Intelligent Prediction of Downhole Drillstring Vibrations in Horizontal Wells by Employing Artificial Neural Network

During the drilling operations and because of the harsh downhole drilling environment, the drill string suffered from downhole vibrations that affect the drilling operation and equipment. This problem is greatly affecting the downhole tools (wear and tear), hole problems (wash-out), mechanical energy loss, and ineffective drilling performance. Extra non-productive time to address these complications during the operation, and hence, extra cost. Detecting the drillstring vibrations during drilling through the downhole sensors is costly due to the extra service and downhole sensors. Currently, the new-technology-based solutions are providing huge capabilities to deal intelligently with the data, and machine learning applications provide high computational competencies to learn and correlate the parameters for technical complex problems. Consequently, the objective of this paper is to develop a machine learning model for predicting the drillstring vibration while drilling using machine learning via artificial neural networks (ANN) for horizontal section drilling. The developed ANN model was designed to only implement the surface rig sensors drilling data as inputs to predict the downhole drilling vibrations (axial, lateral, and torsional). The research used 5000 data set from drilling operation of a horizontal section. The model accuracy was evaluated using two metrics and the obtained results after optimizing the ANN model parameters showed a high accuracy with a correlation coefficient R higher than 0.97 and average absolute percentage error below 2.6%. Based on these results, a developed ANN algorithm can predict vibration while drilling using only surface drilling parameters which ends up with saving the deployment of the downhole sensors.