Accelerated Phase Equilibrium Predictions for Subsurface Reservoirs Using Deep Learning Methods

Tao Zhang, Yiteng Li, Shuyu Sun

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations


Multiphase fluid flow with complex compositions is an increasingly attractive research topic with more and more attentions paid on related engineering problems, including global warming and green house effect, oil recovery enhancement and subsurface water pollution treatment. Prior to study the flow behaviors and phase transitions in multi-component multiphase flow, the first effort should be focused on the accurate prediction of the total phase numbers existing in the fluid mixture, and then the phase equilibrium status can be determined. In this paper, a novel and fast prediction technique is proposed based on deep learning method. The training data is generated using a selected VT dynamic flash calculation scheme and the network constructions are deeply optimized on the activation functions. Compared to previous machine learning techniques proposed in literatures to accelerate vapor liquid phase equilibrium calculation, the total number of phases existing in the mixture is determined first and other phase equilibrium properteis will be estimated then, so that we do not need to ensure that the mixture is in two phase conditions any more. Our method could handle fluid mixtures with complex compositions, with 8 different components in our example and the original data is in a large amount. The analysis on prediction performance of different deep learning models with various neural networks using different activation functions can help future researches selecting the features to construct the neural network for similar engineering problems. Some conclusions and remarks are presented at the end to help readers catch our main contributions and insight the future related researches.
Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science
PublisherSpringer International Publishing
Number of pages10
ISBN (Print)9783030227463
StatePublished - Jun 8 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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