# An Unsupervised Anomaly Detection Based on Self-Organizing Map for the Oil and Gas Sector
## 2 Literature Review
[31] provides a comprehensive overview of machine learning applications in the oil and gas sector. The research shows that different machine learning techniques can improve industrial plant efficiency. [PERSON] et al. [32] expose the main artificial intelligence and machine learning techniques applied in the oil and gas industry. Through these techniques, it is possible to make optimal use of information from smart devices on plants and also keep track of flows in and out of the plant. In the paper of [PERSON] et al. [33], different types of machine learning applied to the oil and gas industry in upstream, midstream, and downstream processes are presented. Examples of how machine learning can optimize exploration, drilling, production, transportation, and refining operations are discussed. Finally, the document deals with the advanced processing of seismic data and analyses current artificial intelligence implementations and their impact on industrial processes. One problem that could arise in an industrial-oil context is related to possible leakage in oil or gas pipelines. [PERSON] et al. [34] developed a real-time ML model to detect pipeline leaks. They designed and compared five automatic classification architectures to conduct the research work. From the results obtained from the networks, they chose the Support Vector Machine (SVM) algorithm since it achieves 97.4 per cent accuracy. The gas-liquid ejector is one of the essential components of an oil and gas plant. This device can mix two flows at different pressures and impart the energy necessary for transport. However, the system will be unstable if a fault occurs in the ejector, and the operator's safety will be jeopardized. [PERSON], [PERSON] et al. [35] developed a Digital Twin of an ejector installed in an experimental plant. This model can predict the future state of the component and diagnose any fault on the line. Swarm Intelligence methods with minimal computational complexity and resource needs are used to build models to reduce system latency. The Multi-Phase Flow Metre (MPFM) is another device used in the oil and gas sector. An MPFM does not separate the phases as this is a time-consuming industry practice but instead offers real-time measurements of a well's gas, oil, and water flows. [PERSON] et al. [36] proposed research focused on the anomaly detection approach for MPFM data, which can successfully handle the complexity and unpredictability of the data because flow composition evaluation is crucial for excellent management and productivity prediction. These are unsupervised approaches, such as the Cluster-Based Local Outlier Factor and Isolation Forest, written as embedded programmes meant for plug-and-play implementations without tweaking the module for the well that hosts the MPFM. The method may be used for other equipment with several independent but connected modules, such as electric vehicles, batteries, and redundant systems, and enables the end user to quickly detect aberrant data and gain an indicator of measurement reliability. Monitoring anomaly detection systems in the oil and gas industries are typically Wireless Sensor Network (WSN)-based or Supervisory Control And Data Acquisition (SCADA)-based systems, which have significant limitations. SCADA systems communicate using different protocols vulnerable to various hacker attacks. Should there be hacker attacks, the security of the operator and the industrial facility could be jeopardized. [PERSON] et al. [37] propose a supervised machine learning algorithm (XGBoost) that can detect the Denial of Service (DoS) hacker attack. From the results obtained, the algorithm identifies this type of attack with 99% accuracy. The existing literature does not include studies on using a Self-Organizing Map (SOM)-type artificial neural network for anomaly identification in the oil and gas industry. To fill this gap, this study proposes an autonomous SOM-based algorithm, trained on various steady-state and anomaly tests, to provide comprehensive support for plant state identification.