Time-lapse (4D) seismic inversion aims to predict changes in elastic rock properties, such as acoustic impedance, from measured seismic amplitude variations due to hydrocarbon production. Possible approaches for 4D seismic inversion include two classes of method: sequential independent 3D inversions and joint inversion of 4D seismic differences. We compare the standard deterministic methods, such as coloured and model-based inversions, and the probabilistic inversion techniques based on a Bayesian approach. The goal is to compare the sequential independent 3D seismic inversions and the joint 4D inversion using the same type of algorithm (Bayesian method) and to benchmark the results to commonly applied algorithms in time-lapse studies. The model property of interest is the ratio of the acoustic impedances, estimated for the monitor, and base surveys at each location in the model. We apply the methods to a synthetic dataset generated based on the Namorado field (offshore southeast Brazil). Using this controlled dataset, we can evaluate properly the results as the true solution is known. The results show that the Bayesian 4D joint inversion, based on the amplitude difference between seismic surveys, provides more accurate results than sequential independent 3D inversion approaches, and these results are consistent with deterministic methods. The Bayesian 4D joint inversion is relatively easy to apply and provides a confidence interval of the predictions.

中文翻译：

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比较时移地震反演的不同方法

延时（4D）地震反演的目的是根据由于烃类产生的测得的地震振幅变化来预测弹性岩石属性（例如声阻抗）的变化。4D地震反演的可能方法包括两类方法：顺序独立3D反演和4D地震差异的联合反演。我们比较了标准的确定性方法（例如有色和基于模型的反演）以及基于贝叶斯方法的概率反演技术。目的是比较使用相同类型算法（贝叶斯方法）的顺序独立3D地震反演和联合4D地震反演，并将结果与​​时移研究中常用的算法进行比较。感兴趣的模型属性是为监听器估算的声阻抗之比，并在模型中的每个位置进行基础调查。我们将这些方法应用于基于Namorado场（巴西东南沿海）生成的综合数据集。使用此受控数据集，我们可以正确地评估结果，因为已知真正的解决方案。结果表明，基于地震勘测之间的振幅差异的贝叶斯4D联合反演比顺序独立3D反演方法提供了更准确的结果，并且这些结果与确定性方法一致。贝叶斯4D联合反演相对容易应用，并提供了预测的置信区间。我们可以正确评估结果，因为它是真正的解决方案。结果表明，基于地震勘测之间的振幅差异的贝叶斯4D联合反演比顺序独立3D反演方法提供了更准确的结果，并且这些结果与确定性方法一致。贝叶斯4D联合反演相对容易应用，并提供了预测的置信区间。我们可以正确评估结果，因为它是真正的解决方案。结果表明，基于地震勘测之间的振幅差异的贝叶斯4D联合反演比顺序独立3D反演方法提供了更准确的结果，并且这些结果与确定性方法一致。贝叶斯4D联合反演相对容易应用，并提供了预测的置信区间。