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Offshore oil production planning optimization: An MINLP model considering well operation and flow assurance

Gao, Xiaoyong, Xie, Yi, Wang, Shuqi, Wu, Mingyang, Wang, Yuhong, Tan, Chaodong, Zuo, Xin and Chen, Tao (2020) Offshore oil production planning optimization: An MINLP model considering well operation and flow assurance Computers and Chemical Engineering, 133, 106674.

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Abstract

With the increasing energy requirement and decreasing onshore reserves, offshore oil production has attracted increasing attention. A major challenge in offshore oil production is to minimize both the operational costs and risks; one of the major risks is anomalies in the flows. However, optimization methods to simultaneously consider well operation and flow assurance in operation planning have not been explored. In this paper, an integrated planning problem both considering well operation and flow assurance is reported. In particular, a multi-period mixed integer nonlinear programming (MINLP) model was proposed to minimize the total operation cost, taking into account of well production state, polymer flooding, energy consumption, platform inventory and flow assurance. By solving this integrated model, each well's working state, flow rates and chemicals injection rates can be optimally determined. The proposed model was applied to a case originated from a real-world offshore oil site and the results illustrate the effectiveness.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Chemical and Process Engineering
Authors :
NameEmailORCID
Gao, Xiaoyong
Xie, Yi
Wang, Shuqi
Wu, Mingyang
Wang, Yuhong
Tan, Chaodong
Zuo, Xin
Chen, TaoT.Chen@surrey.ac.uk
Date : 2 February 2020
Funders : EPSRC - Engineering and Physical Sciences Research Council
DOI : 10.1016/j.compchemeng.2019.106674
Copyright Disclaimer : © 2019 Elsevier Ltd. All rights reserved.
Uncontrolled Keywords : Offshore oil production; Planning optimization; Flow assurance; Integrated planning model; Mixed integer nonlinear programming (MINLP)
Additional Information : This research was supported by National Key R&D Program of China (No. 2016YFC0303703), the National Natural Science Foundation of China (No. 21706282), Science Foundation of China University of Petroleum, Beijing (No. 2462017YJRC028) and the UK EPSRC (EP/R001588/1).
Depositing User : Diane Maxfield
Date Deposited : 07 Jan 2020 16:27
Last Modified : 07 Jan 2020 16:27
URI: http://epubs.surrey.ac.uk/id/eprint/853291

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