Techniques for Analyzing Soil Aggressiveness to Assess the Impact of Corrosion Using Artificial Intelligence on the Mechanical Integrity of Pipelines in the Oil and Gas Industry

Abstract

Abstract. The interaction between soil and oil transportation pipelines is crucial for their durability, as it influences various types of corrosion, including sweet corrosion (SC), acid corrosion (AC), oxygen corrosion (OC), galvanic corrosion (GC), and microbiologically influenced corrosion (MIC). This systematic literature review (SLR) examines 282 studies, highlighting 146 that contribute to open-access data, enriching scientific knowledge and promoting transparency within the research community. The analyzed studies address soil factors that intensify corrosion, such as pH, resistivity, moisture, chloride content, and microbial activity. Additionally, artificial intelligence (AI) technologies such as neural networks, fuzzy logic (FL), and Monte Carlo simulations (MCSs) are explored to predict risks and optimize protection strategies, including coatings and cathodic protection systems. This approach integrates traditional knowledge with innovative solutions, transforming pipeline monitoring and maintenance practices. Ensuring sustainability, minimizing environmental impact, and optimizing costs are essential. This article is intended for metallurgical engineers, corrosion specialists, and oil and gas industry professionals, providing intelligent tools to enhance decision-making and comprehensively address corrosion challenges.