This paper presents the analytics of physics-driven big data in reservoir hydrodynamic simulation and parameter optimization for EOR projects in Daqing oilfield. An application model proposed in this study enables reservoir engineers to dynamically adjust parameters for numerical reservoir simulation. Huge amount of data are collected through Internet of Things (IOT) technology by installing large amounts of small scale and cost effective sensor devices in various systems of EOR projects, including injection system, injectors, production system, down hole pumps, surface gathering system, etc. Realtime data from various sensor sources are then integrated and normalized into the big data system. The big data system integrates and establishes the relationships between difference data source components, e.g., down hole pump, choke, separator, power plant, compressor, water treatment, export infrastructure within scope of EOR projects. Then the big data application model determines dynamic parameters used for the inputs of numerical reservoir simulation. Big data system integrates different data sources and is used to calculate production index, PVT parameters, pressure data, properties of oil and gas on a daily-sequence. Those parameters were then treated as the inputs of existing numerical reservoir simulation models. Hydrodynamics of EOR projects were predicted and the operation parameters on a field scale were adjusted based on the simulation results, such as well pattern and space, optimization of water injection parameters, choke size, chemical slug size, etc. Compared with the previous numerical reservoir simulation, the prediction error was reduced by more than 46% with the help of big data application model. Big data application model integrates different data source into an application model. It helps predict the reservoir dynamics with much more accuracy in the aspects of numerical reservoir simulation.
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