface chemistry.

Motion and Noise Artifact-Resilient Atrial Fibrillation Detection using a Smartphone.

We have recently found that our previously-developed atrial fibrillation (AF) detection algorithm for smartphones can give false positives when subjects' fingers or hands move, as we rely on proper finger placement over the smartphone camera to collect the signal of interest. Specifically, smartphone camera pulsatile signals that are obtained from normal sinus rhythm (NSR) subjects but are corrupted by motion and noise artifacts (MNAs) are frequently detected as AF. AF and motion-corrupted episodes have the similar characteristic that pulse-to-pulse intervals (PPIs) are irregular. We have developed an MNA-resilient smartphone-based AF detection algorithm that first discriminates and eliminates MNA-corrupted episodes in smartphone camera recordings, and then detects AF in MNA-free recordings. We found that MNA-corrupted episodes have highly-varying pulse slope, large turning point ratio, or large kurtosis values in smartphone signals compared to MNA-free AF and NSR episodes. We first use these three metrics for MNA discrimination and exclusion. Then, AF is detected in MNA-free signals using our previous algorithm. The capability to discriminate MNAs and AFs separately in smartphone signals increases the specificity of AF detection. To evaluate the performance of the proposed MNA-resilient AF algorithm, 99 subjects, including 88 study participants with AF at baseline and in NSR after electrical cardioversion as well as 11 participants with MNA-corrupted NSR, were recruited. Using iPhone 4S, 5S, and 6S models, we collected 2-minute pulsatile time series from each subject. The clinical results show that the accuracy, sensitivity and specificity of the proposed AF algorithm are 0.97, 0.98, 0.97, respectively, which are higher than those of the previous AF algorithm.