Bi-Directional Long Short-Term Memory Variational Autoencoder for Real-Time Bit-Wear Estimation

Abstract

Drilling operations rely on learned expertise in monitoring the drilling performance data and the rock data to assess the dull condition of the drill bit. While human learning can subjectively pick up the indicators based on rig surface data streams, this information is highly convoluted with changes in rock and drilling data. Recent approaches for bit wear estimation also include model-based and traditional supervised machine learning methods, which are usually costly and time-consuming. In this study, we developed a bi-directional long short-term memory-based variational autoencoder (biLSTM-VAE) to project raw drilling data into a latent space in which the real-time bit-wear can be estimated. The proposed deep neural network was trained in an unsupervised manner, and the bit-wear estimation is demonstrated as an end-to-end process.

Application of Machine Learning Methods in Modeling the Loss of Circulation Rate while Drilling Operation

“…Most artificial intelligence (AI) and machine learning (ML) techniques can potentially solve practical problems by learning from large historical data sets, something that conventional analytical models cannot do. , The applications of AI in drilling operation activities have evolved over recent years due to their flexibility in classification, optimization, prediction, and selection . These applications include, but are not limited to, identification of formation lithology, estimation of pore and fracture pressures during the drilling operation, , real-time prediction of drilling fluid properties, formation identification while drilling using mechanical surface parameters, early warning signs detection while drilling horizontal wells, use of an Internet-of-things (IoT) environment integrated with cameras and high-computation edge server to implement a deep learning model for proper drill string space out when a well control incident occurs during drilling, employing of raw drilling data to estimate the drilling bit- wear in real time using a bidirectional long short-term memory-based variational autoencoder, and determination of downhole vibrations while drilling surface hole sections to mitigate premature drill string failures …”