A Case Study on the Use of Machine Learning and Data Analytics to Improve Rig Operational Efficiency and Equipment Performance

Abstract

Ensuring an efficient workflow on a drilling rig requires the optimization of the equipment output and the extension of its working life. it is essential first to identify equipment behavior and usage and evaluate their possible efficiency variation. This can lead to predicting possible upcoming usage trends and proposing preventive actions like adjustment to equipment working parameters to improve its output and efficiency. In this regard, machine learning and data analytics provide a clear advantage. This paper showcases a case study that makes use of machine learning to detect rig inefficiencies and optimize operations. The platform has been implemented to first collect the rig data and then process it before sending it to be analysed. The rig used in this case study was connected to a platform that makes use of Internet of Things (IoT) protocols. Noise and redundancy of the data coming from the rig were standardized, filtered and therefore the outliers were removed. Feature selection was used to highlight, from the data pool, the most significant parameters for forecasting and optimization. These resulting parameters were then sent to the machine learning model for training and testing. The processed data was then fed to system, which was developed in-house, to extract additional information regarding equipment efficiency. This system tracks the variations in equipment efficiencies. The study focuses on the performance of an HPU powering a hydraulic hoisting rig which was showing low efficiency. IoT technology was used to collect live data from the field. The gathered datasets were cleaned, standardized and divided into coherent batches ready for analysis. Machine learning models were used to evaluate how the workload would change with tweaks to working parameters. Then, the study analyzed the rig tripping speed and how it was connected to HPU performance. For evaluation of tripping speed, the focus was given also to small operational changes which could lead to improved performance. When connected together, changes to both operating parameters and standard procedures can lead to improved efficiency and reduced invisible lost time. Implementing the results allowed the rig to be operated at a higher efficiency, thereby increasing the life of the equipment while keeping the load within design conditions. This ultimately resulted in a reduction in operational time and failure of equipment and hence a major decrease in down time of the rig.