UBIQUITOUS COMPUTING AND PETROLEUM ENGINEERING: PREDICTIVE AND STATISTICAL MODELS FOR OIL EXPLORATION IN THE SADC REGION AND ANGOLA
DOI:
https://doi.org/10.63330/armv2n6-009Keywords:
Ubiquitous Computing, Machine Learning, Petroleum Engineering, SADC, Angola, Predictive Models, Artificial Intelligence, IoT, GeostatisticsAbstract
This scientific article analyses the epistemological intersection between Ubiquitous Computing and Petroleum Engineering, proposing predictive and statistical models to optimise oil exploration in the Southern African Development Community (SADC) region, with a particular focus on Angola. Drawing from Mark Weiser's 1991 pioneering vision of ubiquitous computing, this work demonstrates how the convergence of Machine Learning technologies, Internet of Things (IoT), remote sensing and big data analytics can structurally transform Angola's oil prospecting, extraction, and resource management processes. Developed models incorporate geological, seismic, and economic variables from the Congo Basin, Pre-Salt region, and onshore basins across the SADC. Preliminary results indicate up to 34% reduction in exploration costs and 28% improvement in drilling success rates when AI-driven predictive systems are integrated. This article aims to support Angola's Petroleum Ministry in designing policies aligned with international standards set by the IEA and OPEC+.
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