Abstract In this paper, the first self-adaptive deep learning algorithm is proposed in details to accelerate flash calculations, which can quantitatively predict the total number of phases in the mixture and related thermodynamic properties at equilibrium for realistic reservoir fluids with a large number of components under various environmental conditions. A thermodynamically consistent scheme for phase equilibrium calculation is adopted and implemented at specified moles, volume and temperature, and the flash results are used as the ground truth for training and testing the deep neural network. The critical properties of each component are considered as the input features of the neural network and the final output is the total number of phases at equilibrium and the molar compositions in each phase. Two network structures are well designed, one of which transforms the input of various numbers of components in the training and the objective fluid mixture into a unified space before entering the productive neural network. “Ghost components” are defined and introduced to process the data padding work in order to modify the dimension of input flash calculation data to meet the training and testing requirements of the target fluid mixture. Hyperparameters on both two neural networks are carefully tuned in order to ensure the physical correlations underneath the input parameters are preserved properly through the learning process. This combined structure can make our deep learning algorithm to be self-adaptive to the change of input components and dimensions. Furthermore, two Softmax functions are used in the last layer to enforce the constraint that the summation of mole fractions in each phase is equal to 1. An example is presented that the flash calculation results of a 8-component Eagle Ford oil is used as input to estimate the phase equilibrium state of a 14-component Eagle Ford oil. The results are satisfactory with very small estimation errors. The capability of the proposed deep learning algorithm is also verified that simultaneously completes phase stability test and phase splitting calculation. Remarks are concluded at the end to provide some guidance for further research in this direction, especially the potential application of newly developed neural network models.

中文翻译：

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一种自适应深度学习算法加速多分量闪存计算

摘要 本文详细提出了首个自适应深度学习算法来加速闪蒸计算，该算法可以定量预测混合物中的总相数和平衡时的相关热力学性质，适用于具有大量组分的现实储层流体。在各种环境条件下。采用热力学一致的相平衡计算方案，并在指定的摩尔、体积和温度下实施，并将闪光结果用作训练和测试深度神经网络的基本事实。每个组件的关键特性被视为神经网络的输入特征，最终输出是平衡相的总数和每个相中的摩尔组成。两种网络结构设计良好，其中之一在进入生产神经网络之前将训练中各种数量的组件的输入和目标流体混合物转换为一个统一的空间。定义并引入了“幽灵成分”来处理数据填充工作，以修改输入闪蒸计算数据的维度，以满足目标流体混合物的训练和测试要求。两个神经网络上的超参数都经过仔细调整，以确保在学习过程中正确保留输入参数下的物理相关性。这种组合结构可以使我们的深度学习算法能够自适应输入组件和维度的变化。此外，最后一层使用了两个 Softmax 函数来强制约束各相摩尔分数的总和等于 1。 以 8 组分 Eagle Ford 油的闪蒸计算结果作为输入来估计14 组分 Eagle Ford 油的相平衡状态。结果令人满意，估计误差很小。还验证了所提出的深度学习算法的能力，可以同时完成相位稳定性测试和分相计算。最后进行评论，为该方向的进一步研究提供一些指导，特别是新开发的神经网络模型的潜在应用。举例说明了以 8 组分 Eagle Ford 油的闪蒸计算结果作为输入来估计 14 组分 Eagle Ford 油的相平衡状态。结果令人满意，估计误差很小。还验证了所提出的深度学习算法的能力，可以同时完成相位稳定性测试和分相计算。最后进行评论，为该方向的进一步研究提供一些指导，特别是新开发的神经网络模型的潜在应用。举例说明了以 8 组分 Eagle Ford 油的闪蒸计算结果作为输入来估计 14 组分 Eagle Ford 油的相平衡状态。结果令人满意，估计误差很小。还验证了所提出的深度学习算法的能力，可以同时完成相位稳定性测试和分相计算。最后进行评论，为该方向的进一步研究提供一些指导，特别是新开发的神经网络模型的潜在应用。还验证了所提出的深度学习算法的能力，可以同时完成相位稳定性测试和分相计算。最后进行评论，为该方向的进一步研究提供一些指导，特别是新开发的神经网络模型的潜在应用。还验证了所提出的深度学习算法的能力，可以同时完成相位稳定性测试和分相计算。最后进行评论，为该方向的进一步研究提供一些指导，特别是新开发的神经网络模型的潜在应用。