DBWM: A diluted bitumen weathering model.

To help better assist the management of Diluted bitumen (DilBit) spills in marine environment, a model named as DilBit Weathering Model (DBWM) was developed in this study to simulate DilBits weathering in marine environment. The DBWM was developed based on specific algorithms for evaporation, dispersion, biodegradation, as well as density and viscosity changes for DilBit weathering and other widely used algorithms for conventional oil weathering in marine environment. To validate the model, a series of DilBit weathering simulation were conducted and compared with the experimental data. Furthermore, the performance of DBWM was compared with a widely used oil weathering model (Automated Data Inquiry for Oil Spills, ADIOS2). The results demonstrated the feasibility and advantages of the developed DBWM in simulating the weathering of marine DilBit spills. Thus, the proposed DBWM can provide effective decision support to marine DilBit spill management.

Reynolds number scaling to predict droplet size distribution in dispersed and undispersed subsurface oil releases.

This study was aimed at testing the applicability of modified Weber number scaling with Alaska North Slope (ANS) crude oil, and developing a Reynolds number scaling approach for oil droplet size prediction for high viscosity oils. Dispersant to oil ratio and empirical coefficients were also quantified. Finally, a two-step Rosin-Rammler scheme was introduced for the determination of droplet size distribution. This new approach appeared more advantageous in avoiding the inconsistency in interfacial tension measurements, and consequently delivered concise droplet size prediction. Calculated and observed data correlated well based on Reynolds number scaling. The relation indicated that chemical dispersant played an important role in reducing the droplet size of ANS under different seasonal conditions. The proposed Reynolds number scaling and two-step Rosin-Rammler approaches provide a concise, reliable way to predict droplet size distribution, supporting decision making in chemical dispersant application during an offshore oil spill.

Application of entropy analysis of in situ droplet-size spectra in evaluation of oil chemical dispersion efficacy.

In situ droplet-size distributions were measured using a laser in situ scattering and transmissiometry (LISST-100X) particle size analyzer during the evaluation of natural and chemical dispersion efficiency of crude oils under different wave and current conditions. An entropy grouping of the in situ dispersed oil droplet-size spectra has classified the multi-modal droplet-size distributions into different groups based on similar droplet-size spectra characteristics within groups and distinction between groups. A generalized linear logistic regression model was fitted to analyze the effects of a number of factors and their interactions on the grouping of oil droplet-size spectra. The grouped results corresponded to the oil dispersion efficiency at different levels. This new method for droplet-size distribution data analysis can have significant implication in field evaluation of natural and chemical dispersion efficiency of oil.