Summary This study investigated the ability to produce accurate multiphase flow profiles simulating the response of producing reservoirs, using generative deep learning (GDL) methods. Historical production data from numerical simulators were used to train a variational autoencoder (VAE) algorithm that was then used to predict the output of new wells in unseen locations. This work describes a procedure in which data analysis techniques can be applied to existing historical production profiles to gain insight into field-level reservoir flow behavior. The procedure includes clustering, dimensionality reduction, correlation, in addition to novel interpretation methodologies that synthesize the results from reservoir simulation output, characterizing flow conditions. The insight was then used to build and select samples to train a VAE algorithm that reproduces the multiphase reservoir behavior for unseen operational conditions with high accuracy. Furthermore, using deep feature space interpolation, the trained algorithm can be used to further generate new predictions of the reservoir response under operational conditions for which we do not have previous examples in the training data set. It is found that VAE can be used as a robust multiphase flow simulator. Applying the methodology to the problem of determining multiphase production rate from new producing wells in undrilled locations showed positive results. The methodology was tested successfully in predicting multiphase production under different scenarios including multiwell channelized and heterogeneous reservoirs. Comparison with other shallow supervised algorithms demonstrated improvements realized by the proposed methodology. The study developed a novel methodology to interpret both data and GDL algorithms, geared toward improving reservoir management. The method was able to predict the performance of new wells in previously undrilled locations, potentially without using a reservoir simulator.

中文翻译：

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使用生成式深度学习模拟油藏中的石油和水生产

总结 本研究调查了使用生成深度学习 (GDL) 方法生成准确的多相流剖面的能力，以模拟生产储层的响应。来自数值模拟器的历史生产数据用于训练变分自动编码器 (VAE) 算法，该算法随后用于预测看不见位置的新井的产量。这项工作描述了一个程序，其中数据分析技术可以应用于现有的历史生产剖面，以深入了解油田级储层流动行为。该过程包括聚类、降维、相关性，以及新的解释方法，这些方法综合油藏模拟输出的结果，表征流动条件。然后，该洞察力用于构建和选择样本以训练 VAE 算法，该算法以高精度再现未见操作条件下的多相油藏行为。此外，使用深度特征空间插值，经过训练的算法可用于在训练数据集中没有先前示例的操作条件下进一步生成油藏响应的新预测。发现 VAE 可用作稳健的多相流模拟器。将该方法应用于确定未钻井位置的新生产井的多相产量问题显示出积极的结果。该方法在预测不同情景下的多相生产方面得到了成功的测试，包括多井通道化和非均质油藏。与其他浅监督算法的比较证明了所提出的方法实现的改进。该研究开发了一种新的方法来解释数据和 GDL 算法，旨在改善油藏管理。该方法能够预测以前未钻井位置的新井的性能，可能无需使用油藏模拟器。