Pan Ocean Oil Corporation is deploying artificial intelligence and machine learning to squeeze more production from ageing Nigerian oil infrastructure, as the country’s upstream sector grapples with assets that in some cases date back nearly seven decades.
Speaking at Nigeria’s International Energy Summit in Abuja, Abiodun Ogunjobi, group chief technical officer for Pan Ocean and its sister company Newcross, outlined how the company has established a dedicated research and data analytics team to develop customised technological solutions for operations hampered by deteriorating equipment and manual processes.
“We operate assets where the first well was drilled in 1958, so that tells you the ageing nature of those facilities,” Ogunjobi said. “Asset integrity is very important for us.”
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The company’s approach reflects broader challenges facing Nigeria’s oil sector, where ageing infrastructure and underinvestment have contributed to production declines. While Nigeria holds Africa’s largest crude reserves, output has struggled to match its OPEC quota in recent years due to technical difficulties, theft, and ageing facilities.
Pan Ocean has moved beyond standard off-the-shelf software, which Ogunjobi said often fails because solutions are not tailored to the operational realities of specific assets.
Instead, the company builds proprietary tools to automate data processing and enable predictive maintenance, allowing engineers and managers to view information simultaneously and make faster decisions.
The CTO emphasised that upstream oil operations generate vast amounts of data, but processing it manually takes considerable time, delaying critical operational decisions. “We don’t just look at data. We automate data,” he said, noting that many Nigerian assets still rely on spreadsheets and manual processes.
Machine learning systems can monitor normal flow patterns within pipelines by tracking vibration, temperature, and pressure. When abnormalities are detected, sensors trigger alerts that enable planned maintenance rather than reactive emergency interventions. “AI is here to bridge the gap between planned deferment and unplanned deferment,” Ogunjobi explained.
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This predictive approach has proven particularly valuable for crude evacuation operations. Pan Ocean has deployed what Ogunjobi described as “an eye in the sky” monitoring system that tracks crude shipments from facilities to the Bonny Terminal. The company has evacuated over 1,000 vessels carrying a cumulative 52 million barrels without a single lost-time injury, according to the CTO.
The technological investments span subsurface exploration optimisation and reservoir analysis, helping the company better understand reservoir heterogeneity and improve recovery rates from mature fields.
Ogunjobi acknowledged that Nigeria’s oil sector lags behind international peers in technology adoption, despite generating substantial data. “We are lagging, but we generate a lot of data, but we are not paying attention to this data,” he said.
The stakes are considerable. Unplanned downtime from equipment failures not only reduces production and revenues but can create supply disruptions and safety hazards. With aging assets requiring more intensive monitoring, machine learning offers a way to prevent costly surprises.
“When you have an ageing asset you need to listen to the data and interpret the data, and that is what AI is here to do for us,” Ogunjobi said, adding that artificial intelligence would be “a determinant factor for tomorrow” regardless of industry reluctance.
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Pan Ocean’s technology-driven strategy comes as Nigeria seeks to revive oil production and attract fresh investment. The country has implemented reforms aimed at improving the business environment for oil companies, though security challenges and regulatory uncertainties persist.
The company plans to expand its AI capabilities into additional asset integrity management applications, building on existing successes in operational systems that have been scaled across Pan Ocean’s portfolio.
