The thesis covers the concept of unsupervised learning algorithms. These include different clustering methods such as K-means, Fuzzy C-means, and Hierarchical clustering. The models are utilized to classify wells in the oil and gas sector. Firstly, the study highlights the essence of accurately classifying wells in the context of operational efficiency in addition to the benefits of applying machine learning (ML) in well classification. It assesses how unsupervised models can be used to transform operations in the energy industry. The investigation aims to determine the comparative benefits of each clustering algorithm in the energy sector. Some core constraints highlighted under each model include reservoir characteristics, well settings, and operational variables. These attributes determine the efficiency of each algorithm for well classification. The thesis also highlights the significance of feature selection when undertaking unsupervised learning, such as enhancing clustering precision and model efficacy. The study methodology follows a concise process involving multiple steps starting from data collection to model evaluation. Each algorithm is examined based on the performance classification of the well data. The analysis focuses on validating clustering models through various evaluation metrics, as well as the advantages and limitations of each model. The investigation establishes that K-means is simple and effective, while Hierarchical clustering allows one to extract nuanced insights into the structure of a dataset. Fuzzy C-means enables soft-clustering yet struggles with defining separated clusters. The study establishes that all the models have a transformational potential for the industry. As such, unsupervised learning effectively addresses the sector's classification needs. Future investigations must delve deeper into algorithm enhancement, integration with supervised learning, and alternative clustering models. The current findings can be subjected to further validation studies and applied to real-world datasets. It will increase operational efficiency and enhance decision-making in the oil and gas energy industry.