Aminu, Kuda Tijjani

Chen, Yi

McGlinchey, Don

English

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

ResearchOnline@GCU

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

https://researchonline.gcu.ac.uk/files/27005516/D.McGlinchey_Revision_PETROL12935R2.pdf

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

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