A study of health management of LWD tool based on data-driven and model-driven

• Autorzy: Li Hui , He Zi Hua , Zhang Yu ting , Feng Jin , Jian Zun Yi , Jiang Yi Bo

Identyfikatory

DOI

10.1007/s11600-022-00755-1

• Języki publikacji: EN

Abstrakty

EN

Electromagnetic wave logging-while-drilling (LWD) tool plays an important role in unconventional oil and gas exploitation and deep-sea oil and gas resource exploration process. The reliability such as reliable life and durability of the tool can control drilling efficiency and production cost in extreme environmental conditions. In this paper, main faults of the electromagnetic wave LWD tool have been analyzed when it working to the drilling site. Failure time of antenna coils, circuit boards, and power supply have been recorded. Therefore, failure mode and failure mechanism can be analyzed of the tool. Secondly, a fault analysis model of electromagnetic wave LWD tool based on Weibull distribution model has been built up, and by using this fault analysis model the reliable life and the remaining useful life of antenna system can be calculated. The last, the goodness-of-ft test can be operated to Weibull distribution model by using Kolmogorov–Smirnov test. Study results show that the reliability and the law of fault occurrence of electromagnetic wave LWD tool can be directly reflected. And it has practical significance to reliability evaluation of the instrument system and joint optimization of safe operation and maintenance of the tool.

Słowa kluczowe

EN

reliability; failure mode; Weibull distribution; remaining useful life; safe operation

PL

niezawodność; Tryb awaryjny; dystrybucja Weibulla; pozostały okres użytkowania; bezpieczna eksploatacja

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2022
• Tom: Vol. 70, no 2
• Strony: 669--676
• Opis fizyczny: Bibliogr. 14 poz.

Twórcy

autor

Li Hui

• School of Photoelectric Engineering, Changzhou Institute of Technology, Changzhou 213032, China

autor

He Zi Hua

• School of Photoelectric Engineering, Changzhou Institute of Technology, Changzhou 213032, China

autor

Zhang Yu ting

• School of Photoelectric Engineering, Changzhou Institute of Technology, Changzhou 213032, China

autor

Feng Jin

• School of Photoelectric Engineering, Changzhou Institute of Technology, Changzhou 213032, China

autor

Jian Zun Yi

• School of Photoelectric Engineering, Changzhou Institute of Technology, Changzhou 213032, China

autor

Jiang Yi Bo

• School of Photoelectric Engineering, Changzhou Institute of Technology, Changzhou 213032, China

Bibliografia

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• 6. Kim J (2020) Implementation of a goodness-of-fit test through Khmaladze martingale transformation. Comput Stat 35:993–2017
• 7. Kovalev MS, Utkin LV (2020) A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds. Neural Netw 132:1–18
• 8. Li H, Yan ZD, Liu CB, Jiang YB (2019) Numerical simulation of azimuthal resistivity LWD instrument responses. J China Univ Petrol Edit Nat Sci 43(1):42–52
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• 10. Tsui KL, Zhao Y, Wang D (2019) Big data opportunities: system health monitoring and management. IEEE Access 7:68853–68867
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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).