MS Student
MS Student
Active
Building 5, Level 0, 0874-WS32
I am a Petroleum Engineering graduate with a strong passion for energy innovation. With a solid foundation in Machine Learning and AI, I aim to integrate these technologies into the petroleum industry to enhance efficiency, sustainability, and drive new advancements.
I believe in a balanced energy future—supporting the growth of renewable energy while recognizing the vital role petroleum still plays in the global energy landscape. My goal is to bridge the gap between traditional energy systems and emerging technologies for a more sustainable future.
I’m also passionate about collaboration and global perspectives, enjoying the opportunity to connect with diverse people worldwide and contribute to impactful conversations in the energy sector.
My research interests focus on the application of machine learning, deep learning, and data analytics in petroleum engineering, with an emphasis on production optimization and reservoir simulation. I have an experience in developing a predictive maintenance model for Electrical Submersible Pump (ESP) based on downhole sensor system and I am interested in learning more about production engineering and its optimization. In addition, I aim to learn more about the application of deep learning in reservoir simulation.
Trap Prevention in Machine Learning in Prediction of Petrophysical Parameters: A Case Study in The Field X
A. Zainuri, P.D. Sinurat, D. Irawan, H. Sasongko
Scientific Contributions Oil and Gas, 46(3), 21–33, (2023)