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Reservoir Parameter Inversion of CO2 Geological Sequestration Based on the Self-adaptive Hybird Genetic Algorithm
Yanjun Hao, Dinghun Yang, Yuanfeng Cheng   2016/12    

Carbon dioxide geological sequestration is an important approach to reduce carbon emission and to mitigate global warming. An important part of CO2 sequestration is seismic monitoring, which is to monitor carbon dioxide distribution variation using the seismic method after sequestration. To achieve this goal, we need to establish the relationship between reservoir fluid saturation through inversion of seismic monitoring data. First, based on the Biot model and multiphase model, we investigate the effects if several physical parameters (porosity, CO2 saturation, temperature and pressure, et al.) on seismic properties such as wave velocity and attenuation of carbon dioxide and water saturated porous media to obtain the regular understanding. The results show that porosity and CO2 saturation have a huge impact on the properties of wave dispersion and attenuation, and temperature and pressure can affect the rock velocity through the properties of porous fluids. Next, based on Biot theory with multiphase flow, we apply the self-adaptive hybrid genetic algorithm, which has stronger anti-interference capacity and better ability of local search and anti-interference, to perform inversion of actual data. The inversion of core experimental data indicates the validity of the algorithm, and shows that Biot theory with multiphase flow can explain the wave velocity characters of CO2 and water saturated rock. At last, we apply the self-adaptive hybrid genetic algorithm to the seismic monitoring data of actual sequestration project, and obtain the CO2 saturation distribution at different periods after sequestration and achieve the purpose of using the seismic method to monitor carbon dioxide distribution. 

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