Data Science Approaches to Asset Integrity Management in Offshore and Onshore Oil and Gas Operations

Abstract

Asset integrity management is important in addressing safety, reliability and economic
sustainability of offshore and onshore oil and gas operations and especially in the ageing
infrastructure and in more complex operating environments. The increasing access to large
volumes of high-velocity, high-volume operational and inspection data has put data science in a
transformative enabling role of integrity assurance throughout the asset lifecycle. This paper
considers the use of data science methods in asset integrity management and how sophisticated
analytics, machine learning, and statistical modeling methods can be used to detect abnormalities,
anticipate degradation and failure, and assist in risk-based decision-making. Significant data
sources such as sensor measurements, inspection history, maintenance history, and operation
history are examined in terms of their contribution to the predictive maintenance and risk-based
inspection models. The paper also explains the implementation issues that are unique to offshore
and onshore, citing the differences in exposure to the environment, accessibility, and reliability of
the data. Combining information-based insights with the current system of program management,
operators will have an opportunity to optimize proactive maintenance initiatives, minimize
unplanned downtime, and increase the overall asset efficiency. The results reinforce the
importance of data science in the strategy of resilient, efficient, and safety-driven asset integrity
management in the oil and gas industry.