Enhancing Cost Forecasting Accuracy in Nigeria’s Underground Oil and Gas Infrastructure through Advanced Digital Sensing Technologies

Abstract

This study examines the technical use of advanced digital sensing technologies to improve the accuracy of cost forecasting in the underground oil and gas infrastructure in Nigeria. Using a mixed-methods approach with 370 survey respondents and 12 expert interviews, the research shows that sensing tools such as DAS, DTS, IoT probes and GPR hold great potential for improving the reliability of predictive tools by providing continuous and high-resolution data on the condition. Quantitative analysis indicates that the most important predictor of forecasting accuracy is digital sensing, while qualitative insights help discard that the following factors lead to more stable budgeting outcomes: early fault detection, reduction of maintenance uncertainty, and improved documentation. Organisational capacity and regulatory gaps, however, prevent full use of technology. Overall, the study makes the digital sensing a strategic economic enabler and can reduce cost overruns and strengthen long-term asset management in Nigeria's petroleum pipeline network.