Quantitative prediction of fluvial sandbodies by combining seismic attributes of neighboring zones

作者：Wei Li, Dali Yue, Luca Colombera, Yushan Du, Shengyou Zhang, Ruijing Liu, Wurong Wang · 发表于：Journal of Petroleum Science and Engineering · 年份：2020 · DOI：10.1016/j.petrol.2020.107749 · 被引用次数：32 · 研究领域：Seismic Imaging and Inversion Techniques、Hydraulic Fracturing and Reservoir Analysis、Hydrocarbon exploration and reservoir analysis

The geological and geophysical characterization of hydrocarbon-bearing sandstones of fluvial origin is a challenging task. Channel sandbodies occurring at different stratigraphic levels (i.e., in a reservoir interval of interest as well as in overlying and underlying stratigraphic intervals) but overlapping in planview usually cause significant seismic interference due to limitations in seismic resolution: this can produce significant error in the prediction of sand location and thickness using seismic attributes. To mitigate the effect of seismic interferences by zones neighboring a target reservoir interval, a new method is proposed that combines multiple seismic attributes of the target interval and of its interfering neighboring zones, implemented by a supervised machine learning algorithm using support vector regression (SVR). Since the thickness of neighboring intervals causing seismic interference has a constant value of a quarter of a wavelength (1/4 λ), the stratal slice corresponding with the top horizon of the target interval is taken as the base of a window of 1/4 λ to calculate seismic attributes for the overlying zone; similarly, the stratal slice corresponding with the bottom horizon is taken as the top of a window of 1/4 λ to calculate seismic attributes for the underlying zone. The proposed method was applied to a subsurface dataset (including a 3D seismic dataset and 255 wells) of the Chengdao oilfield, in the Bohai Bay Basin (China). The interval of interes...