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Globally optimized machine-learning framework for CO2-hydrocarbon minimum miscibility pressure calculations

•A deep learning CGAN model using adversarial training to more effectively perform CO2–oil MMP prediction.•The Bayesian optimization library in Python for tuning CGAN hyperparameters for CGAN initially proposed.•Input CGAN model parameters are single compound instead of various pseudo-components. Ac...

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Bibliographic Details
Published in:Fuel (Guildford) 2022-12, Vol.329, p.125312, Article 125312
Main Authors: Huang, Can, Tian, Leng, Zhang, Tianya, Chen, Junjie, Wu, Jianbang, Wang, Hengli, Wang, Jiaxin, Jiang, Lili, Zhang, Kaiqiang
Format: Article
Language:English
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Summary:•A deep learning CGAN model using adversarial training to more effectively perform CO2–oil MMP prediction.•The Bayesian optimization library in Python for tuning CGAN hyperparameters for CGAN initially proposed.•Input CGAN model parameters are single compound instead of various pseudo-components. Accurate determination of CO2-hydrocarbon minimum miscibility pressure (MMP) is critically important for CO2 geological storage and utilization in oil and gas reservoirs. Here we propose a machine-learning framework, the conditional generative adversarial network, together with the Bayesian optimization algorithm, to calculate the CO2-hydrocarbon MMPs. A total of 180-set MMP data are collected from the public resources to facilitate and validate the proxy model. Also, 21 MMP-influential factors covering fluid compositions and operating conditions are specifically evaluated to analyse their effects on the MMP. In comparison with the existing artificial neural network as well as support vector regression models based on radial basis function kernel and polynomial function kernel, the newly-proposed model does not only outperform with the lowest calculation error (MAPE of 6.81% and MSE of 3.2006), also vividly reflect the interactive relationships of each influential factor and the MMP
ISSN:0016-2361
DOI:10.1016/j.fuel.2022.125312