Prognostics health management for a directional drilling system

Zhan, Shiping; Rodiek, Jens; Heuermann-Kuehn, Ludger Ewald; Baumann, Joerg

doi:10.1109/phm.2011.5939543

Cited by 8 publications

(6 citation statements)

References 6 publications

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“…In this context a case study conducted by Reckmann et al [11] identifies lateral vibrations to be the principal factor contributing to the PCBA degradation, accounting for up to 29 % of vibration related MWD tool failures. Various other component and packaging related failures are documented in [12].…”

Section: B Electronics Failure Modesmentioning

confidence: 99%

“…Reckmann et al [86] discusses the importance of high quality/high frequency data for detection of tool failures and a complementary tool database capturing the tool history from various data sources. In [12] and [87] case studies of the challenges the oil and gas industry is facing in centralizing and streamlining drilling data are listed.…”

Section: Bda: An Oil and Gas Industry Reviewmentioning

confidence: 99%

“…Failure of drill pipes often results in long downtime as recovery is difficult. Based on the RUL prediction of a Recursive Neural Network, maintenance scheduling can be improved, and subsequently NPT reduced Zhan et al [12] developed a model for anomaly detection for RSS modules using a Nonparametric Fuzzy Inference system, based on field data, such as RPM, electrical current of the RSS module, and the pump pressure in order to improve maintenance decision making. Using failure data from BHA-PCBA, Zhan and Zhao [29] elaborate a method for the development of a cumulative stress model for RUL forecasting and maintenance decision making based on a hybrid approach, combining data driven methods and physics of failure Zhan and Ahmad [90] discuss a retrofittable health management solution for real-time BHA-PCBA prognostics for downhole tools.…”

Section: Bda: An Oil and Gas Industry Reviewmentioning

confidence: 99%

“…Within complex drill strings and downhole tools, the ambient conditions lead to component or sub-system failures which in turn lead to Non-Productive Time (NPT), threatening the economic viability of drilling operations. The proportion of drilling costs of the total well costs are considerable [10], with even higher costs offshore [11]; NPT can cost up to $1m per day [12]. 15 % of total annual drilling costs are due to NPT caused by downhole tool failure -more precisely failed subcomponents of the BHA [13].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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

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Section: B Electronics Failure Modesmentioning

confidence: 99%

Section: Bda: An Oil and Gas Industry Reviewmentioning

confidence: 99%

Section: Bda: An Oil and Gas Industry Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…Zhan and others [31] in their work used a nonparametric system of fuzzy inferences to predict the state of the rotary steerable system (RSS) by forecasting the state of the RSS is in real time based on the operating mode and drilling parameters. This method is allows reducing the cost of repair and maintenance of the drilling equipment.…”

Section: Introductionmentioning

confidence: 99%

Risk Factors Evaluation for Monitoring of Well Drilling

Islamov¹,

Grigoriev²,

Белоглазов³

et al. 2021

Preprint

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Drilling of wells for oil and gas production is a highly complex and expensive part of reservoir development. Thus, together with injury prevention, there is a goal to save cost expenditures on downtime and repair of drilling equipment. Nowadays companies have begun to look for ways to improve the efficiency of drilling and minimize non-production time with the help of new technologies. To support decisions in a narrow time frame, it is valuable to have an early warning system. Such a decision support system will help an engineer to intervene in the drilling process and prevent high expenses of unproductive time and equipment repair due to a problem. This work is describing a comparison of machine learning algorithms for anomaly detection during well drilling. Tested models classify drilling problems based on historical data from previously drilled wells. To validate anomaly detection algorithms, we use historical logs of drilling problems for 67 wells at a large brownfield in Siberia, Russia. Wells with problems were selected and analyzed. It should be noted that out of the 67 wells, 20 wells were drilled without expenses for unproductive time. Experiential results illustrated that a model based on gradient boosting can classify the complications in the drilling process best of all.

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