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《Acta Petrolei Sinica》 2013-02
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Prediction of the remaining oil distribution using multi-component seismic full waveform elastic inversion

ZHAO Bangliu1 SHI Yumei2 YAO Fengchang2 NIU Yanliang3 JIANG Yan3 CHEN Zhide3 WANG Guishui3 MA Xiaoyu2(1.PetroChina Exploration & Production Company,Beijing 100007,China;2.PetroChina Research Institute of Petroleum Exploration & Development,Beijing 100083,China;3.Research Institute of Petroleum Exploration & Development,Daqing Oilfield Company Ltd.,Daqing 163712,China)  
Although main reservoirs in old oilfields of eastern China have high water cut after decades of production,there is a considerable portion of remaining crude oils being left in the reservoirs.To identify the remaining oil distribution in these reservoirs is one of the keys problems for optimizing development programs and enhancing oil recovery.Based on previous studies on physical properties of crude oils and formation water from oilfields in eastern China,the influence of viscosity fluid on shear moduli of saturated rocks and their variation laws were discussed in detail.A method based on the Krief model and generalized Gassmann equation was proposed to extract the fluid shear modulus from shear moduli of saturated rocks(also known as the effective shear modulus) and then to identify oils and water.The effective modulus was obtained from multi-component seismic data by using full waveform inversion based on the elastic wave equation that naturally takes the complex behavior of propagating waves into account and can be used to make accurate estimates of formation elastic parameters.The method was applied to field data from the Daqing oilfield in eastern China.The verification drilling confirmed that the distribution of remaining oils can be predicted effectively on condition that the full waveform elastic inversion of multi-component seismic data is integrated with the fluid shear modulus when distinguishing crude oils from formation water.
【Fund】: 国家基础研究发展计划(973)项目(2007CB209500);; 中国石油天然气集团公司基础研究项目(2008A-1803)资助
【CateGory Index】: P631.4;P618.13
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