The Effects of Emotion, Trust and Perception on Tax Compliance: Empirical Evidence from Vietnam

Nguyen, Vu Manh Hoai; Nguyen, Van Thi Bich; Nguyen-Cuu, Dinh; Nguyen, Van Thi Thu; Ngoc, Phuong Ly; Ngo, Thuy Thi Ngoc; Tran‐Nam, Binh

doi:10.1108/s1058-749720210000029010

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…Key words: Machine learning, decline curve analysis, wellhead pressure, production forecast, reserves. In recent years, machine learning has permeated the global oil and gas industry, for example for lithofacies classification [2,3], depositional facies prediction [4,5], history matching [6,7], and virtual flowmeter [8]. Regarding DCA, outstanding successful applications in production forecasting have been made.…”

Section: Discussionmentioning

confidence: 99%

Application of machine learning to decline curve analysis (DCA) for gas-condensate production wells with complex production history due to add-on perforation of new reservoirs

Ngo,

Pham,

Nguyen

et al. 2023

PVJ

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For every oil and gas operator, DCA plays an essential role since it provides crucial information for production planning and reserves estimation. DCA is the analysis of the decline in production rate or pressure over time, which can be done by fitting a curve through production or pressure historical data points and making a forecast for the well based on the assumption that the same declining trend will continue in the future. However, the conventional DCA method has been shown to have some limitations. On the other hand, machine learning has been vigorously and extensively researched in the last decade; its applications can be found in every aspect of life as well as in the oil and gas industry. Therefore, it is the ideal time to study the application of machine learning to DCA, to complement this important analysis. In this case study, machine learning was used to predict the decline of wellhead pressure, thereby determining well life as well as estimating reserves. The method was applied to 2 wells with very complex production histories due to add-on perforation of new reservoirs. The prediction was verified to have high reliability by comparison with dynamic modeling results.

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Section: Discussionmentioning

confidence: 99%

Application of machine learning to decline curve analysis (DCA) for gas-condensate production wells with complex production history due to add-on perforation of new reservoirs

Ngo,

Pham,

Nguyen

et al. 2023

PVJ

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show abstract

“…According to experiments, the MSSMA-KELM is a promising technology. Lan Ngoc-Nguyen et al [153] introduced SMA to detect and monitor the suspension footbridge's damage. The experimental results proved that SMA was more reliable at finding the damage location and determining damage's degree than the CS and GA. Hamza MA et al [155] introduced a SMO model with Bidirectional Gated Recurrent Unit (BiGRU) model to forecast traffic condition in smart cities.…”

Section: Deep Learning (Dl)mentioning

confidence: 99%

Advances in Slime Mould Algorithm: A comprehensive Survey

Wei,

Othman,

Daud

et al. 2023

Preprint

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Slime Mould Algorithm (SMA) is a new swarm intelligence algorithm inspired by the oscillatory behavior of slime molds during foraging. Numerous researchers have widely applied SMA and its variants in various domains and proved its value by the experiments in literatures. In this paper a comprehensive survey on SMA is introduced, which is based on 130 articles visa Google-scholar between 2022 and July, 2023. Firstly, the theory of SMA is described. Secondly the improved SMA variants are provided and categorized according to the approach that they are applied with. Finally, it also discusses the main applications domains of SMA such as engineering optimization, energy optimization, machine learning, network, scheduling optimization, image segmentation and etc. This review presents some research suggestion for researcher who is interested in this algorithm.

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Tax education and fear-appealing messages: A grease or sand in the wheels of tax compliance?

Trawule

Gadzo

Kportorgbi

et al. 2022

Cogent Business & Management

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The Effects of Emotion, Trust and Perception on Tax Compliance: Empirical Evidence from Vietnam

Cited by 3 publications

References 22 publications

Application of machine learning to decline curve analysis (DCA) for gas-condensate production wells with complex production history due to add-on perforation of new reservoirs

Application of machine learning to decline curve analysis (DCA) for gas-condensate production wells with complex production history due to add-on perforation of new reservoirs

Advances in Slime Mould Algorithm: A comprehensive Survey

Tax education and fear-appealing messages: A grease or sand in the wheels of tax compliance?

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