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

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

doi:10.1109/access.2020.2990152

Lucas Kirschbaum, Gulshan Singh + Show 4 more

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https://doi.org/10.1109/access.2020.2990152

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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.

Highlights

Despite a global trend of decarbonisation, oil and gas resources remain one of the central energy sources for the coming decades
This paper presents a thorough review of Big Data Analytics (BDA) health management strategies for downhole drilling tools
We identified one predominant failure cause as being the Small Outline Integrated Circuit (SOIC) on the Printed Component Board Assemblies (PCBA)

Summary

Introduction

Despite a global trend of decarbonisation, oil and gas resources remain one of the central energy sources for the coming decades. The global share of renewables is expected to increase significantly compared to the current energy mix in the coming decade, with an annual growth of 3 % to 6 % [1], [2]. Gas consumption is expected to reach 4000 BCM in 2035 and up to 5500 BCM in 2040 [2], [5] Based on these forecasts, the exploitation of existing oil and gas resources is not sufficient to meet the estimated demand. Multi trillion USD of investments into oil and gas infrastructure, and the exploration and development of existing resources and new reserves are required over the two decades [2], [6], [7]

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