Importance of Machine Learning Methods and Analysis in Engineering

Scope

This symposium will explore the integration of machine learning (ML) techniques in various domains of engineering, especially Petroleum Engineering. Participants are invited to contribute research, case studies, or practical applications addressing the use of ML in drilling optimization, reservoir characterization, production forecasting, and enhanced oil recovery. The scope also includes data preprocessing, feature selection, model validation, and the use of real-time analytics for decision-making. Contributions may highlight supervised and unsupervised learning methods, deep learning architectures, and hybrid modeling approaches. We encourage submissions that demonstrate innovation in applying ML to field data, interpretability of models, and integration with existing engineering workflows. This symposium aims to foster interdisciplinary collaboration and bridge the gap between data science and engineering practice.