Application Of Subtle Interpretation Technique Of High-precision 3-D Seismic Data In Secondary Development: A Case From Qiuling Oilfield

Qiuling oilfield is located in Taibei depression, Tulufan-Hami basin, where structure is cut into two blocks by north-south faults and where Qiketai group, Sanjianfang group and Xishanyao group of the Middle Jurassic are the source formations. In 2005, the integrated high-precision 3D seismic project including data acquisition, processing and interpretation was implemented in Tuha oilfield, which was to resolve the problems found in oil-field development. Based on high-precision 3D seismic data, combing with borehole data, logging data and development dynamic information, we subtly interpreted the small faults and predicted reservoirs.Materially, the problems facing to oil-filed development can be taken that the ability to recognize small faults and the precision of reservoir prediction are not meet the requirement in oil-field development. In the paper, small faults which displacement is less than 10m can be identified by forward modeling and spectral decomposition. Based on high-precision 3D seismic data, technique framework of seismic data interpretation which is suitable for geological conditions like Qiuling oil-filed was constructed.The old research results show there are two sets of faults. According to structure family theory and geologic mechanics theory, the new fault system is classified as three structure families in the research area, which makes fault combination, cutting relationship and distribution in plane clearly, and is according with geologic stress circumstance and geologic rules of Qingling structure belt.Wavelet analysis has good local time-frequency character and neural network has allowing-error ability and auto-learning ability, so wavelet neural network method is selected to perform seismic constrained inversion. Before seismic constrained inversion, it is necessary that well data, logging data and 3D seismic data in the research area are neatened. After selecting optimum constrained condition, we give the random initial value of each parameter of wavelet neural network, and continuously adjust network parameters until the completion of network training. Comparing the inversion results and the log curves which are not used as constrained condition, it can be found that the inversion results of resistivity curve used as constrained condition is clearly, and there is a good corresponding between high impedance in inversion result and abnormity in spontaneous potential curve when. In our research, FSVM is used to reservoir prediction.After analyzing the characteristics of lithology, sedimentary and petrophysics of the targets in Qiuling oil-field, the attributes of targets are extracted by the means of single or multi-channel time-window. Learning samples is composed of these attributes. After training FSVM, the results is used to predict the reservoir. Random comparing prediction results with drilling data, prediction errors are within the allowable range. The research result shows that there are potential of secondary development in Lower Jurassic, Triassic and Permian the structures are reliable,and the source is abundant.