| Abstract Scope |
Materials informatics-enabled materials discovery and selection is enabled on the existing big data of properties of diverse Mg alloys propelled towards degradable fracturing tools. The performance requirements of tools in energy-intensive and extreme conditions of fracturing processes, particularly in unconventional low-porosity and low-permeability oil and gas fields, rely on the synergy between mechanical and degradative properties. A novel methodology that combines multiple-attribute decision-making (MADM) methods, principal component analysis (PCA), and hierarchical cluster analysis (HCA) was applied. Objective and subjective methods evaluated the weights of the properties. The rank assigned by several MADMs, viz., Technique of order preference by similarity to ideal solution (TOPSIS), Combined compromise solution (CoCoSo), Operational competitive ratio (OCRA), and so forth, were consistent. PCA and HCA consolidated the MADM ranks and grouped similar alloys. While the performance requirements of degradable fracturing tools are still emerging, this investigation highlights directions for improvement and/or development geared toward the application. |