ABSTRACT In this work, we tackle the challenge of quantitative estimation of reservoir dynamic property variations during a period of production, directly from four‐dimensional seismic data in the amplitude domain. We employ a deep neural network to invert four‐dimensional seismic amplitude maps to the simultaneous changes in pressure, water and gas saturations. The method is applied to a real field data case, where, as is common in such applications, the data measured at the wells are insufficient for properly training deep neural networks, thus, the network is trained on synthetic data. Training on synthetic data offers much freedom in designing a training dataset, therefore, it is important to understand the impact of the data distribution on the inversion results. To define the best way to construct a synthetic training dataset, we perform a study on four different approaches to populating the training set making remarks on data sizes, network generality and the impact of physics‐based constraints. Using the results of a reservoir simulation model to populate our training datasets, we demonstrate the benefits of restricting training samples to fluid flow consistent combinations in the dynamic reservoir property domain. With this the network learns the physical correlations present in the training set, incorporating this information into the inference process, which allows it to make inferences on properties to which the seismic data are most uncertain. Additionally, we demonstrate the importance of applying regularization techniques such as adding noise to the synthetic data for training and show a possibility of estimating uncertainties in the inversion results by training multiple networks.

中文翻译：

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针对压力和饱和度变化的 4D 地震反演深度神经网络应用：优化合成训练数据集的使用

摘要在这项工作中，我们直接从振幅域中的四维地震数据解决了在生产期间对储层动态特性变化进行定量估计的挑战。我们采用深度神经网络将四维地震振幅图反演为压力、水和气饱和度的同时变化。该方法应用于真实的现场数据情况，在这种应用中，在井中测量的数据不足以正确训练深度神经网络，因此，网络是在合成数据上训练的。合成数据的训练为设计训练数据集提供了很大的自由度，因此，了解数据分布对反演结果的影响非常重要。要定义构建合成训练数据集的最佳方法，我们对填充训练集的四种不同方法进行了研究，并对数据大小、网络通用性和基于物理的约束的影响进行了评论。使用油藏模拟模型的结果来填充我们的训练数据集，我们展示了将训练样本限制为动态油藏属性域中流体流动一致组合的好处。有了这个，网络学习了训练集中存在的物理相关性，将这些信息结合到推理过程中，这使它能够对地震数据最不确定的属性进行推理。此外，