基于储集层地质模式用多种地震属性参数预测砂体含油气性

惠民凹陷江家店地区地震波的平均对数衰减率、反演波阻抗和地震振幅对砂体的含油气性反映较敏感 ,在对这些属性参数进行相关性研究的基础上 ,研究已知含油气性砂体的不同储集层地质模式 ,建立在地质模式约束下的多属性参数学习模式 ,采用复赛谱技术计算直接反映砂体吸收属性的地震波平均对数衰减率 ,在储集层地质模式的约束下 ,用支持向量网络方法判别砂体的含油气性。用该方法计算的结果和已知产油层位基本吻合 ,预测结果被钻井所证实。复赛谱分析方法算法稳定 ,计算速度快 ,是识别砂体含油气性较稳定和可靠的方法 ;支持向量网络方法具有更强的分类能力 ,预测实际问题的风险比人工神经网络方法要小得多。表 1图 4参 1 6

Predicting the oil/gas bearing potential of sand bodies with various seismic attribute parameters based on geological model of the reservoirs

Average seismic wave attenuation, inversion wave impedance and seismic amplitude of Jiangjiadian area in Huimin depression are sensitive to oil/gas bearing sand bodies. On the basis of the research for relativity between above mentioned attribute parameters, the geological models of different reservoirs of given oil/gas sand bodies were studied; multiple attribute parameters study model restrained by geological model was built up; average seismic wave attenuation of absorption attribution of sand bodies, which can be able to directly represent absorption attribute of sand bodies, was calculated by using cepstrum technology. With the restraint of geological model of reservoir, support vector network was used to judge oil/gas bearing potential of sand bodies. Results calculated by this method are basically identical with the given location of productive strata, and the predicted results have been confirmed by drilling. The cepstrum analysis, with small error in results and very quick calculation speed, is a reliable method for identification of oil sand bodies. Its risks in predicting practical problems are much smaller compared with artificial nervous network.