Coal structure quantitative prediction with sensitive-attribute and parameter-inversion fusion
FENG Xiaoying1, YANG Yanhui1,2, ZUO Yinqing1,2, DING Ruixia1, HAN Sheng1, QIN Chen3
1. Exploration and Development Research Institute, Huabei Oilfield Company, PetroChina, Renqiu, Hebei 062552, China; 2. Exploitation Pilot Base for Coalbed Methane, CNPC, Renqiu, Hebei 062552, China; 3. No.2 Oil Production, Daqing Oilfield Company, PetroChina, Daqing, Heilongjiang 163414, China
Abstract:Coal structures are closely related to the gas production of coal reservoirs,so the coal structure quantitative prediction is essential.We analyze seismic sensitive attributes and inversion sensitive parameters on a 3D seismic dataset in Coal 3#,Mabi,Qinshui Basin.It is found that the most sensitive attribute is the texture,and the most sensitive is resistivity of logging data.The texture attribute change is relatively smooth in the horizontal direction,and makes faults clear,but its vertical resolution is low.On the other hand,the vertical resolution for inversed resistivity is high,and the inversed data in well positions is consistent with logging curves,but its horizontal change is not smooth,and fault identification is impossible.With the texture-attribute and inversed-resistivity fusion,the coal structure quantitative prediction is achieved,which is proved by the late well drilling.Our practice and experience demonstrate the validity of the proposed approach and may provide a useful reference for similar conditions.
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