PTE406 AI in Petroleum Engineering – Lab

[1–2, 1 cr.]

The laboratory component of this course provides hands-on experience with AI and machine learning techniques applied to chemical engineering systems. Students work directly with experimental, industrial, and simulated process datasets to develop data-driven models, perform process monitoring, implement hybrid modeling approaches, and build predictive tools used in real chemical engineering practice. Each lab focuses on applying theoretical concepts to practical problem-solving through Python-based analysis, model development, and visualization. Students gain experience in data handling, model training and evaluation, optimization, and interpretation of results to support decision-making in chemical processes.

Co-requisite: PTE405 AI in Petroleum Engineering.