Upstream Oil & Gas Process Efficiency Improvement by Using Machine Learning

The petroleum industry in Kuwait accounts for nearly half of the country’s GDP and makes up 15% of Japan’s crude oil import. In this collaborative research with Kuwait Oil Company (KOC), we aim for increasing the efficiency of the Exploration & Production (E&P) upstream processes in the oil and gas industry. We use data that has been collected by KOC in Kuwait from various sources such as SCADA systems, seismic sensors, and RFID sensors, and apply machine learning(ML) techniques for feature discovery, gas flow regimes identifications, and reservoir characterization. This research contributes to the E&P processes in reducing the exploration time and improving real-time well management by replacing the empiricalcorrelations with our ML-based predictive models in estimating the pressure-volume-temperature (PVT) properties of crude oils and natural gas. In our research, pattern recognition techniques are applied on big data to identify “liquid loading” at early stages in gas wells during production as an approach in extending the production time of gas wells.