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Analyze of scanning electron microscope images using deep learning for calcium carbonate polymorph detection

Calcium carbonate is a material involved in various types of industries, such as medical, electronics and environmental. In the oil and gas production, calcium carbonate precipitation can occur in the production well and also in upstream processing units, such as three-phase separator and heat exchanges. Calcium carbonate can occur in three main polymorphs: calcite, vaterite and aragonite. Scanning electron microscope (SEM) is used to investigate crystallization of calcium carbonate and to understand its mechanism for diﬀerent environmental condition. Additionally, the calcite polymorph is often considered in the literature as a more tenacious and hard to remove scaling. In this work, we used convolution neural networks to detect crystals in the vaterite form of SEM images for an automatic identiﬁcation that can be used to infer the ratio of vaterite to calcite in the sample.