Olanayo Philip

Petroleum Engineering student with a strong passion for Data Science and Machine Learning. I specialize in bridging the gap between traditional petroleum engineering principles and modern data-driven techniques to solve real-world reservoir and production challenges. My work focuses on applying machine learning algorithms as surrogate models for conventional PVT correlations, production forecasting, and reservoir characterization. Beyond research, I am committed to building practical skills in Python, data analysis, and predictive modeling. I believe the future of petroleum engineering lies at the intersection of domain expertise and data science — and I am positioning myself to be part of that future. Areas of Interest: • Machine Learning for Reservoir Characterization • PVT Property Prediction & Surrogate Modeling • Production Forecasting & Decline Curve Analysis • Data-Driven Drilling Optimization • Python for Petroleum Engineering Applications I am always open to collaborations, research opportunities, and conversations with professionals working at the intersection of energy and data science.