Increasing Oil and Gas Recovery Through Optimal Flow Measurement
Time: 12:45 – 13:30, July 4th
Mahdi is a second-year PhD student at the Centre for Flow Measurement and Fluid Mechanics at Coventry University, UK. My research title is "Increasing Oil and Gas Recovery Through Optimal Flow Measurement" and I am supervised by Dr Seyed Shariatipour and Prof Andrew Hunt From Coventry University and prof Manus Henry from Oxford University. Presently, my focus is on effects of flow measurement errors on history matching. I have done both my bachelors and masters in petroleum engineering and has previously been a member of Enhanced Oil Recovery Research Centre at Shiraz University, Iran for 4 years. Back in Iran, my research focus was on compositional modelling of gas lifting, reservoir simulation, and EOR.
History matching is the process of modifying a reservoir model using observed data. In the oil and gas industry, production data is employed during history matching to reduce the uncertainty in reservoir models. However, production data, which is normally measured by flowmeters or allocated to the wells using mathematical equations, inevitably has inherent errors. In other words, the data which is used to reduce the uncertainty of the model has some uncertainty in itself. The impact of this uncertainty in the production data on history matching has not been addressed in the literature so far. In this research, the effects of systematic and random errors in observed data on history matching is investigated. The results show although random errors don't have a significant effect on history matching, systematic errors considerably affect the model parameters and future production forecasts. Despite random errors that are unavoidable and can just be reduced by using new flow meter technologies, systematic errors can be eliminated by careful calibration and maintenance of flow meters. Therefore, the results suggest that in case of history matching, investing in calibration and maintenance of current flow meters is more important than paying for new more precise technologies.