Making resource development, profitable, transparent and accountable during the energy transition, by transforming raw emissions data and earth signals into actionable intelligence that drives energy availability
Applying machine learning to seismic processing, interpretation automation, petrophysical analysis, and subsurface uncertainty reduction.
Satellite remote sensing, flaring quantification, CCUS monitoring, and geospatial ML for environmental compliance and emissions accountability.
GHG Protocol compliance pipelines, carbon accounting, TCFD reporting, and audit-ready emissions intelligence for energy entities.
Growing up in Nigeria, irregular power supply has always been the norm and has hindered productivity by a great deal.
I am a geophysics graduate and I get into flow when I am playing with signals, working with them to make sense of what they reveal and yield results. Geophysics gives me this opportunity as I interprete what lies kilometres beneath the surface.
The larger picture came into focus as I got more conversant with the energy sector. Nigeria sits on untapped gas reservoirs large enough to transform its energy landscape, yet gas continues to be flared because it is not considered economically viable to capture. The same resource that could solve the electricity problem is being burned into the atmosphere.
That realisation has given me a sense of purpose to work towards countries optimally utilising their resources for development whilst protecting the environment. My core focus is petroleum geophysics: understanding what lies beneath the surface and how to extract it responsibly. Layered on top of that is a growing capability in environmental sustainability, using Python, machine learning, satellite remote sensing, and ESG frameworks to turn emissions data and earth signals into intelligence that guides entities to profitably produce whilst protecting the environment and people.
An integrated toolkit spanning subsurface analysis, data engineering, and climate frameworks.
Full petrophysical analysis using Python scripts to derive porosity and fluid saturation across multiple wells for economic field valuation.
Spatial autocorrelation modeling to quantify vegetation risk for utility providers, reducing maintenance overhead and ensuring field safety.
If you are working on optimisation and decarbonisation of Nigeria's energy sector I want to hear from you.
Available for strategic partnerships and energy audit projects.