Automatic Drilling Dynamics Interpretation Using Deep Learning

Abstract

Abstract Severe drilling dynamics of a bottomhole assembly (BHA) causes energy to dissipate into vibrations which undermines drilling efficiency. Dangerous dynamics modes, such as backward whirling and high frequency torsional oscillation, could cause downhole drilling tools to fail prematurely. To mitigate the risk of failure due to these dangerous conditions, it is critical to identify the damaging dynamics modes by interpreting the drilling data. Based on a deep learning approach, a novel method was proposed to automatically identify severe drilling dynamics modes directly from the time-series data. The drilling dynamics data can be obtained from either a downhole sensor measurement or transient dynamics simulation. First, a deep neural network, which is composed of convolutional and fully connected layers, is employed to explore patterns in the data by generating a feature map of drilling dynamics. Knowledge of drilling dynamics physics can be used to facilitate data clustering in the feature map. Each data cluster can be tagged with the corresponding drilling dynamics mode. Using the tagged dataset, a machine learning classification model can be trained to automatically identify the dynamics modes based on the input of time-series drilling data. The deep learning approach can be implemented to recognize a collection of dynamics modes of BHA, such as various whirling patterns and high frequency torsional resonance. The most commonly available drilling dynamics data channels, accelerations and collar RPM, were used as the model inputs. The deep neural network was trained to predict the next data sample based on the previous time-series data. One of the hidden layers of the neural network was employed to generate the feature map, in which the dataset forms several clusters. The orbits of BHA movement were plotted on top of the clusters for pattern visualization. After this practice, the simple polygon boundary was drawn between whirling and stable cases, and the dataset was tagged automatically. With the tagged dataset, the classification model was trained to identify various whirling patterns and the stable drilling state. Similar processes can be readily applied to interpret other dynamics modes. Interpreting the drilling dynamics modes provided a high-level description of the data, which offered clues on how to optimize BHA design and drilling practices to improve efficiency. The automatic interpretation of drilling dynamics data can significantly improve the consistency and efficiency of the existing manual interpretation workflow. The generated feature map enables the exploration of new motion patterns and new vibration modes. This approach eliminates the need to manually tag the data. With minimum human interactions, the dataset can be automatically tagged. The model employs only the raw time series data of basic dynamics channels as inputs, which makes the algorithm universally applicable for various data sources.