整合多尺度地球物理数据建立高分辨率储层模型的方法及应用

Most of the fields in China are in the middle-late development phase or are mature fields. It becomes more and more difficult to develop the remaining oil/gas. Therefore, it is import to enhance oil/gas recovery in order to maintain the production. Fine scale modeling is a key to improve the recovery. Incorporation of geological, seismic and well log data to 3D earth modeling is essential to build such models. In Ken71 field, well log, cross-well seismic and 3D seismic data are available. A key issue is to build 3D earth model with these multi-scales data for oil field development.In this dissertation, studies on sequential Gaussian-Bayesian simulation have been conducted. Its comparison with cokriging and sequential Gaussian simulation has been performed. The realizations generated by sequential Gaussian-Bayesian simulation have higher vertical resolution than those generated by other methods. Less differences between these realization and true case are observed. With field data, it is proved that incorporating well log, cross-well seismic and 3D seismic into 3D fine scale model is reliable. In addition, the advantages of sequential Gaussian-Bayesian simulation and conditions for input data are demonstrated. In Ken71 field, the impedance difference between sandstone and shale is small. It would be difficult to identify sandstone in the reservoir with traditional impedance inversion. After comparisons of different inversion techniques, stochastic hillclimbing inversion was applied. With this method, shale content inversion is performed using 3D seismic data. Then, the inverted results of shale content and well log data are incorporated into 3D models. This demonstrates a procedure to build fine scale models using multi scale seismic data, especially 3D seismic amplitude volume.The models generated through sequential Gaussian-Bayesian simulation have several advantages including: (1) higher vertical resolution compared with 3D inverted acoustic impedance (AI); (2) consistency of lateral variation as 3D inverted AI; (3) more reliability due to integration cross-well seismic data. It is observed that the precision of the model depends on the 3D inversion.