Application Of Pre-stack AVO Inversion Technique In Glutenite Reservoir Prediction

Glutenite reservoirs have become the focus of exploration in Dongying depression, the proven glutenite reservoirs in this study area have big reserves, big accumulation of scale and enormous exploration potential. It made the research become more difficult because of the special deposition law and accumulation environment. Thus finding the suitable inversion method and reservoir prediction method have great significance for the region’s reservoirs. This thesis took the glutenite reservoirs as the research focus, did the following research based on the reservoir characteristics.The optimization stack was used because of the poor seismic data quality characteristics of glutenite reservoirs. Unlike the conventional stack that weighting coefficient is 1 in each trace, the optimization stack considers more factors such as the amplitude, the frequency, the angle of incidence and the offset, each parameter is calculated according to their formula, then calculating the comprehensive optimization stack parameters. A higher signal to noise ratio, good quality seismic data was obtained by this approach, and it was convenient for the next inversion.Conventional post-stack inversion can’t achieve the desired results because of the complex fluid nature and difficulties in prediction of effective reservoir. However, the pre-stack seismic data contain different offset information, it can reflect the reservoir characteristics more accurately, so the pre-stack AVO inversion can predict the reservoir more effectively. Rock physical analysis was used to get the missing well logs and correcting the well logs. After obtaining a high-quality seismic data and logging data, various physical parameters of the reservoir can be obtained based on AVO forward modeling and inversion techniques. And then do effective reservoir prediction and comprehensive analysis using the physical parameters. Utilization of seismic rock physical analysis and pre-stack AVO technology, it can greatly reduce the multiple solutions for lithology identification and hydrocarbon detection, effectively improve the accuracy of hydrocarbon detection.First of all, I processed the seismic data of the studied area and obtained high-quality seismic data by optimization stack techniques, then recognized sedimentary cycles of glutenite bodies. Then did well-to-seismic calibration and structural interpretation by using optimization stack data. Then obtained spatial distribution characteristics of glutenite combined with post-stack seismic attributes analysis techniques. On this basis, to estimate shear velocity log data, rock physical analysis and a series of processing, then obtained multiple elastic parameter data from partial stack angle gathers and AVO forward and AVO inversion. Predicting effective reservoir combined with the spatial distribution characteristics of glutenite. Ultimately developed the reservoir inversion and reservoir prediction methods that is applied to the glutenite reservoirs.