Design of a three-dimensional current sensor with measuring upwelling

Hu, Shaozheng; Wang, Huaiyang; Liu, Zhengshi; Wang, Yong

doi:10.1016/j.flowmeasinst.2019.101606

Cited by 2 publications

(2 citation statements)

References 20 publications

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“…Evaluation of the performance of the proposed models is ordinarily done by comparison of the model prediction with the real values by calculating the various statistical parameters, including average percent relative error (APRE), average absolute percent relative error (AAPRE), standard deviation (SD), root mean square error (RMSE), and coefficient of determination. These statistical parameters are obtained from the following Equations: where E i is the percent relative error and is stated based on the following formula 53 : …”

Section: Evaluation Of the Modelmentioning

confidence: 99%

Modeling liquid rate through wellhead chokes using machine learning techniques

Dabiri,

Hadavimoghaddam,

Ashoorian

et al. 2024

Sci Rep

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Precise measurement and prediction of the fluid flow rates in production wells are crucial for anticipating the production volume and hydrocarbon recovery and creating a steady and controllable flow regime in such wells. This study suggests two approaches to predict the flow rate through wellhead chokes. The first is a data-driven approach using different methods, namely: Adaptive boosting support vector regression (Adaboost-SVR), multivariate adaptive regression spline (MARS), radial basis function (RBF), and multilayer perceptron (MLP) with three algorithms: Levenberg–Marquardt (LM), bayesian-regularization (BR), and scaled conjugate gradient (SCG). The second is a developed correlation that depends on wellhead pressure (Pwh), gas-to-liquid ratio (GLR), and choke size (Dc). A dataset of 565 data points is available for model development. The performance of the two suggested approaches is compared with earlier correlations. Results revealed that the proposed models outperform the existing ones, with the Adaboost-SVR model showing the best performance with an average absolute percent relative error (AAPRE) of 5.15% and a correlation coefficient of 0.9784. Additionally, the results indicated that the developed correlation resulted in better predictions compared to the earlier ones. Furthermore, a sensitivity analysis of the input variable was also investigated in this study and revealed that the choke size variable had the most significant effect, while the Pwh and GLR showed a slight effect on the liquid rate. Eventually, the leverage approach showed that only 2.1% of the data points were in the suspicious range.

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Section: Evaluation Of the Modelmentioning

confidence: 99%

Modeling liquid rate through wellhead chokes using machine learning techniques

Dabiri,

Hadavimoghaddam,

Ashoorian

et al. 2024

Sci Rep

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show abstract

“…They are growing in the field of environmental resource management [18] and have many users for coastal and marine environments. These sensors can measure upwellings [79], oil spills [79], water quality, fish behaviour [80], and storm detection [81]. They can also be used as biosensors for monitoring marine contaminants [82] and many other applications [83].…”

Section: Rs and Pressuresmentioning

confidence: 99%

Contribution of Remote Sensing Technologies to a Holistic Coastal and Marine Environmental Management Framework: A Review

et al. 2020

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Coastal and marine management require the evaluation of multiple environmental threats and issues. However, there are gaps in the necessary data and poor access or dissemination of existing data in many countries around the world. This research identifies how remote sensing can contribute to filling these gaps so that environmental agencies, such as the United Nations Environmental Programme, European Environmental Agency, and International Union for Conservation of Nature, can better implement environmental directives in a cost-effective manner. Remote sensing (RS) techniques generally allow for uniform data collection, with common acquisition and reporting methods, across large areas. Furthermore, these datasets are sometimes open-source, mainly when governments finance satellite missions. Some of these data can be used in holistic, coastal and marine environmental management frameworks, such as the DAPSI(W)R(M) framework (Drivers–Activities–Pressures–State changes–Impacts (on Welfare)–Responses (as Measures), an updated version of Drivers–Pressures–State–Impact–Responses. The framework is a useful and holistic problem-structuring framework that can be used to assess the causes, consequences, and responses to change in the marine environment. Six broad classifications of remote data collection technologies are reviewed for their potential contribution to integrated marine management, including Satellite-based Remote Sensing, Aerial Remote Sensing, Unmanned Aerial Vehicles, Unmanned Surface Vehicles, Unmanned Underwater Vehicles, and Static Sensors. A significant outcome of this study is practical inputs into each component of the DAPSI(W)R(M) framework. The RS applications are not expected to be all-inclusive; rather, they provide insight into the current use of the framework as a foundation for developing further holistic resource technologies for management strategies in the future. A significant outcome of this research will deliver practical insights for integrated coastal and marine management and demonstrate the usefulness of RS to support the implementation of environmental goals, descriptors, targets, and policies, such as the Water Framework Directive, Marine Strategy Framework Directive, Ocean Health Index, and United Nations Sustainable Development Goals. Additionally, the opportunities and challenges of these technologies are discussed.

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Design of a three-dimensional current sensor with measuring upwelling

Cited by 2 publications

References 20 publications

Modeling liquid rate through wellhead chokes using machine learning techniques

Modeling liquid rate through wellhead chokes using machine learning techniques

Contribution of Remote Sensing Technologies to a Holistic Coastal and Marine Environmental Management Framework: A Review

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