Separation efficiency prediction of non-Newtonian oil-water swirl-vane separators in offshore platform based on GA-BP neural network

Li, Bin; Huang, Chengyu; Liu, Leo Yang; Yao, Lan; Ning, Bo; Yang, Lele

doi:10.1016/j.oceaneng.2024.116984

Ocean Engineering

2024

DOI: 10.1016/j.oceaneng.2024.116984

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Separation efficiency prediction of non-Newtonian oil-water swirl-vane separators in offshore platform based on GA-BP neural network

Bin Li,

Chengyu Huang,

Leo Yang Liu

et al.

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Cited by 3 publications

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Underwater glider motion parameter generation based on structure-optimized deep belief network and BP neural network

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Separation performance and centrifugal characteristics of a cascade dewatering equipment

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A diagnosis method for loss of circulation based on transient-pressure wave analysis and particle swarm optimization

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Physics of Fluids

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Lost circulation during operations poses a significant threat to production processes. In the search for an effective detection method, an impulse-response detection method of lost circulation is introduced. This method involves generating transient pressure waves at the wellhead and analyzing their time-frequency domain characteristics to pinpoint location for lost circulation within the wellbore annulus system. Utilizing the data processing capabilities of machine learning models, this study proposes an integrated model for signal feature classification and diagnosis model for lost circulation. Drawing from extensive experimental data, this model integrates laboratory experiments, signal analysis, and machine learning algorithms. Data preprocessing, including wavelet variation and denoising, precedes the application of an enhanced adaptive noise complete ensemble empirical modal decomposition with adapted noise (ICEEMDAN) alongside energy and sample entropy analysis for feature extraction. By establishing a mapping relationship between signal features and lost circulation changes, we develop an improved backpropagation neural network (IBP) model using the particle swarm optimization (PSO) algorithm for diagnosis (PSO-IBP). Comparative analysis of five models reveals compelling results: ① PSO-IBP achieves an average accuracy of 97.60%, with a standard deviation of 0.356; ② diagnosis accuracy for every lost circulation scenario exceeds 92%, outperforming other models in precision, recall, and F-Score; ③ even with limited training data, PSO-IBP maintains 84% accuracy, demonstrating superior performance. Further analysis highlights the efficacy of PSO-IBP, especially when leveraging ICEEMDAN for signal feature extraction, in accurately diagnosing lost circulation.

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Separation efficiency prediction of non-Newtonian oil-water swirl-vane separators in offshore platform based on GA-BP neural network

Cited by 3 publications

References 32 publications

Underwater glider motion parameter generation based on structure-optimized deep belief network and BP neural network

Underwater glider motion parameter generation based on structure-optimized deep belief network and BP neural network

Separation performance and centrifugal characteristics of a cascade dewatering equipment

A diagnosis method for loss of circulation based on transient-pressure wave analysis and particle swarm optimization

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