Assessing the Potential of Advanced Sensor Technologies and IoT in Predicting Downhole Drilling Issues

Abstract Fast detection of downhole drilling problems and drilling fluid property changes remains one of the critical challenges requiring further attention from the drilling industry. One potential solution to this issue might be the use of advanced sensor technologies installed at the bell nipple and the utilization of the Internet of Things (IoT) as a gateway to transfer data between the rig site and the Real-Time Drilling Operations Centre (RT-DOC). In this context, this paper discusses the development of a large-scale laboratory prototype that mimics the bell nipple in field applications and presents the results of experiments conducted to evaluate the potential of the sensors installed on the prototype to detect downhole problems. Four sensors have been integrated into the prototype: an in-line density sensor, an in-line viscosity sensor, a non-intrusive level sensor, and a Doppler ultrasonic velocity sensor. Real-time data recorded by these sensors were collected and transmitted via the cloud to a data analytical station. Multiple experiments were conducted using the developed prototype. The main aims of these experiments are to assess the accuracy of the sensors, evaluate the response time of data transmission, evaluate the sensors response to rapid changes, and their ability to detect downhole problems, particularly kicks and losses. The main findings of the work can be summarized as follows: The five-layer system developed for data acquisition and analysis, leveraging IoT technology, exhibited seamless functionality in continuously and autonomously collecting sensor data, processing it, and transmitting it to a real-time monitoring and analysis framework without human intervention. Furthermore, integrating fluid level measurements alongside complementary indicators such as delta flow demonstrated promise in effectively detecting kick and loss symptoms. The evaluated in-line density and viscosity sensors displayed exceptional accuracy and responsiveness, enabling real-time monitoring of fluid properties and facilitating the prompt detection of rapid changes. This capability offers significant advantages to rig-side mud engineers, allowing for swift identification of variations compared to the traditional hourly measurement approach reliant on marsh funnel viscosity and mud balance. While the ultrasonic velocity sensor exhibited satisfactory accuracy, its utility in detecting loss and kick events cannot be fully realized using the delta flow method. However, better results in detecting the loss and kick events are achieved by using advanced incident classification algorithms.