Digitalization Using IIoT and Cloud Technology in Oil and Gas Upstream-Merits and Challenges

Dange, Aarti (Middlesex University, UAE) | Ranjan, Pallavi (Middlesex University, UAE) | Mesbah, Hossam (Ajal Group, Kuwait) | Chokshi, Rajan (Accutant Solutions, LLC)

OnePetro 

Abstract

This paper discusses the application of IIoT in various areas of oil and gas upstream. It elaborates on the drivers of IIoT, presents the advantages and benefits and describes the challenges faced as of today in the implementation. IIoT and cloud computing work hand in hand. IIoT generates huge amount of data and cloud computing provides a pathway to present this data is a useful way and travel to the end user. A detail evaluation of the investment in using this technology and its anticipated returns are demonstrated. IIoT is believed to be an emerging solution for oil and gas complexities. The main drivers behind this technology are data storage, data analytics, reliability improvement and materiality assessment and control. The application of IIoT in areas of artificial lift optimization, Supply chain in real time, cyclic steam stimulation and flow assurance is described. This technology provides real time solution for dynacards interpretation and analysis for Sucker rod pumps, operating point analysis for Electrical submersible pumps and predicted cumulative production for all artificial lift optimization; efficient planning and waste elimination for supply chain and logistics; real time steam quality and quantity check for CSS and a complete digital approach to reservoir management and flow assurance. The main benefits of this technology are reduced MTBF, high efficiency, improved HSE standards, Instantaneous control over production loss, collaborative decisions leading to fast turnaround, highly responsive supply chain and enhancing environmental footprint. This has helped substantially in real time management of wells by exception and alerts in form of intelligent alarms indicating any deviation in the expected behaviour. This has significantly brought down the non-productive time (NPT). However, this paradigm shift comes with a substantial cost. The technical challenges include the data security, protocol non-uniformity, possible data loss and limitations of redundant system.