The conventional time-lapse processing workflow is usually sensitive to the non-repeatable uncertainties between different vintages caused by noise, acquisition designs and independent processing. Therefore, in order to reduce these non-repeatable uncertainties, all the datasets are usually acquired from well-sampled and well-repeated acquisition surveys, and the independent processing is always carefully tailored to maximally reduce the non-repeatable uncertainties during processing. Moreover, the conventional time-lapse analysis method, based on a time-shift map, is not always a good indicator of the actual velocity differences due to its local one-dimensional subsurface assumption. In order to relax these rigid requirements and have a better velocity change indicator, a robust high-resolution simultaneous joint migration inversion was proposed as an effective time-lapse tool for reservoir monitoring. The method combines a simultaneous data-processing strategy with the joint migration inversion method. Joint migration inversion is a full wavefield inversion method with a parameterization in terms of reflectivity and propagation velocity. The simultaneous strategy allows the baseline and monitor parameters to communicate and compensate with each other dynamically during inversion, thus, suppressing the non-repeatable uncertainties during the time-lapse processing. To investigate the feasibility of using high-resolution simultaneous joint migration inversion in practice, some numerical experiments are conducted to test the dependence of high-resolution simultaneous joint migration inversion on the quality of the time-lapse datasets including the following aspects: random noise; sparse surveys; ocean bottom node versus streamer (different types of monitoring surveys); non-repeated sources, including source positioning errors and non-repeated source wavelets; spatial weighting operators in the L2-norm penalty terms; and sensitivity to weak time-lapse effects. All the experiments are carried on the basis of a realistic synthetic time-lapse model based on the Grane field offshore Norway. These experiments show that high-resolution simultaneous joint migration inversion is robust to random noise, survey sparsity, survey non-repeatability, source positioning errors and source wavelet discrepancies. Furthermore, high-resolution simultaneous joint migration inversion remains effective when the spatial weighting operators in the L2-norm penalty terms are largely relaxed, and high-resolution simultaneous joint migration inversion is capable of detecting weak time-lapse changes (e.g. velocity changes down to $\pm$35 m/s).

中文翻译：

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同时联合偏移反演作为一种高分辨率延时成像方法：可行性和稳健性研究

传统的延时处理工作流程通常对噪声、采集设计和独立处理引起的不同年份之间不可重复的不确定性很敏感。因此，为了减少这些不可重复的不确定性，通常所有数据集都是从采样良好、重复性好的采集调查中获取的，独立处理总是经过精心定制，以最大限度地减少处理过程中的不可重复不确定性。此外，基于时移图的传统延时分析方法由于其局部一维地下假设，并不总是实际速度差异的良好指标。为了放宽这些刚性要求，有更好的速度变化指标，提出了一种强大的高分辨率同时联合偏移反演作为储层监测的有效延时工具。该方法将同步数据处理策略与联合偏移反演方法相结合。联合偏移反演是一种全波场反演方法，具有反射率和传播速度方面的参数化。同时策略允许基线和监测参数在反演过程中动态地相互通信和补偿，从而抑制延时处理过程中的不可重复的不确定性。研究在实践中使用高分辨率同时联合偏移反演的可行性，进行了一些数值实验来测试高分辨率同时联合偏移反演对延时数据集质量的依赖性，包括以下方面：随机噪声；稀疏调查；海底节点与拖缆（不同类型的监测调查）；非重复源，包括源定位误差和非重复源小波；L2范数惩罚项中的空间加权算子；以及对微弱延时效应的敏感性。所有实验都是在基于挪威海上 Grane 油田的现实合成延时模型的基础上进行的。这些实验表明，高分辨率同时联合偏移反演对随机噪声、测量稀疏性、测量非重复性、源定位误差和源小波差异具有鲁棒性。此外，$\pm$35 米/秒）。