As drilling of new oil and gas wells increase to meet energy demands, it is essential to optimize processes to ensure the health and safety of the crew as well as the protection of the environment. Drilling operations represent a dynamic and challenging environment with natural and mechanical factors that need to be closely managed. Well control refers to the technique employed while drilling for balancing the hydrostatic and formation pressures to prevent the influx of water, gas, or hydrocarbons that would ultimately result in an uncontrolled flow to the surface. In the event of a well control incident, the crew must take proper and prompt actions to mitigate the risks and shut-in the well. In this study, we introduce the Well Control Space Out technology, an internet-of-things (IoT) environment that couples cameras and an edge server to implement stateof-the-art deep-learning models for the real-time processing of video images recording the drillstring. The computational models automatically perform object detection to keep track of key drilling rig components. The results from the video analysis are displayed on a dashboard describing the state and steps to follow in a well control incident without the need for any time-consuming, manual calculations. The internet-of-things edge foundation laid in drilling can be seamlessly expanded to other upstream sectors, where time-sensitive, critical decisions can be made in real-time, in the field, closer to operations. Finally, this technology can be seamlessly integrated with the current technologies to develop an automated closed-loop control system.INDEX TERMS Automation, deep-learning, computer vision, edge computing, internet-of-things, oil and gas drilling, well control.
A marketplace for the Internet of Things acts as the corner stone of an IoT ecosystem, by matching the offer (i.e., data or functionalities) with the demand coming from IoT applications (e.g. analytics). In this paper, we present the semantic matching implemented on the public BIG IoT marketplace. Pre-print version.Many Internet of Things (IoT) platforms have come up and provide data and functionalities of things, e.g., ThingWorx, Xively or Siemens Mind-Sphere. In order to enable a vibrant and collaborative IoT ecosystem across these platforms, marketplaces are needed to enable providers to monetize the access to their platforms by consumers (e.g., applications or services). The BIG IoT project [1] offers such a marketplace and enables providers to register their IoT resources as offerings and consumers to formulate queries to discover these offerings. Once offerings and queries are registered on the BIG IoT marketplace, it is crucial to effectively support the matching between offerings and queries, so that consumers are reported which offerings suit their needs in near real-time.
This study focuses on the design and infrastructure development of Internet-of-Things (IoT) edge platforms on drilling rigs and the testing of pilot IoT-Edge Computer Vision Systems (ECVS) for the optimization of drilling processes. The pilot technology presented in this study, Well Control Space Out System (WC-SOS), reduces the risks associated with hydrocarbon release during drilling by significantly increasing the success and time response for shut-in a well. Current shut-in methods that require manual steps are prone to errors and may take minutes to perform, which is enough time for an irreversible escalation in the well control incident. Consequently, the WC-SOS enables the drilling rig crew to shut-in a well in seconds. The IoT-ECVS deployed for the WC-SOS can be seamlessly expanded to analyze drillstring dynamics and drilling fluid cuttings/solids/flow analysis at the shale shakers in real-time. When IoT-ECVSs communicate with each other, their value is multiplied, which makes interoperability essential for maximizing benefits in drilling operations.
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