AI-driven Maintenance Support for Downhole Tools and Electronics Operated in Dynamic Drilling Environments

Lucas Kirschbaum, Darius Roman, Gulshan Singh, Jens Bruns, Valentin Robu, David Flynn

Research output: Contribution to journalArticle

1 Citation (Scopus)
54 Downloads (Pure)

Abstract

Downhole tools are complex electro-mechanical systems that perform critical functions in drilling operations. The electronics within these systems provide vital support, such as control, navigation and front-end data analysis from sensors. Due to the extremely challenging operating conditions, namely high pressure, temperature and vibrational forces, electronics can be subjected to complex failure modes and incur operational downtime. A novel Artificial Intelligence (AI)-driven Condition Based Maintenance (CBM) support system is presented, combining Bottom Hole Assembly (BHA) data with Big Data Analytics (BDA). The key objective of this system is to reduce maintenance costs along with an overall improvement of fleet reliability. As evidenced within the literature review, the application of AI methods to downhole tool maintenance is underrepresented in terms of oil and gas application. We review the BHA electronics failure modes and propose a methodology for BHA-Printed Component Board Assemblies (PCBA) CBM. We compare the results of a Random Forest Classifier (RFC) and a XGBoost Classifier trained on BHA electronics memory data cumulated during 208 missions over a 6 months period, achieving an accuracy of 90 % for predicting PCBA failure. These results are extended into a commercial analysis examining various scenarios of infield failure costs and fleet reliability levels. The findings of this paper demonstrate the value of the BHA-PCBA CBM framework by providing accurate prognosis of operational equipment health leading to reduced costs, minimised Non-Productive Time (NPT) and increased operational reliability.
Original languageEnglish
Pages (from-to)78683-78701
Number of pages19
JournalIEEE Access
Volume8
Early online date24 Apr 2020
DOIs
Publication statusPublished - 2020

Keywords

  • Bottom hole assembly
  • artificial intelligence
  • condition based maintenance
  • diagnostics
  • dynamic environments
  • failure modes
  • machine learning
  • oil drilling
  • printed component board assembly
  • prognostics

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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