All Outputs (2)

Development of an expert-informed rig state classifier using naive Bayes algorithm for invisible loss time measurement. (2024)
Journal Article
YOUCEFI, M.R., BOUKREDERA, F.S., GHALEM, K., HADJADJ, A. and EZENKWU, C.P. 2024. Development of an expert-informed rig state classifier using naive Bayes algorithm for invisible loss time measurement. Applied intelligence [online], 54(17-18), pages 7659-7673. Available from: https://doi.org/10.1007/s10489-024-05560-5

The rig state plays a crucial role in recognizing the operations carried out by the drilling crew and quantifying Invisible Lost Time (ILT). This lost time, often challenging to assess and report manually in daily reports, results in delays to the sc... Read More about Development of an expert-informed rig state classifier using naive Bayes algorithm for invisible loss time measurement..

Monitoring carbon emissions using deep learning and statistical process control: a strategy for impact assessment of governments' carbon reduction policies. (2024)
Journal Article
EZENKWU, C.P., CANNON, S. and IBEKE, E. 2024. Monitoring carbon emissions using deep learning and statistical process control: a strategy for impact assessment of governments' carbon reduction policies. Environmental monitoring and assessment [online], 196(3), article number 231. Available from: https://doi.org/10.1007/s10661-024-12388-6

Across the globe, governments are developing policies and strategies to reduce carbon emissions to address climate change. Monitoring the impact of governments' carbon reduction policies can significantly enhance our ability to combat climate change... Read More about Monitoring carbon emissions using deep learning and statistical process control: a strategy for impact assessment of governments' carbon reduction policies..