The oil and natural gas industry is a crucial sector in our daily lives. Unwanted incidents in this sector can significantly impact the household sector. Therefore, an automatic early warning system is necessary to detect errors in the pipeline network connecting production and processing points. Anomaly detection methods can be used to overcome these problems. One model suitable for unsupervised anomaly detection is the deep learning Long Short-Term Memory (LSTM) model. This research aims to implement an LSTM-based deep learning anomaly detection model on time-series operational data from an observation point situated within the sink, which consists of 17 usable features and 8,736 data points, representing a year’s worth of data. Model selection involves optimizing hyperparameters (e.g., dropouts, regularizers, layers, and batch sizes) using the Mean Squared Error (MSE) through 3-fold cross-validation, resulting in 10 model candidates. The best-performing model is then trained using the training data. After training, the LSTM model reconstructs the original time-series data to calculate anomaly scores based on the Euclidean distance metric. These scores determine anomalies using a set threshold derived from the distribution of anomaly scores. Human interpretation validates the model's capability to accurately identify anomalies within the dataset. Infrastructure requirements for real-world applications are also discussed, focusing on the use of edge computing methods to enhance real-time anomaly detection capabilities.