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.

Summary Underground gas storage (UGS) wells are essential components in energy security. However, UGS wells present a complicated and delicate combination of elements where ensuring safe and secure functionality over long periods is paramount. This paper showcases how a digital twin is used to evaluate and forecast the link between leaks and temperature and pressure trends in a UGS well, allowing the identification and quantification of defects and, subsequently, well barrier integrity. The digital twin used for this application presents advantages compared with other solutions present on the market with regard to the simplified configuration; that is, with minimal input data, the system can produce an accurate and useful output, which is then used in the well integrity decision-making process. UGS wells present additional criticalities with respect to normal production wells due to their longer life span and the repetitive production and injection cycles. This makes early and accurate leak detection essential for the safe management of the well barriers. The proposed digital twin simulates the trends of pressure and temperature within each annulus and compares results with data from the field, allowing the identification of the position and size of leaks. A genetic algorithm is applied to optimize the placement of leaks on their specific barriers. Once a leak is identified, a risk assessment is conducted to evaluate the overall integrity of the well. If the status of the well is found to be critical enough, an intervention may be planned. The studies presented show how the digital twin has been used on two wells with similar problems. At first, it has confirmed the necessity to put the well out of service as opposed to planning maintenance, thereby saving both time and cost. In the second case, it allowed the validation of a solution that led to a 60% reduction in failure consequence, allowing the well to continue operating without major costs or risks. The errors of the resulting simulations were always confined within the 0.5 bar limit highlighting its accuracy. The system has been in use for over a year and has shown great potential in accurate and efficient identification of leaks. This has accelerated the process of well integrity evaluation and allowed timely interventions on wells that required it. On the other hand, the process has highlighted cases where previous assumptions about leak location and size were corrected using the digital twin, therefore reducing the costs of interventions. Finally, the model showcases a clear readiness for predictive capabilities aimed to select, plan, and design fit for purpose mitigating actions.

Underground gas storage (UGS) are essential components in energy security. However, UGS wells present a complicated and delicate combination of elements where ensuring safe and secure functionality over long periods is paramount. Today, with the advancement of continuous remote monitoring and digitalization, evaluating the integrity of UGS wells has become quicker and more efficient. This paper showcases how a digital twin is used to evaluate and forecast the link between leaks and temperature and pressure trends in a UGS well, allowing the identification and quantification of defects and, subsequently, well barrier integrity. UGS wells present additional criticalities with respect to normal production wells due to its longer life span and the repetitive production and injection cycles. This makes early and accurate leak detection essential for a safe management of the well barriers. The proposed digital twin has been developed using material and energy balances and considering each annulus as a separate control volume. Each control volume can exchange heat and mass through predesigned barriers. Simulating evolution in time of pressure and temperature in the control volumes., and comparing results with data from field, allows the identification of position and size of leaks. A genetic algorithm is applied to optimize placement of leaks on their specific barriers. The system aims to identify the position and dimension of possible leaks by matching historical pressure, temperature, and flow data. Once a leak is identified, a risk assessment is conducted to evaluate the overall integrity of the well. If the status of the well is found to be critical enough, an intervention may be planned. The system has been in use for little over a year and has shown great potential in accurate and efficient identification of leaks. This has accelerated the process of well integrity evaluation and allowed timely interventions on wells that required it. On the other hand, the process has highlighted cases where previous assumptions about leak location and size were corrected using the digital twin, therefore reducing the costs of interventions. Finally, the model showcased a clear readiness for predictive capabilities aimed to select, plan and design fit for purpose mitigating actions. This paper highlights the power that a digital twin can present leveraging field data with advanced algorithms. The paper also showcases workflows that allow convenient, efficient, and timely evaluation of well integrity, which leads to safer operating conditions and lower operational costs.