Artificial Intelligence (AI) and Machine Learning (ML) are becoming increasingly vital in flow assurance, especially in the oil and gas industry. AIML-based predictive modelling plays a crucial role in forecasting flow assurance problems and simulating various operational scenarios to identify risks before they occur based on historical and real-time data. The primary aim of this book is to consolidate the diverse research efforts, methodologies, and case studies that have explored the use of AIML in solving complex flow assurance issues. The book is intended to serve a wide audience: researchers and academics will find here a comprehensive reference that situates AIML-driven flow assurance within the broader field of petroleum and chemical engineering. Graduate and postgraduate students and flow assurance engineers will benefit from the clear explanations of AIML concepts, methodologies, and applications, which can inspire further study and innovation. By addressing both theoretical foundations and applied engineering, this volume aspires to act as a bridge between the academic community and the energy industry.
This volume provides science and environmental educators with examples of learning that contribute to a more eco-socially just and sustainable world. This education moves beyond facts to develop learners’ values and capabilities to envision and work towards preferable futures.
