Automated Fluid Management System (AFMS)
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Prosjektopplysninger
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- SkatteFUNN / Norges forskningsråd
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- SkatteFUNN
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- SkatteFUNN-prosjekt
- Kommune
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Offentlig prosjektsammendrag
BH are seeking to develop a novel system to automatically monitor real-time fluid (mud) parameters and perform predictive drilling fluid treatment, known as the Automated Fluid Management System. This will involve the collaboration of multiple partners, each delivering a specific piece of the overall solution. The investment in this project will enable the company to benefit from IO Level 3 operations directly by increasing the efficiency of chemical mixing, reducing reaction time to changes in fluid properties, and executing fluid plans in real-time. This functionality can be bundled into new services along with real time fluid sensors and automated additive dispensing system such as NOV DORS, MHWirth mudView and Equinor Wels logistics system, to provide an Autonomous Fluid Management System. The technology being developed will be qualified partly in lab and partly on yard (Mongstad base) before testing on a drilling rig. Baker Hughes has identified a roadmap to expedite the development of a Digital Ecosystem in the offshore environment to include an Automatic Fluid Advisory software in the Beacon centre that would allow for: - Recommended instructions for communication to rig on fluid maintenance/additions - Reduced POB - reduction/removal of personnel needed to monitor / maintain a drilling fluid system in preparation for future elimination of fluids personnel onboard - Increased robustness of sensors & autonomous remote fluids operations service needed to promote reduced POB -Lower operational cost The vision of the project is to build a system which will improve operational efficiency and allow BH to secure cost efficiencies that act to bring down operational cost and service risk. BH believes there will be significant value improvements in the development and execution of mud treatment technology by developing smarter, better-connected systems that use big data, machine-to-machine communication intelligent data analytics technology to optimize productivity
Kilde og proveniens
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