Artificial neural networks in predicting of the gas molecular diffusion coefficient

Wang, Xiuqing; Daryapour, Mahboobeh; Shahrabadi, Abbas; Pirasteh, Saied; Razavirad, Fatemeh

doi:10.1016/j.cherd.2023.10.035

Cited by 11 publications

(3 citation statements)

References 38 publications

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“…The MTGNN model attained an impressive 92% accuracy, demonstrating its efficacy in the spatiotemporal area of power transformer problem detection. In the context of non-temporal analysis within the field of crude oil, Wang et al [ 33 ] studied contemporary research, employing an ANN and a hybrid Multilayer Perceptron with Backpropagation for prediction. The model used 172 samples and a variety of characteristics to estimate diffusion coefficients, including temperature, pressure, liquid viscosity, gas viscosity, liquid molar volume, gas molar volume, liquid molecular weight, gas molecular weight, and interfacial tension.…”

Section: Predicted Analytics Models For Oandgmentioning

confidence: 99%

“…Some examples include ML techniques [ 22 , 23 , 24 ], ensemble techniques [ 25 , 26 ], soft computing techniques [ 27 , 28 ], statistical techniques [ 29 ], and fuzzy-based systems [ 30 ]. The effective application of AI in several O&G domains, such as gas [ 31 ], pipeline [ 32 ], crude oil [ 33 ], oxyhydrogen gas retrofit [ 34 ], and transformer oil [ 35 ], has received increased interest in the last few years.…”

Section: Introductionmentioning

confidence: 99%

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A Review of Predictive Analytics Models in the Oil and Gas Industries

R Azmi,

Yusoff,

Mohd Sallehud-din

2024

Sensors

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Enhancing the management and monitoring of oil and gas processes demands the development of precise predictive analytic techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern machine learning techniques. Several review articles detail the developments in predictive maintenance and the technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sectors. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry’s predictive analytical modeling. This review covers different forms of machine learning techniques used in predictive analytical modeling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, describing the model’s categories, the data’s temporality, field, and name, the dataset’s type, predictive analytics (classification, clustering, or prediction), the models’ input and output parameters, the performance metrics, the optimal model, and the model’s benefits and drawbacks. In addition, suggestions for future research directions to provide insights into the potential applications of the associated knowledge. This review can serve as a guide to enhance the effectiveness of predictive analytics models in the oil and gas industries.

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Section: Predicted Analytics Models For Oandgmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Review of Predictive Analytics Models in the Oil and Gas Industries

R Azmi,

Yusoff,

Mohd Sallehud-din

2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Some examples include ML techniques [19]- [21], ensemble techniques [22], [23], [22], [23], soft computing techniques [24], [25], statistical techniques [26], and fuzzy-based systems [27]. The effective application of AI in several O&G domains, such as gas [28], pipeline [29], crude oil [30], oxyhydrogen gas retrofit [31], and transformer oil [32], have increased interest in the last few years.…”

Section: Introductionmentioning

confidence: 99%

Predictive Analytics Performance on Oil and Gas: A Significant Review

R Azmi,

Yusoff,

Mohd Salledud-din

2024

Preprint

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Enhancing the management and monitoring of oil and gas processes demands developing precise predictive analytics techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern Machine Learning techniques. Several review articles detail the developments in predictive maintenance and technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sector. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry's predictive analytical modelling. The review covers forms of Machine Learning techniques used in predictive analytic modelling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, comprising of the model’s categories, the data's temporality, field, and name, the dataset's type, predictive analytics (classification or clustering or prediction), the models' input and output parameters, performance metrics, optimal model, and benefits and its drawbacks. Additionally, suggestions for future research directions are provided to raise the potential of the associated knowledge and increase the accuracy of oil and gas predictive analytics models.

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