Optimal design for real-time quantitative monitoring of sand in gas flowline using computational intelligence assisted design framework

Kuda Tijjani Aminu, Don McGlinchey, Yi Chen

Research output: Contribution to journalArticle

Abstract

Global demand for oil and gas is still increasing rapidly. The direct consequence of this is the increased operating pressure amid concerns over increasing sand production. According to the Society of Petroleum Engineers (SPE), 70% of the world's hydrocarbon reserves are contained in reservoirs situated on unconsolidated formations. Given the reality of these formations, sand production will certainly be a problem of significant concern particularly during the later life of the fields when they become more ‘mature’. However, to monitor sand and optimise its production for improved recovery and safety, life extension and economy of the fields and ensured reliability, the automatic detection and prediction of sand flow characteristic measurements; sand flow rate (SFR), sand concentration (SC), line pressure drop (PD), and gas velocity (GV), has become an important research topic of great interest. Despite this importance, discussion of the topic is still lacking in the literature. This paper proposes a novel and robust architecture of intelligent real-time sand flow characteristic measurement using an acoustic sensor and computational intelligence assisted design (CIAD) framework. It fully incorporates acoustic signal processing and analysis, prediction algorithms and optimisation algorithms in the design. Acoustic features based on acoustic signal processing techniques are extracted to reduce the dimensionality of the acoustic signals. A classical Artificial Neural Network (ANN) is used to model the non-linear relationships between the acoustic signal characteristics and the flow characteristics measurands. In addition, the ANN algorithm adapts its weights and biases using the Grey Wolf Optimiser (GWO) through minimisation of the cost function during the training phase. Preliminary results obtained on a laboratory test rig demonstrate that an acoustic sensor coupled with CIAD may provide simple and robust practical solution to the measurement problem of particle-laden gas flow characteristics in real-time.
Original languageEnglish
Pages (from-to)1059-1071
Number of pages13
JournalJournal of Petroleum Science and Engineering
Volume177
Early online date12 Mar 2019
DOIs
Publication statusPublished - Jun 2019

Fingerprint

Flowlines
Artificial intelligence
Sand
Monitoring
Acoustics
Gases
Acoustic signal processing
Plant life extension
Neural networks
Signal analysis
Sensors
Optimal design
Cost functions
Pressure drop
Flow of gases
Crude oil
Hydrocarbons
Flow rate
Engineers
Recovery

Keywords

  • sand production
  • signal processing
  • quantitative modelling
  • ANN
  • CIAD
  • GWO

Cite this

Aminu, Kuda Tijjani ; McGlinchey, Don ; Chen, Yi. / Optimal design for real-time quantitative monitoring of sand in gas flowline using computational intelligence assisted design framework. In: Journal of Petroleum Science and Engineering. 2019 ; Vol. 177. pp. 1059-1071.
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Optimal design for real-time quantitative monitoring of sand in gas flowline using computational intelligence assisted design framework. / Aminu, Kuda Tijjani; McGlinchey, Don; Chen, Yi.

In: Journal of Petroleum Science and Engineering, Vol. 177, 06.2019, p. 1059-1071.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Optimal design for real-time quantitative monitoring of sand in gas flowline using computational intelligence assisted design framework

AU - Aminu, Kuda Tijjani

AU - McGlinchey, Don

AU - Chen, Yi

N1 - Acceptance from webpage AAM: 12m embargo

PY - 2019/6

Y1 - 2019/6

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