摘要
In the Hongze Area, the reservoirs vary rapidly laterally and are controlled by many factors, such as structure, lithology, oil source, and so on. S-wave well log curves are calculated from an equation derived from multiple-attribute regression analysis of RT, DT, GR, and DEN logs. Representative P-and S-wave velocities and Poisson＇s ratio are statistically computed for oil and water bearing reservoir rock, shale, and calcareous shale in each well. The averaged values are used for AVO forward modeling. Comparing the modeling results with actual seismic data limit the possible AVO interpretations. Well and seismic data are used to calibrate inverted P-wave, S-wave, Poisson＇s ratio, and AVO gradient attribute data sets. AVO gradient data is used for lithofacies interpretation, P-wave data is used for acoustic impedance inversion, S-wave data is used for elastic impedance, and Poisson＇s ratio data is used for detecting oil and gas. The reservoir and hydrocarbon detections are carried out sequentially. We demonstrate that the AVO attributes method can efficiently predict reservoir and hydrocarbon potential.
洪泽地区由于沉积的特点,储层横向变化快,油藏受构造、岩性、油源多因素控制。在对该区三维AVO属性体解释中,利用多元回归方法求取了横波曲线,分岩性和含油气性统计了纵、横渡、泊松比参数分布规律,建立了本区的含油砂岩的正演模型,从而降低了AVO解释的多解性。通过井-震结合对四种AVO属性数据体进行了标定,并确定了各属性体应用范围,进而进行了储层和含油气检测。实践表明,该方法能有效地利用AVO属性数据体进行储层预测及油气检测,具有一定的推广价值。
