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• W2948490939 abstract "Different sources of data are used to construct a reliable model of reservoir for oil/gas production. This model ought to be matched with the production history of reservoir and also show reliable predictions for future performance. To this end, permeability modeling (characterization of heterogeneity) is crucially important which is proved to be done by Multiple Point Statistics (MPS) recently. Furthermore, deep learning methods are massively used as a promising tool for regression applications. In this study, one MPS method is employed for generating the reservoir realizations. Realizations, alongside their simulation outputs, are utilized for training a convolutional deep network. In this manner, MPS is joined with deep learning to find the most appropriate realization(s) of the reservoir based on the fluid flow simulation. Moreover, unseen MPS realizations as well as another MPS realizations are used to verify the selection ability of trained network. The detailed architecture of convolutional network is illustrated in this study. The purpose of training this network and combination with MPS is to generate the matched realization(s) in history period that also show acceptable reservoir behavior in the future times of reservoir simulation. After training, the actual production data of selected realizations are obtained by simulation the reservoir for history and also future times. The results show that selected realizations efficiently capture the trend of reference behavior. Although these realizations lack identical permeability values, they have same texture of permeability (permeability heterogeneity). Meanwhile, they show acceptable match in reservoir simulation outputs. By proposed workflow, the uncertainty of permeability modeling is considered more exhaustively. It is done by selecting the realizations from enormous possible realizations dataset and providing a deep learning tool which is capable for screening quite large number of realizations. Interesting finding is satisfactory behavior of realization(s) in both history and future periods of reservoir performance." @default.
• W2948490939 created "2019-06-14" @default.
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• W2948490939 date "2019-10-01" @default.
• W2948490939 modified "2023-10-18" @default.
• W2948490939 title "Toward more realistic models of reservoir by cutting-edge characterization of permeability with MPS methods and deep-learning-based selection" @default.
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• W2948490939 doi "https://doi.org/10.1016/j.petrol.2019.05.086" @default.
• W2948490939 hasPublicationYear "2019" @default.
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