Soft Sensing for Gas-Condensate Field Production Using Parallel-Genetic-Algorithm-Based Data Reconciliation

[+] Author and Article Information
Dan Wang

18 Fuxue Road, Changping District, Beijing 102249, P.R. China Beijing, Beijing 102249 China wangdanradio@126.com

Jing Gong

18# FuXue Road, ChangPing District, BeiJing, China BeiJIng, 102249 China ydgj@cup.edu.cn

Qi Kang

18 Fuxue Road, Changping, Beijing, China Beijing, 102249 China kangqichn@qq.com

Di Fan

18 Fuxue Road, Changping, Beijing, China Beijing, 102249 China 904533915@qq.com

Juheng Yang

No.27 Chengfang Street, Xicheng District Beijing, Beijing 100033 China yangjuheng@126.com

1Corresponding author.

Manuscript received December 25, 2018; final manuscript received April 24, 2019; published online xx xx, xxxx. Assoc. Editor: Matthew I. Campbell.

ASME doi:10.1115/1.4043671 History: Received December 25, 2018; Accepted April 27, 2019


During present offshore gas-condensate production, multiphase flow-meters, due to its exceedingly high cost, are being substituted by Soft Sensing (SS) technique for estimating total and single-well flowrates through sensor measurements and physical models. In this work, the inverse problem is solved by Data Reconciliation (DR), minimizing weighted sum of errors with constraints integrating multiple two-phase flow models. The DR problem is solved by Parallel Genetic Algorithm (PGA), without complex calculations required by conventional optimization. The newly developed SS method is tested by data from a realistic gas-condensate production system. The method is proved of good accuracy and robustness with individual pressure sensor invalid or total flowrate measurements unavailable. Meanwhile, the proposed method shows good parallel performance and the time cost of each DR process can meet the demand of engineering application.

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