Petroleum Engineer and Earth Scientist with experience integrating innovative solutions to optimize engineering projects in the energy sector. My academic journey, including a master's degree focused on machine learning applications for geoscience challenges, has equipped me with a deep understanding of subsurface complexities and the skills to address them.

Recently, my professional journey has evolved to bridge the gap between engineering and machine learning technology. During my Master's program, I've been applying physics-informed machine learning models to solve complex flow problems in the subsurface.

I'm continuously exploring opportunities to utilize AI and machine learning, aiming to drive innovation and foster significant advancements in the energy sector. If you're interested in discussing the future of the oil and gas industry, digital transformation, or machine learning, I'd be delighted to connect and explore opportunities to collaborate.

• Model oil and gas reservoir production using physics-powered machine learning through in-house web applications.

• Conduct ETL processes to integrate data from diverse sources provided by oil operator companies, preparing it for use within internal applications.

• Develop proxy models from Intersect simulation models to analyze uncertainty, optimize production strategies, and enhance reservoir management decisions. Work with SLB's Intersect reservoir simulation software, building scenario cases to refine and evaluate forecasts and history matching.

• Analyze and visualize modeling and optimization results by developing interactive web dashboards connected to Tachyus and customer databases, primarily utilizing the Plotly library.

• Develop saltwater disposal models to understand pressure distribution and analyze possible geological hazards for drilling plans and optimize operations.

• Collaborate closely with customers, research, and engineering teams to tailor solutions that maximize Net Present Value (NPV) and improve recovery factors.