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Technical Brief

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

[+] Author and Article Information
Dan Wang

National Engineering Laboratory for Pipeline Safety,
China University of Petroleum-Beijing,
Changping, Beijing 102249, China
e-mail: wangdanradio@126.com

Jing Gong

Professor
National Engineering Laboratory for Pipeline Safety,
China University of Petroleum-Beijing,
Changping, Beijing 102249, China
e-mail: ydgj@cup.edu.cn

Qi Kang

National Engineering Laboratory for Pipeline Safety,
China University of Petroleum-Beijing,
Changping, Beijing 102249, China
e-mail: kangqichn@qq.com

Di Fan

National Engineering Laboratory for Pipeline Safety,
China University of Petroleum-Beijing,
Changping, Beijing 102249, China
e-mail: 904533915@qq.com

Juheng Yang

National Engineering Laboratory for Pipeline Safety,
China University of Petroleum-Beijing,
Changping, Beijing 102249, China
e-mail: yangjuheng@126.com

1Present address: PetroChina International Co., Ltd., Beijing 100033, China.

Manuscript received December 25, 2018; final manuscript received April 24, 2019; published online June 7, 2019. Assoc. Editor: Matthew I. Campbell.

J. Comput. Inf. Sci. Eng 19(4), 044501 (Jun 07, 2019) (8 pages) Paper No: JCISE-18-1333; 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 a 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 invalid individual pressure sensor or unavailable total flowrate measurements. 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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Figures

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Fig. 1

Schematic of instruments of the gas-condensate production system

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Fig. 2

Mechanism of PGA running on the multicore cluster system

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Fig. 3

Complete network topology

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Fig. 4

Solution procedure of PGA

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Fig. 5

Convergence performance of SGA and PGA repeated five times

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Fig. 6

Speed-up ratio under different population sizes

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Fig. 7

Parallel efficiency under different population sizes

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Fig. 8

Parallel performance under different number of subpopulations

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Fig. 9

Results of mass flowrates with pipeline model calibrated

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Fig. 10

Results of mass flowrates without model calibration

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Fig. 11

Estimated flowrates without total flowrate measurements

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Fig. 12

Estimated flowrates without bottom hole pressure measurements

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Fig. 13

Estimated flowrates without wellhead pressure measurements

Tables

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