The Evolution of Grey Relational Analysis and its Application in Oilfield Development
Grey relational analysis serves as an effective tool for processing small-sample and poor-information systems. Its application in oilfield development has grown substantially. This study systematically reviews the methodological evolution from traditional grey relational analysis to improved forms and weighted improved forms. It clarifies differences in theoretical assumptions, distinction coefficient determination, and weight integration mechanisms across these methods. The work aims to provide methodological support for multi-source information fusion and quantitative decision-making in complex reservoir settings. On this basis, the study systematically synthesizes four major application domains in oilfield development: preferential channel identification and prediction, reservoir evaluation and classification, productivity prediction with key factor analysis, and development optimization with risk warning. Using specific case examples, the analysis examines method applicability and critical considerations across these scenarios. Grey relational analysis has formed a complete technical chain from theoretical foundations to engineering applications in oilfield development. The methodological evolution exhibits clear trends toward adaptive parameter adjustment and information-based weighting. Engineering applications span the entire development lifecycle, establishing quantitative linkages between static geological characteristics and dynamic production responses. Current methods show limitations in nonlinear relationship characterization, multi-physical field coupling, high-dimensional dynamic sequence processing, and weight interpretability. Future directions include theoretical model innovation, spatiotemporal relational expansion, and deep integration with deep learning architectures.