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机构地区:[1]长江大学油气资源与勘探技术教育部重点实验室,武汉430100 [2]中石化胜利油田分公司勘探开发研究院,山东东营257015 [3]长江大学地球科学学院,武汉430100
出 处:《地球物理学报》2016年第5期1839-1848,共10页Chinese Journal of Geophysics
基 金:国家自然科学基金(41472097);中石化股份公司科技项目"基于地质模型的地震资料深度域综合解释技术"(P15057)联合资助
摘 要:横波速度预测问题的关键有两个,一是如何建立合理的岩石物理模型,二是针对建立的横波预测目标函数,如何准确高效地求解.针对第一个问题,对Pride模型和Lee模型(P-L模型)进行变形,提出拟固结指数的概念,将干岩石模量和岩石基质模量相联系,变形后的P-L模型在没有降低P-L模型准确度的情况下简化了问题的复杂度,建立起了饱和流体岩石弹性模量与干岩石模量、岩石基质模量、混合流体模量之间的关系,进而计算理论上的纵波速度,并通过比较实测纵波速度与计算的理论纵波速度大小,最终建立了横波预测的目标函数.针对第二个问题,借鉴了地震反演的思路,将该目标函数的最优化问题转化为线性矩阵方程组迭代求解问题,通过几步迭代就可以求解出合适的拟固结指数,进而得到预测横波速度.实际验证和应用表明,该横波预测方法具有很好的稳定性和准确性,并且岩石物理模型的构建和目标函数的求解思路可用于其他储集类型地层的横波预测.How to establish reasonable rock physical models,and how to solve objective function accurately and efficiently according to the established problem are the keys to accurate S-wave velocity prediction.For the former problem,the original P-L model is modified and a new concept of quasi-consolidation-coefficient is presented.The modified P-L model simplifies the relationship between dry rock modulus and rock matrix modulus without reducing the accuracy of the rockphysics model.Relationships among saturated rock elastic modulus,dry rock modulus,rock matrix modulus,and mixed fluid modulus are established using Gassmann equation,and P-wave velocity is calculated through these relationships.The final S-wave velocity prediction objective function is established by comparing the measured P-wave velocity and the calculated one.For the latter problem,the optimization problem of objective function is transformed into the iterativeproblem of linear matrix equations by means of the strategy of seismic inversion.Quasiconsolidation-coefficients are obtained through a few iterations,and then the S-wave velocity is determined.Model validation and actual application results show that the proposed method has good stability and accuracy.The construction of rock physics models and the solving strategy of objective function can be applied to S-wave velocity prediction for other types of reservoirs.
关 键 词:变形P-L模型 拟固结指数 非线性 矩阵迭代 横波预测
分 类 号:P631[天文地球—地质矿产勘探]
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