Version 1
: Received: 12 October 2023 / Approved: 13 October 2023 / Online: 17 October 2023 (11:29:47 CEST)
How to cite:
Chigarev, B. Analyzing the Transferability of Anomaly Detection Knowledge from Computer Science and Mathematics Publications to the Field of Oil and Gas Research. Preprints2023, 2023101089. https://doi.org/10.20944/preprints202310.1089.v1
Chigarev, B. Analyzing the Transferability of Anomaly Detection Knowledge from Computer Science and Mathematics Publications to the Field of Oil and Gas Research. Preprints 2023, 2023101089. https://doi.org/10.20944/preprints202310.1089.v1
Chigarev, B. Analyzing the Transferability of Anomaly Detection Knowledge from Computer Science and Mathematics Publications to the Field of Oil and Gas Research. Preprints2023, 2023101089. https://doi.org/10.20944/preprints202310.1089.v1
APA Style
Chigarev, B. (2023). Analyzing the Transferability of Anomaly Detection Knowledge from Computer Science and Mathematics Publications to the Field of Oil and Gas Research. Preprints. https://doi.org/10.20944/preprints202310.1089.v1
Chicago/Turabian Style
Chigarev, B. 2023 "Analyzing the Transferability of Anomaly Detection Knowledge from Computer Science and Mathematics Publications to the Field of Oil and Gas Research" Preprints. https://doi.org/10.20944/preprints202310.1089.v1
Abstract
Anomaly detection in equipment processes plays an important role in the oil and gas sector. Algorithms for detecting anomalies in measured data are best understood in computer science and mathematics. Therefore, a possible transfer of knowledge from the latter knowledge area to the former can play a significant role. This paper addresses such a task by analyzing bibliometric data of Computer Science and Mathematics papers published in MDPI journals and publications found on the SPE search platform. It is shown that the main algorithms both extensively studied in MDPI publications and found in SPE publications and reflecting the anomaly detection problem are Random Forest, Support Vector Machine, Long-term Memory Method and Recurrent Neural Network. The main advantages and disadvantages of these methods are briefly described. Examples of classical, highly cited publications describing the work of these algorithms are given. Examples of papers describing their application in the oil and gas industry are given. The sections of SPE disciplines with the largest number of publications using the above algorithms that are frequently used for anomaly detection are presented.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
