# An Unsupervised Anomaly Detection Based on Self-Organizing Map for the Oil and Gas Sector
## 2 Literature Review  
The literature review about the oil and gas sector was conducted systematically to thoroughly evaluate all relevant scientific studies. The literature analysis was conducted using the SCOPUS database. The first keyword digit in the database was \"oil and gas sector\". The search produced 2023 results. The results obtained from the research are based on the information in the abstract, introduction, and keywords. Figure 1 represents a timeline for the number of publications by year. Based on an initial analysis of the literature, it can be inferred that the first studies on oil and gas plants were conducted in the 1980s, and the subject remained relatively unexplored for many years. However, research in this area started gaining momentum around the early 2000s and has since experienced a consistently growing trend. Indeed, the industrial oil and gas sector underwent significant changes and improvements in the 2000s, thanks to the introduction of new technologies and the increasing attention to safety [25]. During the search, specific filters were applied to identify the most relevant articles. For the literature analysis, only scientific journal articles published in English between 2019 and 2023 were considered to keep up with the latest research studies on oil and gas and how new enabling technologies have improved production processes. A thorough examination of all articles was conducted to assess their relevance and appropriateness concerning the new technologies in the oil and gas sector. Table 1 presents the selected keywords, the number of papers retrieved by Scopus, and the relevant papers for this literature review. According to all the scientific papers reviewed, industrial oil and gas plants can be called complex systems because they comprise numerous interconnected elements that aim to extract resources from underground. Therefore, maintaining safety in these plants requires strict control of numerous components. Improved reliability can be achieved by predicting which components will fail during a specific time interval or identifying those operating under abnormal conditions [26; 27]. However, this is challenging, especially when several parameters must be considered simultaneously, such as the probability of failure, maintenance costs, etc. [28]. Thus, as mentioned before, the definition of effective maintenance policies is the main objective to support companies in dealing with process interruptions while preventing significant profit losses [29]. [PERSON] et al. [30] propose a deep learning algorithm named recurrent neural network-gated recurrent unit (RNN-GRU) capable of monitoring the health of machines \begin{table}
\begin{tabular}{c c c} \hline \hline
**Keyword** & **\# of Papers** & **\# of Relevant Papers** \\ \hline “oil and gas sector” AND “machine learning” & 11 & 3 \\ “oshore platform” AND “machine learning” & 1 & 1 \\ “oil and gas sector” AND “anomaly detection” & 11 & 8 \\ “oil and gas sector” AND “artificial intelligence” & 4 & 1 \\ “oil and gas sector” AND “digital twin” & 2 & 1 \\ “multiphase flow” AND “digital twin” & 6 & 1 \\ “oil and gas sector” AND “Internet of Things” & 5 & - \\ “oil and gas sector” AND “artificial neural network” & 4 & 1 \\ “oil and gas sector” AND “Self-Organizing Map” & 1 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the selected literary contributions. Figure 1: Oil and gas documents by year. inside the oil industrial complex. Predicting an imminent event is a fundamental action to safeguard the health of the plant and the safety of workers. Additionally, knowing the status of machines in an industrial line also helps save costs in the event of an unexpected breakdown. For example, an unplanned breakdown of a machine, in addition to halting the machine itself, could stop the production line, causing losses of up to millions of euros. This algorithm achieves an overall accuracy of 87 per cent in predicting the state of machines. The study by [PERSON] et al.