Tying seismic data to well data is critical in reservoir characterization. In general, the main factors controlling a successful seismic well tie are an accurate time-depth relationship and a coherent wavelet estimate. Wavelet estimation methods are divided into two major groups: statistical and deterministic. Deterministic methods are based on using the seismic trace and the well data to estimate the wavelet. Statistical methods use only the seismic trace and generally require assumptions about the wavelet’s phase or a random process reflectivity series. We have compared the estimation of the wavelet for seismic well tie purposes through least-squares minimization and zero-order quadratic regularization with the results obtained from homomorphic deconvolution. Both methods make no assumption regarding the wavelet’s phase or the reflectivity. The best-estimated wavelet is used as the input to sparse-spike deconvolution to recover the reflectivity near the well location. The results show that the wavelets estimated from both deconvolutions are similar, which builds our confidence in their accuracy. The reflectivity of the seismic section is recovered according to known stratigraphic markers (from gamma-ray logs) present in the real data set from the Viking Graben field, Norway.

中文翻译：

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基于线性反演的地震子波估计和反射率恢复：北海Viking Graben真实数据集的井-地震联系

将地震数据绑定到井数据对于储层表征至关重要。通常，控制成功的地震井口的主要因素是准确的时深关系和相干小波估计。小波估计方法分为两大类：统计方法和确定性方法。确定性方法基于使用地震道和井数据估算小波的方法。统计方法仅使用地震道，并且通常需要关于小波相位或随机过程反射率序列的假设。我们已经通过最小二乘最小化和零阶二次正则化将小波用于地震井口目的的估计与同态反卷积获得的结果进行了比较。两种方法都没有假设小波的相位或反射率。最佳估计的小波用作稀疏峰反褶积的输入，以恢复井位附近的反射率。结果表明，从两个反卷积估计的小波相似，这建立了我们对其准确性的信心。地震剖面的反射率是根据挪威Viking Graben油田的真实数据集中存在的已知地层标记（来自伽马射线测井）恢复的。