Nigeria National Petroleum Company (NNPC) Limited, on Tuesday, presented a technical paper at the SPE Technical Programme of the ongoing 2026 Abu Dhabi International Petroleum Exhibition and Conference (ADIPEC) on the use of machine learning to assess the oil and gas potential of source rocks in frontier basins.
The paper, titled: “Petroleum Target Explorer (PTX-AIM): A Rigorously Validated Machine Learning Framework for TOC Prediction in Frontier Basins: A Case Study from the Kolmani River-2 Well, Gongola Basin, Upper Benue Trough, Nigeria,” was presented by Dr. Fodio Longman, Research Fellow, NNPC Research, Technology & Innovation (NNPC RTI), and co-authored with Husaini Balarabe and Ifeanyichukwu Edeh, both of NNPC RTI.
Dr. Longman explained that the estimation of Total Organic Carbon (TOC), the principal measure of how rich a source rock is in the organic matter that generates oil and gas, is difficult in frontier basins because laboratory measurements are scarce and conventional log-based methods are affected by geological variation and the quality of available data.
Commenting on the presentation, the Chief Innovation Officer, NNPC RTI, Rasheed Ojulari, said the paper reflects the growing research capability of NNPC RTI and its application of machine learning to petroleum system analysis in frontier basins, in support of efforts to grow Nigeria’s hydrocarbon reserves at lower cost.
