Upstream Oil and Gas (O&G) companies that operate brownfield assets with aging surface facilities experience challenges in managing their base-business operations.  Companies must manage the increasing challenges of maintaining equipment reliability, integrity, and operational safety. The high quality and timely decision to take necessary action for the operation and maintenance of the aging facilities becomes very critical. The support from the comprehensive analytics approach to drive the decision-making capability also becomes very important.  The Digitalization and the adoption of Business Analytics (BA), such as Digital Twin, Computerized Maintenance Management System (CMMS), Big Data (BD), Artificial Intelligent (AI), Machine Learning (ML), Deep Learning (DL), Decision Support Centre (DSC), become the option which is commonly growing in line with the increase of the investment in the BA adoption. There is a need to have a thorough evaluation or lookback to review whether those efforts on BA adoption have produced the promised solution, and returned the benefits of the investment that has been allocated. This paper is a research proposal that provides the introduction and background, the state-of-the-art and research gap identification, and the proposed methodology for the operationalization of the research. The research background and objectives are described, many previous studies are summarized and compared and finally, the methodology is proposed.