Application of machine learning in wellbore stability prediction: A review

Ensuring wellbore stability is vital for safe drilling and production in the process of oil exploration and development. The current empirical formula and numerical simulation methods used to analyze wellbore stability have limitations such as low adaptability and high computational complexity. In contrast, machine learning methods have gained wide attention in the oil and gas industry due to their ability to efficiently handle big data and adaptability. The use of machine learning methods for predicting wellbore stability has become a current research focus, but there is a lack of introduction to research progress in prediction methods and models. Therefore, this paper systematically investigates and summarizes the research progress in intelligent prediction of wellbore stability. First, the intelligent prediction methods for wellbore stability are divided into non-full process and full-process machine learning methods, with the full-process machine learning methods showing better application results. Next, the advantages and disadvantages of various models in current practice are analyzed, with the neural network model having great potential due to its strong adaptability. Finally, by analyzing existing research achievements and issues, it is believed that the future important research directions include the application of deep learning and reinforcement learning models, integration of multi-source data, and utilization of real-time monitoring data for dynamic prediction. These directions are expected to provide more reliable wellbore stability assessment for the drilling industry, thereby assisting and optimizing construction plans and reducing risks.