(109a) Predictive Analytics Mega Implementation: Data Challenges and Successes

Asset Management is a critical aspect of Energy Industry operations, which requires advanced and integrated digital solutions to enhance efficiency, reliability and safety of the assets and processes. However, implementing such solutions is not a trivial task, as it involves many challenges such as data quality, management of change, long-term support models and value capturing tools. In this paper, we present a novel solution that leverages predictive analytics to optimize the performance and lifecycle costs of hundreds of rotating equipment in a large-scale corporate wide deployment. The solution is based on a robust and innovative integrated system that supports the facility to achieve its goals by minimizing asset failures and operational upsets proactively. the solution also aims to empower the operation , engineering and maintenance staff with timely and strategic support from various SMEs within the organization as well as foster collaboration among different disciplines through a set of advanced and automated decision support systems. The focus of this paper is to showcase the successes and challenge of implementing predictive analytics solution trough a large-scale deployment such as improving asset utilization, reliability and availability, integrity, safety, and sustainability which enhances operator decisions and overcome data-related challenges. The strength of this deployment is that diversity of assets that has tackled, predictive models for over 5 types of rotating equipment were studied, prepared and tested to check the accuracy of prediction based on FMEA studies. Moreover, the impact of data quality, data availability, and adoption management were studied after this implementation. In this submission the aim is to showcase the lessons learned and best practices from Aramco's journey in this specific large-scale advanced analytics implementation.