Abstract：Delta stratigraphic reservoirs such as stream course sand body are thin and interbedded with mudstone, and they have poor connectivity. Feeble reflected signals from this kind of reservoirs on seismic section are not helpful for sand body distribution interpretation. Therefore we propose a method for reservoir parameter estimation based on support vector regression machine and well logging data. First we extract parameters from well logging data, which can reveal reservoir features as a guide. Then we establish relations between reservoir feature parameters and some seismic attributes by support vector regression machine for reservoir prediction. We apply this method to a project in the Block Changfeng, Huabei Oilfield. Estimated gamma parameter and R4 parameter are used to predict sand body distribution. The results reveal that the sand body distribution feature is well consistent with that of real well drilling, which proves that the proposed method can predict delta stratigraphic reservoirs and sand body distribution features.
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