Influx detection and well control are critical situations where the traditional human reaction has been the accepted standard for years. This paper discusses the results of the driller stress tests and the implementation of a system to assist the operator in kick detection, space out and preparation for well shut in. The system implements dynamic well monitoring to reduce the risk of false alarms. The objective is to prove how automation helps mitigate human factors. A stress analysis test was conducted on a variety of experienced drillers to identify the specific operations with the highest risk factors influenced by operator stress. Once the target operations were identified, an automation algorithm was designed and tested to mitigate the human factor during these specific instances. The resulting system detects drilling kicks and automatically performs the space out operation. Dynamic trip and active tank monitoring were implemented to reduce false alarms. This provides the driller with an assistant for tripping operations. The package can be adapted to any type of rigs or blow out preventer (BOP) stacks without requiring additional hardware. Testing was conducted in both simulated and real-life situations, during tripping and drilling operations. The system was able to predict tool joint positions in the well with a mean error consistently below 1%. The automation of the space out operation allowed the system to perform the operation in significantly shorter times and with higher accuracy, eliminating the risk of any tool joint being placed across BOP elements. During tripping operations, dynamic tank tracking effectively eliminated risks of kicks helping the driller keep a constant and adequate filling of the well. Finally, a comparison of the operator stress levels with and without the use of the automation package shows the positive impact such a system can have on situational awareness and concentration. The plug and play aspect of the system proved critical for easy and fast implementation, as well as ensuring a quick familiarization of the driller with the different functionalities. The tests also highlighted the importance of accurate and high-resolution sensors to ensure optimal working conditions. Automation in the field of well control is a relatively new subject. This paper showcases the impacts this type of systems can have on operations and proposes an implementation method to profit from automation while minimizing its impact on critical operations. The field implementation showed how different sensor configurations can lead to different degrees of automation and, thus, different impacts on the operations.

Technical training is an essential activity for optimizing rig operations. Recently, the use of drilling simulators has revolutionized the way training is done and, accompanied with on-site assistance, it has ensured near optimal performance from the trained crews. This paper explains how machine learning and physiology can be used to improve rig technical training by monitoring the operator's stress, identifying the key operations where situational awareness is low and targeting these operations with dedicated exercises. The developed methodology is based on a study of human psychological indicators captured through light biometric devices. These indicators are fed to a machine learning algorithm that calculates a stress index for the observed operator and uses this index to identify key operations where the operator lacks focus, is under high stress or feels a lack of preparation. The measured indicators are skin temperature, specific face movements, heart rate, and sweat. The model uses machine vision to identify key physiological parameters and a convolutional neural network to interpret them. Finally, a third algorithm correlates the stress index to specific operations. The system can be used either in simulation environment or on the rig itself during operational studies. The primary results show high detection accuracy with minimal errors. Using this methodology for well control simulation, the main periods of high stress and low concentration were correctly identified. The repeated tests showed that different drillers or supervisors respond differently to the situation and may be stressed out by different operations. This highlighted a key drawback of the training that focuses on the same main operations for all participants. By customizing the second training session for each participant's needs, the high stress levels were significantly reduced. From the initial trials, a key point needed to be highlighted: for the study to be as non-intrusive as possible, the biometric devices used for monitoring stress need to be as light as possible. This led to a review of the devices used and a compromise between accuracy and lightness. As with advanced military training, targeted training for drilling rig crews can deeply impact the outcome of the training and preparedness of the crew. Today, biometric devices combined with machine learning models finally, allow for an accurate detection and evaluation of human stress. Using this analysis methodology to customize training will prove essential soon and may revolutionize the way rig crews are trained.

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.