An architecture boundary-based fine characterization method of delta front reservoir：A case study on the Paleogene Lower Dongying Formation in Suizhong oilfield, Northern Bohai Bay Basin, North China

Sand of front delta is formed by constantly migration and progradation of mouth bar. There is the muddy interbed between the mouth bars, and the inner single mouth bar develops foreset bed which is thin, so that is difficult... [ view full abstract ]

Sand of front delta is formed by constantly migration and progradation of mouth bar. There is the muddy interbed between the mouth bars, and the inner single mouth bar develops foreset bed which is thin, so that is difficult to characterize, so the accuracy of geologic model must be high to characterize the thin forest bed. And it’s necessary to upscale model for numerable modelling, but it could affect effect of upscaling model for characterizing forest bed. Therefore, an architecture boundary-based fine characterization method of delta front reservoir is presented in the paper to deal with these problems. On basis of the fine study of reservoir characterization, taking 3-4 levels’ architecture boundary as construct horizon, taking well characterization as conditional data, using plane distributive boudary to constraint plane scale, using architecture top and bottom boundary to constraint vertical scale, the conditional geological model which accords with geological pattern could be built by this mothod. A case study on the Paleogene Lower Dongying Formation in Suizhong oilfield, Northern Bohai Bay Basin, North China, this paper makes the fine three-dimensional characterization to typical area. The greatest advantage of this modeling method is to make the best use of the geological research achievements. The result shows:(1) The model built by this method could accord with conditional data absolutely, and proportions of architecture elements in the model are almost the same with those in well. This kind of conditional model is reliable. (2) Although model is conditional, computing rate of the model is not slow. And modeling process is not complicated. (3) The architecture element characteristics including shape configuration, scales and combination style matches delta front architecture pattern perfectly. The model could finely characterize spacial distribution, scale and pattern of architecture elements of different level. (4) After model upscaling, model still could reappear characteristic of architecture elements including foreset bed. It could deal with the contradiction between characterizing foreset bed finely and upscaling model. Therefore, model built by this method not only could finely reappear spatial distribution of delta front sand-body, but also could increase the accuracy of numerical modeling after model upscaling.