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ABSTRACT
Solid mineral scale formation in oil wells and production equipment poses a significant challenge to the oil and gas industry, leading to formation damage, infrastructure degradation, reduced productivity, and environmental impact. While traditional empirical correlations have been employed for scale prediction, their oversimplification of the complex underlying mechanisms limits their accuracy. This research addresses the shortcomings of existing methods by harnessing the power of machine learning. Machine learning models are designed and trained to capture intricate relationships among multiple factors, such as pressure, temperature, ionic concentrations, and pH levels, which influence scale formation. By doing so, they provide a more precise and reliable means of predicting when and where scale deposits will occur. The adoption of machine learning promises to enhance operational efficiency, protect infrastructure, minimize environmental impact, and establish the oil and gas industry as a leader in innovative and sustainable practices. This research represents a pivotal step toward revolutionizing scale prediction and control, ultimately benefiting the industry and broader society.
