Tunde Oguntona is a Data Analyst at Chevron Nigeria. He holds degrees in Statistics and Energy Economics from the University of Ibadan. With experience in forecasting and predictive analytics, and a strong commitment to sustainability, Oguntona explains how AI can support real-time leak detection, predictive maintenance, and satellite-based environmental monitoring, among others applications, to help reduce environmental damage in the Niger Delta.

To begin, could you tell our readers a bit about your background and current role?

Thank you very much for having me. I studied at the University of Ibadan, where I earned a Bachelor’s degree in Statistics followed by a Master’s in Energy Economics. These two fields have given me both a solid analytical foundation and a broad understanding of the energy sector. I currently work as a Data Analyst at Chevron Nigeria. My role involves applying data science techniques such as forecasting, predictive analytics, and modelling to support operational efficiency, strategic planning, and informed decision-making within the company.

You have recently been vocal about the environmental damage in the Niger Delta. What specifically concerns you about the situation there?

The Niger Delta is rich in oil and natural gas, but it is also one of the most ecologically sensitive regions in Nigeria.

Sadly, decades of oil exploration and production have led to frequent oil spills, gas flaring, and significant environmental degradation. This has not only polluted water bodies and farmlands, but has also affected air quality and the health and livelihoods of communities in the region. As someone working within the industry, I feel a deep responsibility to help change the narrative.

You once mentioned that Artificial Intelligence (AI) could help address these issues. Could you elaborate on how AI might contribute to reducing pollution caused by oil and gas operations?

AI offers huge potential to transform how we manage environmental risks in oil and gas operations. One of the most promising uses is in real-time leak detection.
Machine learning algorithms can analyse sensor data to quickly identify pipeline leaks, allowing for faster response and reduced environmental damage.

Predictive maintenance is another key application. AI can help forecast equipment failures before they happen, enabling companies to carry out timely repairs and avoid major incidents. It also plays a role in optimising operations to cut down flaring and reduce emissions. On a broader scale, AI-powered satellite imagery can be used for environmental monitoring detecting oil slicks, gas flares, or illegal deforestation with high precision.

AI enables a shift from reactive crisis management to a proactive, data-driven approach to environmental protection.

Are there any current initiatives or examples of this happening in Nigeria?

Yes, although many are still in the pilot phase. Several international oil companies operating in Nigeria, including Chevron, are beginning to explore AI integration in their processes. Some initiatives have used drones equipped with AI-driven image recognition systems to monitor pipelines and detect vulnerabilities. Others are deploying AI to refine their energy efficiency models, which helps improve emission control.

However, to scale these technologies meaningfully, stronger collaboration is needed between government, academia, and the private sector. Regulatory agencies also have a crucial role to play by creating an enabling environment through supportive policies and investment in the right digital infrastructure.

From a policy or economic perspective, what do you think is needed to accelerate this AI-led environmental shift?

First, we need substantial investment in digital infrastructure, especially in areas such as data collection, cloud computing, and remote sensing. Without reliable data, AI systems can’t function effectively.
Second, capacity building is essential.

Nigeria needs more skilled professionals in AI, data science, and environmental analysis. Universities and research institutes must develop tailored programmes that bridge the gap between technology and sustainability.

Lastly, policy frameworks should actively encourage environmental innovation. This could be through tax incentives for companies investing in green technologies or direct government grants for technology-driven environmental projects. A clear regulatory roadmap combined with smart investment can help drive real change.

Looking to the future, what is your vision for the role of AI in Nigeria’s energy sector, particularly in relation to sustainability?

I see a future where AI is central to achieving a cleaner, more efficient, and responsible energy sector in Nigeria. It is not just about adopting new technologies, it is about building systems that allow us to extract value from our natural resources without compromising the environment.

The Niger Delta, in particular, deserves focused attention. By embedding AI into our environmental strategies, we can create a model that promotes both economic growth and ecological responsibility. It’s ambitious, but entirely achievable with the right policies, collaboration, and long-term commitment.

Finally, what would you say is the most important factor in ensuring that technology like AI truly delivers on its promise for environmental sustainability in Nigeria?

I would just like to emphasise that while technology alone is not a silver bullet, it becomes a powerful force when combined with political will, public engagement, and cross-sector collaboration. I’m hopeful about the road ahead and fully committed to being part of the solution.