Determining Severity of Lateral and Torsional Downhole Vibrations While Drilling Surface Holes Using Three Machine Learning Techniques

Abstract

Summary Downhole vibrations while drilling surface holes could pose significant operational risks, such as premature failure of drillstring components. Using expensive downhole sensors is the most accurate and reliable technique to monitor downhole vibrations in real time. The high cost, however, hinders the operator to use such sensors in each well. This research aims to build different machine learning (ML) models, namely, K-nearest neighbors (K-NN), logistic regression (LR), and random forests (RF), to determine the severity of downhole vibrations (i.e., lateral and torsional while drilling surface holes). Actual field data of 10 wells, where the operator used downhole sensors to monitor downhole vibrations, were used to build predictive models. The input variables included drilling surface parameters and daily operational drilling reports, while the output variables were the severity of the downhole vibrations. One-hot encoding was applied to all categorical variables to convert them to a suitable format for ML modeling. During the model’s development, a new approach that considers the previous actual and predicted vibrations was applied to enable the ML models to learn from the past to classify the severity of vibrations in the future. The developed models were optimized by tuning specific hyperparameters for each technique to reach the optimum models’ performance. Well 11 was used to validate the developed models. The analysis showed that the data set of Well 11 is not balanced in terms of the severity of vibrations. Therefore, the performance of the models was evaluated using macroaveraging and microaveraging methods to depict the actual performance of the models in all classes of the severity of vibrations. The results showed that the LR model outperformed the other models in predicting the severity of downhole lateral and torsional vibrations in Well 11. This indicates that there is a stronger relationship between the input variables and the output variables in our data set in the case of the LR model compared with other models. The approach described in this paper shows potential for continuous monitoring of downhole vibrations in the subsequent offset wells without the need for installing downhole sensors, hence reducing the overall cost of the well.

Machine Learning Solution for Predicting Vibrations while Drilling the Curve Section

“…The models were optimized by tuning specific hyperparameters for each technique to reach the optimum models’ performance. The performance of the models was evaluated using macroaveraging and microaveraging methods, and the logistic regression (LR) model outperformed the other models [K-nearest neighbors (K-NN) and random forests (RF)] in predicting the severity of downhole lateral and torsional vibration blind well data (Alsaihati and Alotaibi 30 ). Recent research has studied the drillstring vibration failure in ultrahigh temperature and high-pressure wells with a curvature profile and proposed an axial-lateral-torsion coupling nonlinear vibration model that was established using the energy method and Hamiltonian principle.…”

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 …”