A Hybrid Machine Learning Model for Intelligent Asset Integrity in the Oil and Gas Industry

Asset integrity management is essential in many industries, especially oil and gas, to ensure assets operate efficiently and safely. Traditionally, maintenance monitoring has relied on manual inspections. However, this approach is time-consuming, labor-intensive, inefficient, error-prone, ineffective for real-time tracking, and incurs high operational costs. This study seeks to leverage artificial intelligence (AI) to minimize reliance on manual inspections and maintenance, thereby reducing human error and enhancing efficiency. This paper proposes a hybrid machine learning model utilizing a recurrent neural network (RNN) and long short-term memory (LSTM) for asset integrity management using oil and gas plant data to detect potential issues before they occur. The hybrid model combining LSTM and RNN enhances predictive performance and analysis in dynamic contexts by leveraging the advantages of both algorithms. Using a Monte Carlo simulation approach with 100 iterations, we demonstrate that the LSTM-RNN model performs better than traditional RNN, LSTM, and hybrid RNN-LSTM models for time-series data. Subsequently, we implement the models using oil and gas industry data through transfer learning. Our results show that the LSTM-RNN model can serve as a baseline for an autonomous system that maintains assets in the oil and gas industry.