Lucian Toader
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Paulinus Abhyudaya Bimastianto
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Shreepad Purushottam Khambete
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Suhail Mohammed Al Ameri
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Erwan Couzigou
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...
Abstract
In a drive to enhance drilling operational awareness, the Real-Time Operations Center (RTOC) has developed a State-of-the-Art event detection algorithm that consistently highlights the deviations of critical parameters by actively comparing real-time values against comprehensive physical models and alerting the users through a dashboard. The process relies on different levels of frequency and severity in order to detect events at their onset and prevent developing into a situation that compromises the operations.
The first pillar of the solution consists of deterministic modelling of the expected values for a series of parameters in order to provide the basis for comparison and diagnostics. The main parameters sought to be modelled consist of the Standpipe Pressure, the Rotary Torque and the Hook load, which respectively are generated through individual methods taking into consideration actual conditions as well as relevant contextual data to ensure accuracy. The second pillar of the solution consists of visual alerts, triggered and displayed on a dashboard based on frequency and severity levels, as percentage of deviation from accepted operational envelope.
The solution has been initially implemented during drilling operations where different issues were expected to take place, finding that whenever such occurrences took place, the algorithms were able to signal potential events in most of the cases. Some challenges were observed mainly due to sensor calibration and behavior since the expected model values not necessarily match reality, including residual pressure when the pumps are off or when the string is set on slips but the hook load values still present some variance. Also, it has been observed during transient periods where flow and rotation are changed drastically, that the stabilization to a steady state present with high variance, which has demanded the introduction of further logics within the algorithms to account for these effects and avoid the generation of false indications of issues. The solution has given encouraging results thus far in signaling different dysfunctions on the drilling process without the need of immediate human interpretation of data, which has allowed to move forward in the digitalization of operations, not only by timely signaling the onset of issues, but as well by providing the basis to further develop real time diagnosis of the problems to accelerate their resolution.
The conception of the event detection based on deterministic real time analysis of individual channels against robust physical models from the existing digital twin solution has proven an immediate asset for operations on its own. By providing clear signaling of issues, while providing a solid framework to ultimately develop a diagnostic solution to translate a potential event into a proactive approach to support decision making process.
A Real-Time Early Warning Seismic Event Detection Algorithm Using Smart Geo-Spatial Bi-Axial Inclinometer Nodes for Industry 4.0 Applications
