Study of Porosity Quantitative Predication via Prestack Attribute
Ang Li, Chen Shumin, Erhua Zhang, Ju Linbo, Zhang Liyan · 2014
Daqing Changyuan Fuyu pay is the focus target pay for the next exploration and development, sand thickness is generally less than 5 meters, porosity less than 10%, permeability about 1md, is a typical thin reservoir with low porosity and permeability, it is difficult for effective reservoir prediction as seismic data resolution is low. To address this problem, we try to apply the inverse Q filtering method to improve the resolution of seismic gathers; AVO information in the far offset traces is prominent. With high-resolution CRP gathers, AVO multi-attributes forward modeling was performed; results show that porosity changes are more sensitive to AVO attribute compared with saturation, there is good positive correlation between |G|/P and porosity, so it is possible for quantitative prediction of porosity via AVO attribute, the method is applied to the actual data, and good result was achieved, predication coincidence rate is more than 80%. X11 well deployed via the result achieved 40 tons / day.