Real Time Decision Support in Drilling Operations Using Bayesian Decision Networks

2009 ◽  
Author(s):  
Mohamad Ali Rajaieyamchee ◽  
Reidar Brumer Bratvold
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Bayesian Decision ◽  
Decision Networks ◽  
Drilling Operations ◽  
Bayesian Decision Networks

Drilling Advisory for Automatic Drilling Control

2021 ◽  
Author(s):  
Kenza Lahlou ◽  
Sven Inge Oedegaard ◽  
Morten Svendsen ◽  
Tore Weltzin ◽  
Knut Steinar Bjørkevoll ◽  
...  
Keyword(s):  
Decision Support ◽  
Control Systems ◽  
Real Time ◽  
Rotation Speed ◽  
3D Visualization ◽  
Running Speed ◽  
Optimal Flow ◽  
Drilling Operations ◽  
Back Office ◽  
Forward Looking

Abstract This paper describes a system being developed for providing an optimized real-time decision support with automatic forward-looking and what-if simulations. It will address the challenge of achieving automation, better performance, and avoidance of non-productive time (NPT) in drilling operations. It will additionally address the demanding human support currently required in the entire decision support workflow. The approach includes utilization of Model based reasoning in Artificial Intelligence (AI) with a Digital Twin combined with Machine Learning (ML) and advanced 3D visualization which is a key enabler for operation alerts and optimization. Multiple forward-looking and what-if simulations will also be run in real-time to find optimal parameters for flow, rotation and running speed. A Diagnostic module will detect abnormalities and trigger safeguards. Auto-configuration and auto-calibration will be the key elements for Drilling Advisory system and deployment without the need for back-office support. The personnel involved in the operation (drilling contractor, service provider and operator) will be able to quickly provide the necessary operational input and then the system will be auto-calibrated during the operation. Results will be an Advisory Tool providing the operation with an optimal flow, rotation speed and running speed during Drilling, Tripping, Casing/liner/screen running and cement operations in two applications areas: In front of the driller as an Advisory tool for rigs with legacy drilling control systems not capable of receiving automated instructions. Base for providing direct commands and safeguards to rigs with control systems capable of receiving automated commands of optimal flow, rotation speed and running speed.

scholarly journals Quantum JIDOKA. Integration of Quantum Simulation on a CNC Machine for In–Process Control Visualization

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5031
Author(s):  
Javier Villalba-Diez ◽  
Miguel Gutierrez ◽  
Mercedes Grijalvo Martín ◽  
Tomas Sterkenburgh ◽  
Juan Carlos Losada ◽  
...  
Keyword(s):  
Real Time ◽  
Sensor Network ◽  
Industry 4.0 ◽  
Numerical Control ◽  
Manufacturing Processes ◽  
Bayesian Decision ◽  
Cnc Machine ◽  
Digital Twin ◽  
Decision Networks ◽  
Computational Performance

With the advent of the Industry 4.0 paradigm, the possibilities of controlling manufacturing processes through the information provided by a network of sensors connected to work centers have expanded. Real-time monitoring of each parameter makes it possible to determine whether the values yielded by the corresponding sensor are in their normal operating range. In the interplay of the multitude of parameters, deterministic analysis quickly becomes intractable and one enters the realm of “uncertain knowledge”. Bayesian decision networks are a recognized tool to control the effects of conditional probabilities in such systems. However, determining whether a manufacturing process is out of range requires significant computation time for a decision network, thus delaying the triggering of a malfunction alarm. From its origins, JIDOKA was conceived as a means to provide mechanisms to facilitate real-time identification of malfunctions in any step of the process, so that the production line could be stopped, the cause of the disruption identified for resolution, and ultimately the number of defective parts minimized. Our hypothesis is that we can model the internal sensor network of a computer numerical control (CNC) machine with quantum simulations that show better performance than classical models based on decision networks. We show a successful test of our hypothesis by implementing a quantum digital twin that allows for the integration of quantum computing and Industry 4.0. This quantum digital twin simulates the intricate sensor network within a machine and permits, due to its high computational performance, to apply JIDOKA in real time within manufacturing processes.

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