Assessment and selection of Mg-based alloys for fracturing applications by decision science-driven techniques

The efficiency and reliability of energy production in unconventional oil and gas fields depend on materials that withstand extreme conditions while ensuring controlled degradation. Degradable fracturing tools play a crucial role in these energy-intensive processes, requiring a balance between mechanical strength and degradability to optimize performance and minimize environmental impact. This study applies a data-driven approach to identify and evaluate materials suitable for such applications. A novel methodology integrating multiple-attribute decision-making (MADM) methods, principal component analysis (PCA), and hierarchical cluster analysis (HCA) was employed to assess and rank candidate materials based on their mechanical and degradative properties. Property weights were determined through objective (Shannon’s entropy method) and subjective techniques for a balanced assessment. Several MADM methods, including the Technique of Order Preference by Similarity to Ideal Solution (TOPSIS), Grey Relational Analysis (GRA), and Operational Competitive Ratio (OCRA), consistently ranked materials in a similar order. PCA and HCA further consolidated these rankings by clustering materials with comparable characteristics. As performance requirements for degradable fracturing tools continue to evolve, this study highlights key material attributes and identifies potential alloys and composites that meet the demands of extreme service conditions. The findings contribute to the advancement of material solutions that enhance energy production efficiency while promoting sustainability, aligning with the symposium’s focus on materials for extreme service conditions in energy applications.