Application of Combined Norm Constrained Sparseness Spike Inverse
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摘要: 稀疏脉冲反演实际上就是利用反褶积原理, 从带有噪声的地震道中计算出具有稀疏分布特征的反射系数的振幅和时间.稀疏脉冲反演是非线性优化问题, 通常都是把非线性优化问题转化为线性优化问题, 然后用线性优化算法求解.以范数约束为基础, 提出L1-L2范数联合约束求解的方法.该算法采用了目前国际流行的内点算法, 与传统的优化算法相比, 这种算法具有精度高, 速度快的优点.通过研究人工模型和南海某油田实际数据, 表明该算法的计算结果和测井记录吻合好, 提高了地震分辨率, 目的层段分辨率优于8m.利用反射系数剖面预测的储层厚度和开发井吻合很好, 大大地降低了资源量计算的风险和油田开发的不确定性.Abstract: Sparse-spike deconvolution is an inverse issue which estimates the time and the amplitudes of the sparseness reflectivity (spikes) from the noisy seismic traces.Sparseness spike inverse is highly non-linear optimization problem that can be solved using the L1-L2 norm constrained method introduced in this paper.This method is characterized with its application of the log-barrier interior point to solve the sparseness inverse problem which is higher in terms of resolution and faster than conventional optimization methods.Resultsfrom the synthetic and real 3D data show that the physically meaningful high-resolution sparse-spike profile can be derived from the band-limited noisy data.Real data show that the method improves seismic resolution and estimates the thickness of thin bed which can reduce the uncertainty of resource estimation and oil field production.
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表 1 反演的毛厚度和地震视厚度的对比(m)
Table 1. Comparison between seismic apparent thickness and predicted thickness from inversion
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