Structure of Asphaltenes during Precipitation Investigated by Ultra-Small-Angle X-ray Scattering.

Time-resolved size and structure measurements of asphaltenes while in the process of precipitating were monitored for the first time using ultra-small-angle X-ray scattering. The results revealed that asphaltenes precipitating from a heptane-toluene mixture demonstrate a hierarchical structure of an asphaltene-rich phase (e.g., droplet) that further agglomerates into fractal flocs. The fractal flocs that form by the agglomeration of the asphaltene-rich phase are what is commonly detected by optical microscopy above the precipitation detection point. The surface of the asphaltene-rich phase is initially rough and transitions to a smooth interface, as would be expected for a highly viscous liquid. Simultaneous small-angle X-ray scattering measurements were also performed to investigate the structure of soluble asphaltenes, providing comprehensive structural characterization from the nanometer- to micrometer-length scales as a function of time. Further, the results demonstrate that the size and concentration of asphaltenes remaining in solution (e.g., soluble asphaltenes) do not change during precipitation, whereas the structure of insoluble asphaltenes varies. The ability to measure the properties of asphaltenes as they undergo precipitation opens new opportunities for understanding the fundamental mechanisms of asphaltene deposition and aggregation and the impact of chemical inhibitors to alter these processes. The universality of these conclusions and how specific properties vary as a function of asphaltene source and solution properties can provide valuable insight into asphaltene behavior.

Auto-segmentation technique for SEM images using machine learning: Asphaltene deposition case study.

Enormous efforts have been attempted to replace the subjective human analysis of digital images by automated computational methods such as image processing and computer vision. The image processing methods (i.e., pixel- and object-based assessments) determine a variety of spectral/optical features and devices/data properties on the images so that appropriate information could be extracted. However, these methods may not be appropriate when globally heterogeneous and locally anisotropic features exist such as those found in Scanning Electron Microscopy (SEM) Images. Thus, it is essential to have an adaptive and data-driven procedure to extract optimal information from individual SEM images. In this study, we developed a fully automated image processing and analysis method using data analytics, pattern recognition, and machine learning (including deep learning) techniques to automate the image processing and investigate physical properties and nanoscale deposition of petroleum constituents such as asphaltenes on surfaces from over hundreds of images. To do so, various data preparation processes (i.e., image filtering and quality assessment methods) were first introduced to mine and enhance the data. Then, the extracted information was used to identify and quantify targeted physical properties and deposition attributes by denosing and image segmentation techniques. To validate the proposed method, we applied the model to the experimental results from asphaltene deposition studies. The model results were then compared with the corresponding experimental counterparts from the literature. The insight from this application led to a better understanding of the asphaltene deposition mechanism. To the best of our knowledge, this work is one of the first attempts to develop a fully automated image-processing model that describes physical properties of deposited species. In addition, our combination of data and descriptive models allowed us to differentiate foreground and background information particles from SEM images meaning that the model could be used for other applications in image processing.