Nigerian universities have been urged to take the lead in carbon storage research, leveraging on pore-scale modeling and machine learning to develop sustainable climate solutions.
This was the submission of Raymond Aderoju, a researcher at the University of Georgia, Athens, Georgia, United States of America, in an interview with our correspondent.
Aderoju, who has conducted extensive research on pore-scale modeling and its applications in carbon storage, noted that Nigerian universities can significantly contribute to global efforts to mitigate climate change.
He said, “Despite the challenges facing Nigerian universities, advancements in geological research, computational modeling, and machine learning enable them to make significant contributions to climate solutions.”
Aderoju emphasized the importance of pore-scale modeling in understanding the behavior of fluids in porous media, which is crucial for optimizing carbon storage efficiency and minimizing leakage risks.
He said, “Nigerian tertiary institutions have traditionally served as hubs for knowledge creation, yet they remain underutilized in addressing global issues like climate change. While developed nations employ cutting-edge research to mitigate greenhouse gas emissions, Nigerian institutions continue to face challenges such as limited resources, outdated curricula, and insufficient research funding.
Nevertheless, despite these obstacles, advancements in geological research, computational modeling, and machine learning enable Nigerian universities to significantly contribute to climate solutions. In line with these efforts, my research on pore-scale modeling and the integration of machine learning for predicting multiphase flow dynamics in complex porous media aligns with these initiatives, providing academic institutions a way to enhance scientific investigation and develop practical solutions even in environments with limited resources. This will be achieved through knowledge sharing, publications, and integrating research methodologies into university curricula.
Excessive carbon dioxide (CO₂) emissions are the primary cause of climate change, a significant global concern. Immediate actions and long-term strategies to efficiently capture, store, and manage CO₂ are necessary for mitigating these emissions. Geological carbon sequestration (GCS), which involves permanently storing CO₂ in deep underground rock formations, is one of the most promising alternatives.
However, this process is complicated because the injected CO₂ navigates through complex networks of tiny pores in the rock matrix instead of remaining in one place. Understanding how CO₂ and other fluids behave in these confined spaces is crucial for optimizing storage efficiency, minimizing leakage risks, and ensuring long-term environmental safety. This is where pore-scale modeling becomes essential.
Pore-scale modeling provides insights into capillary forces, fluid displacement, and phase interactions in porous geological formations by examining the movement of various fluids (CO₂, water, oil, and gas) through microscopic rock pores. Researchers have historically used numerical simulations and experimental tests to investigate these processes. However, these methods are often time-consuming and demand high-performance computational resources. To tackle these challenges, machine learning algorithms are increasingly integrated into pore-scale modeling to enhance predictive capabilities. By training models on extensive datasets derived from observational data and experiments, machine learning allows for quicker and more accurate predictions of how fluids interact in subsurface environments.
This research area offers significant promise for Nigerian and global universities, particularly in hydrology, geology, petroleum engineering, and environmental sciences. Pore-scale and other macro modeling techniques, when combined with machine learning, can be integrated into research frameworks to help institutions develop greater expertise in subsurface fluid dynamics, thereby improving scientific output related to climate change and environmental research in both developed and developing nations.
This field is increasingly relevant for carbon sequestration, groundwater management, hydrocarbon recovery, and environmental remediation. However, given the financial limitations many Nigerian institutions face, they can take advantage of open-source computational tools for fluid flow modeling and machine learning applications as an excellent starting point. Researchers can conduct pore-scale simulations without the need for expensive proprietary software using several high-performance applications, including OpenFOAM, TOUGH2, and Lattice Boltzmann Methods (LBM) simulators.
Furthermore, scalable computational power can be accessed through cloud-based computing services at a fraction of the cost of maintaining physical computing devices. These tools can be utilized by university researchers and students with basic training to investigate fluid flow in porous media and explore potential applications for CO₂ storage and other subsurface activities.
Another crucial element in overcoming resource limitations is collaboration.
Nigerian universities can access research funding, high-quality datasets, and advanced laboratory equipment by partnering with international organizations, foreign universities, government agencies, and industry leaders. Organizations like the Global CCS Institute, the Intergovernmental Panel on Climate Change (IPCC), and other energy-focused research groups actively support research on carbon sequestration and multiphase flow. By utilizing these international networks, Nigerian researchers expand their knowledge and make significant contributions to the scientific community, resulting in broader impacts overall.
Curriculum enhancement is essential for incorporating pore-scale modeling into Nigeria’s academic landscape. Even as the world advances toward cleaner energy alternatives, many colleges continue to implement curricula that strongly emphasize conventional petroleum exploration methods. Updating course material to include pore-scale fluid dynamics, machine learning applications in geology, and computational modeling for subsurface research will better prepare students for careers in carbon storage, renewable energy, and environmental sustainability. As industries seek professionals with experience in geotechnical modeling and data-driven decision-making, these skills are becoming increasingly valuable.
Beyond academia, pushing research in multiphase flow dynamics has direct economic and environmental policy implications for Nigeria. The nation is Africa’s largest oil producer, but oil extraction processes tend to lead to environmental degradation, groundwater pollution, and gas flaring emissions. Understanding the behavior of fluids in porous media can optimize enhanced oil recovery (EOR) techniques, reducing waste and enhancing production efficiency. Additionally, by adopting localized CO2 sequestration strategies, Nigeria’s energy sector emissions can be offset, contributing to global climate goals and generating revenue in carbon credit markets.
Nigeria’s growing population and increased urbanization also necessitate sustainable management of groundwater, a topic that relates significantly to multiphase flow studies, which affects the common man on the street. Most regions in the country rely on groundwater as the primary source of drinking water, yet aquifer contamination from industrial and agricultural activities remains a persistent problem across different regions in Nigeria. Techniques in pore-scale modeling used in CO₂ sequestration research can be applied to track groundwater flow, predict contamination pathways, and create effective remediation strategies. Universities researching this area can provide evidence-based recommendations to local policymakers to facilitate sustainable water resources for future generations, strengthening the United Nations sustainable development goal 6.
Despite budget and infrastructural constraints, Nigerian universities can make incremental progress in incorporating pore-scale modeling and machine learning research into their curricula. Establishing focused computational research groups, promoting interdisciplinary collaborations, and seeking international grants for climate-focused projects can help institutions incrementally develop capacity in this field.
In addition, postgraduate students and young scientists can spend their time on thesis and dissertation research on subsurface modeling, CO₂ storage, and multiphase flow dynamics-related problems, thereby generating locally relevant research to address the unique environmental challenges in Nigeria.
Scientific discoveries are no longer limited to well-funded laboratories in developed countries. High-quality scientific resources are now more widely accessible through the development of digital research tools, artificial intelligence, and global knowledge-sharing platforms. Universities in Nigeria have the chance to take the lead in research on climate change mitigation, contributing solutions that benefit the country and have far-reaching global implications. Institutions may significantly contribute to the worldwide transition toward climate resilience and sustainable energy by strategically investing in geological research, machine learning applications, and computational modeling.
Climate change is a global crisis, but solutions must be locally relevant and contextually appropriate. With the promise of pore-scale modeling, machine learning, and subsurface fluid research, Nigerian tertiary education can lead to climate adaptation and mitigation efforts. With the right vision, collaboration, and commitment to scientific progress, the country’s universities can convert current challenges into opportunities and create a sustainable, data-informed future.”
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