- author = "Lars Vidar Magnusson and J. Roland Olsson and Chau Thi Thuy Tran",
- title = "Recurrent Neural Networks for Oil Well Event Prediction",
- journal = "IEEE Intelligent Systems",
- year = "2023",
- volume = "38",
- number = "2",
- pages = "73--80",
- month = mar # "-" # apr,
- keywords = "genetic algorithms, genetic programming, ADATE, ANN",
- ISSN = "1541-1672",
-
DOI = "
doi:10.1109/MIS.2023.3252446",
- size = "8 pages",
- abstract = "We have conducted a comparison between three types of recurrent neural networks and their ability to predict anomalies occurring in oil wells using a publicly available dataset. We have included two types of well-known state-of-the-art recurrent neural networks and a new type with neurons evolved specifically for the dataset using automatic programming. We show that the new type of recurrent neuron offers a massive improvement over the state of the art. The overall test accuracy of the new network type is 94.6percent, which is an improvement by 18.3percent, or 14.6 percentage points. We also show that a network with the new neuron performs better than any other solution proposed for the dataset.",
-
notes = "also known as \cite{10058896}
Ostfold University College, Department of Computer Science and Communication, NO-1757, Halden, Norway",
