Design and Surge Study of Salaya Mathura Pipeline for Higher Throughput of Crude Oil Transportation

Sahith, S. Jai Krishna; Rao, K. Venkateswara; Rao, P. Srinivasa

doi:10.1016/j.matpr.2017.12.134

Cited by 5 publications

(2 citation statements)

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“…Especially with the condition of extensive severe haze weather in recent years in China, it is particularly important to seek ways to solve or reduce the carbon emission problem of traditional transportation [6]. Among them, the development of new modes of transportation, such as pipeline transportation, is one effective way to ease transportation tension and reduce carbon emissions [7,8].…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation of Flow Velocity Characteristics during Capsule Hydraulic Transportation in a Horizontal Pipe

Sun

et al. 2020

Water

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Capsule hydraulic transportation is a kind of low-carbon and environmentally friendly pipeline transportation technique. In this study, the flow velocity characteristics in the pipeline when the capsule is transported in a straight pipe section were simulated by adopting the RNG (Renormalization Group) k–ε turbulence model based on Fluent software and experimentally verified. The results showed that the simulated value of flow velocity in the pipeline was basically consistent with the experimental value during transportation of the material by the capsule, and the maximum relative error was no more than 6.7%, proving that it is feasible to use Fluent software to simulate the flow velocity characteristics in the pipeline when the capsule is transported in a straight pipe section. In the process of material transportation, the flow velocity distribution of the cross-section near the upstream and downstream sections of the capsule was basically the same, which increased with the increased length–diameter ratio of the capsule. The axial flow velocity was smaller in the middle of the pipe and larger near the inner wall of the pipe. From the inner wall to the center of the pipe, the radial flow velocity first increased and then decreased. The circumferential flow velocity was distributed in the vicinity of the support body of the capsule. The axial flow velocity of the annular gap section around the capsule first increased and then decreased from the inner wall of the pipe to the outer wall of the capsule. In the process of transporting materials, the influence of the capsule on the flow of its downstream section was greater than that of its upstream section. These results could provide a theoretical basis for optimizing the technical parameters of capsule hydraulic transportation.

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Section: Introductionmentioning

confidence: 99%

Numerical Simulation of Flow Velocity Characteristics during Capsule Hydraulic Transportation in a Horizontal Pipe

Sun

et al. 2020

Water

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“…Multiphase flow is come across in various domains. In the oil & gas and chemical processing industry, the multiphase flow has been of utmost importance, especially about the challenges that come across due to its occurrence of it in the flow lines [2][3][4] . The frequent transportation of natural gas is via subterranean pipelines, and immense attention has to be given to the safety of the gas transportation system.…”

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confidence: 99%

Experimental investigation and ANN modelling on CO2 hydrate kinetics in multiphase pipeline systems

Shaik

Sayani

Benjapolakul

et al. 2022

Sci Rep

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Gas hydrates are progressively becoming a key concern when determining the economics of a reservoir due to flow interruptions, as offshore reserves are produced in ever deeper and colder waters. The creation of a hydrate plug poses equipment and safety risks. No current existing models have the feature of accurately predicting the kinetics of gas hydrates when a multiphase system is encountered. In this work, Artificial Neural Networks (ANN) are developed to study and predict the effect of the multiphase system on the kinetics of gas hydrates formation. Primarily, a pure system and multiphase system containing crude oil are used to conduct experiments. The details of the rate of formation for both systems are found. Then, these results are used to develop an A.I. model that can be helpful in predicting the rate of hydrate formation in both pure and multiphase systems. To forecast the kinetics of gas hydrate formation, two ANN models with single layer perceptron are presented for the two combinations of gas hydrates. The results indicated that the prediction models developed are satisfactory as R2 values are close to 1 and M.S.E. values are close to 0. This study serves as a framework to examine hydrate formation in multiphase systems.

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Development of a Prediction Model for Gas Hydrate Formation in Multiphase Pipelines by Artificial Intelligence

et al. 2022

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A prediction model is developed by means of artificial neural networks (ANNs) to determine the gas hydrate formation kinetics in multiphase gas dominant pipelines with crude oil. Experiments are conducted to determine the rate of formation and reaction kinetics of hydrates formation in multiphase systems. Based on the results, an artificial intelligence model is proposed to predict the gas hydrate formation rate in multiphase transmission pipelines. Two ANN models are suggested with single-layer perceptron (SLP) and multilayer perceptron (MLP). The MLP shows more accurate prediction when compared to SLP. The models were predicted accurately with high prediction accuracy both for the pure and multiphase systems.

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Design and Surge Study of Salaya Mathura Pipeline for Higher Throughput of Crude Oil Transportation

Cited by 5 publications

References 0 publications

Numerical Simulation of Flow Velocity Characteristics during Capsule Hydraulic Transportation in a Horizontal Pipe

Numerical Simulation of Flow Velocity Characteristics during Capsule Hydraulic Transportation in a Horizontal Pipe

Experimental investigation and ANN modelling on CO2 hydrate kinetics in multiphase pipeline systems

Development of a Prediction Model for Gas Hydrate Formation in Multiphase Pipelines by Artificial Intelligence

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