Sand production during hydrocarbon exploitation: Mechanisms, factors, prediction, and perspectives

Abstract

Sand production poses significant challenges for hydrocarbon extraction, particularly in weakly consolidated reservoirs and unconventional formations. This bibliometric analysis highlights the growing focus on sand production, showcasing the relevant advancements in computational methods, geomechanics, and artificial intelligence (AI) applications. Significant gaps remain in understanding multiphysics coupling, mechanical failure, and erosion, and in integrating risk assessment indices with AI-based approaches. This review paper provides a comprehensive examination of sand production mechanisms. Specifically, it investigates the roles of multiphysics coupling, mechanical failure, and erosion processes. In addition, key influencing factors such as reservoir characteristics, production strategies, and completion methods are evaluated. Key risk assessment indices are summarized to provide guidance for operational decision-making. To address the limitations of the traditional experimental, theoretical, and numerical approaches, this study provides an in-depth evaluation of AI-based methods, including machine learning and expert systems. By validating these methods across production-scale and laboratory-scale datasets, this review demonstrates their superior predictive accuracy and capacity to capture the non-linear interactions governing sand production. A conceptual framework was proposed that emphasises the integration of AI with real-time monitoring to enable adaptive and efficient sand production management. This review bridges the existing knowledge gaps and provides practical insights for improving the safety and sustainability of hydrocarbon recovery.