Zainab Iyiola, a rising Nigerian researcher in petroleum engineering and data science, is gaining recognition on the global academic stage for her contribution to a groundbreaking study on enhanced oil recovery (EOR) using carbon dioxide and low-salinity water.

Her work features as a co-authored chapter in a 2024 Taylor & Francis publication, titled, Data Science and Machine Learning Applications in Subsurface Engineering, a peer-reviewed volume comprising high-impact studies shaping the future of hydrocarbon production.

Chapter 7 of the book, titled, Carbon Dioxide Low Salinity Water Alternating Gas (CO₂ LSWAG) Oil Recovery Factor Prediction in Carbonate Reservoirs, tackles a long-standing challenge in the oil and gas sector: how to efficiently extract more oil from carbonate reservoirs without increasing environmental or economic costs. The chapter applies advanced data-driven modeling to this issue and introduces a novel hybrid injection technique.

Iyiola’s contribution centre on the development of predictive models that estimate how much oil can be recovered when alternating carbon dioxide with low-salinity water injections in carbonate formations, a method known as CO₂ LSWAG.

This emerging technique is attracting international attention for its ability to combine environmental responsibility with technical efficiency.

Compared to traditional waterflooding or standalone CO₂ injection, the hybrid approach enhances both pressure support and wettability alteration in the rock, unlocking significant additional oil volumes.

What distinguishes the study is its use of machine learning to model the complex fluid interactions involved. Instead of relying solely on conventional reservoir simulations, which are resource-intensive and time-consuming, Iyiola and her team implemented two machine learning algorithms, Multivariate Adaptive Regression Splines (MARS) and the Group Method of Data Handling (GMDH), to develop proxy models.

These models were trained on extensive simulation datasets to forecast oil recovery factors under varying injection conditions and rock properties.
The results were striking.

The GMDH model, in particular, delivered oil recovery factor predictions as high as 99 percent, outperforming traditional forecasting methods. These data-driven models also proved robust in handling noisy and non-linear relationships, making them particularly valuable for field-level application.

“This work demonstrates how machine learning can support strategic decisions in reservoir development,” says Iyiola. “The models we built allow engineers to predict outcomes with minimal computational cost, accelerating planning and improving efficiency in the field.”

Her innovation has drawn praise from experts in the field. “Zainab’s contribution marks a turning point in data-driven EOR modeling,” reveals Dr. Eric Thompson Brantson, Senior Lecturer at the University of Mines and Technology.

“From her undergraduate days, she consistently demonstrated intellectual curiosity, discipline, and a rare blend of engineering intuition and computational skill. Her work today represents the future of sustainable petroleum engineering, and I have no doubt she will continue to drive innovation at the global level.”

Experts believe the implications of this study could be especially transformative for countries like Nigeria. With vast carbonate reserves across the Niger Delta and other basins, the CO₂ LSWAG method offers a promising pathway to boost production from mature fields without requiring major infrastructure overhauls. Additionally, the partial sequestration of CO₂ during injection aligns with national and global efforts to reduce greenhouse gas emissions.

The study also highlights the growing influence of Nigerian researchers in international scientific advancement. Iyiola, who brings a unique blend of petroleum engineering knowledge and data science expertise, played a leading role in designing and validating the machine learning models.

Her ability to bridge traditional reservoir engineering with modern computational methods is gaining attention across academia and industry. In addition to her technical achievements, Iyiola’s work contributes to a broader push toward smart EOR strategies that incorporate carbon management and predictive analytics.

Her research portfolio includes efforts in Carbon Capture, Utilisation, and Storage (CCUS), hydrogen energy modeling, and the creation of decision-support tools for optimising energy infrastructure.

Currently pursuing graduate studies in petroleum engineering and data science at the University of Oklahoma, Iyiola is a graduate researcher at the university’s prestigious Well Construction and Technology Centre.

There, she is involved in a U.S. Department of Transportation-funded project aimed at improving the safety and reliability of hydrogen pipeline infrastructure, a line of research with the potential to transform global energy systems.

She previously graduated top of her class from the University of Mines and Technology in Ghana and was selected as one of the top 100 petroleum engineering students worldwide for the 2022 International Petroleum Technology Conference (IPTC) Education Week in Riyadh, Saudi Arabia.

In 2023, she was named one of only eight Africans to receive the African Energy Chamber’s Energy Scholar Award. In 2024, she was also one of just four global recipients of the SPE Egbert Imomoh Scholarship, one of the Society of Petroleum Engineers’ most competitive and prestigious scholarships awarded yearly to exceptional African graduate students demonstrating leadership potential and academic excellence in petroleum engineering.

Most recently, Iyiola was awarded the prestigious rank of Fellow by the National Institute of Professional Engineers and Scientists (NIPES), a recognition reserved for professionals who have demonstrated outstanding leadership and technical excellence in the field.

This honour reflects her growing stature within the global petroleum engineering community and underscores her contributions to advancing innovation in the energy sector. Professionally, Iyiola is an active member of the Society of Petroleum Engineers (SPE), where she supports technical outreach, mentors young engineers, and champions the integration of data-driven technologies in energy exploration and production.

One of her recent research works, titled, Investigating the Impact of Feature Engineering Techniques on the Predictions of Well Log Attributes, has been accepted for presentation at the 2025 Nigeria Annual International Conference and Exhibition (NAICE), following a rigorous blind peer review process.

NAICE is one of Africa’s most prestigious oil and gas conferences and a flagship event of SPE, attracting leading professionals, researchers, and industry stakeholders from around the world.

After the conference, the paper will be published on OnePetro, the world’s largest repository of technical literature in petroleum engineering, widely regarded as a prestigious platform for disseminating cutting-edge research within the professional energy community.

As Nigeria explores sustainable methods to improve oil recovery, curb emissions, and modernise its energy systems through data and technology, researchers like Zainab Iyiola are helping to chart the course, bringing innovation, precision, and purpose to the forefront of the nation’s energy future.