The significance of research on seismic attributes and the effects of seismic attributes in oil exploration and exploitation are self-evident now. This technology becomes the bridge of geology, exploration and exploitation nowadays, and plays an important role in every aspect in oil industry. The meaning of this dissertation lies in, tracing the advanced technology in seismic multi-attribute analysis at home and abroad, enhancing the preciseness and efficiency of seismic attributes for reservoir prediction, then giving full scope to guidance in practice.The study thought of this dissertation if follows, firstly, grasping the present status and developing trend of the study and application of this technology at home and abroad, carding and synthesizing the basic knowledge of seismic attribute in detail, secondly, selecting the Dainan information of Majiazui Area of Gaoyou Structure in the south of Jiangsu Oilfield as the target survey, collecting the data of geologic, seismic, well logging, and oil test, establishing the forward model to carry out fluid replacement, studying the rock physics and the seismic attributes'characteristics of thin bed among different lithology and different fluids. Analysis more than thirty seismic attributes and carry on geophysical inversion with forward modeling results and drilling data, at the same time, finding out six fault-lithology traps with oil by seismic attribute analysis,finally, developing a software raise working efficiency of oil prediction by seismic multi-attribute analysis and getting a satisfied result.The innovative points of this dissertation are as follows:1 Primarily established technical thought and working process of reservoir prediction method by means of studying the geologic feature and petro-physics feature with forward model and inversion; a suitable time-window can raise the prediction precision remarkably; the strength of diffraction wave can use to distinguish oil, gas and water as an important seismic attribute.2 Some seismic attributes such as RMS amplitude, instantaneous frequency, instantaneous phase, instantaneous amplitude and spectrum decomposition are better to describe the distribution of sand and oil than others in target survey, and restrain with seismic attributes can reduce the multiple solution of inversion. According to this, we are finding out six favorite traps.3 Enhancing the precise of neural network implicit function by auto-adjust study ratio back propagation algorithm, which is improving the learning speed. We develop a software use the algorithm to predict reservoir and have a remarkable result.4 Draw the sedimentary facies of the target survey with well logging data and seismic data. It is mainly delta facies, lake facies and underwater fan facies in the target survey. Traps, which are predicted by seismic multi-attributes analysis, lie on the favorable sedimentary facies with oil and gas production, solute the explore problem. |