Prediction of Hydrate Formation in Natural Gas Systems

Rossi, Lorenzo; Gasparetto, Carlos Alberto

doi:10.2118/22715-ms

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…Sloan 18 has quoted most of the experimental data on hydrate formation and inhibition, which are sparsely reported in the open literature. The data used in the present study have been obtained from Sloan, Munck et al, Englezos and Bishnoi, Stange et al, Song and Kobayashi, Notz et al, Rossi and Gasparetto, Englezos, , Lingelem and Majeed, Hight, Corrigan et al, Tohidi et al, − Hutz and Englezos . The data cover a wide range of system compositions and hydrate formation and inhibition conditions, as shown in Table .…”

Section: Optimal Injection Modelmentioning

confidence: 99%

Optimal Hydrate Inhibition Policies with the Aid of Neural Networks

Elgibaly

Elkamel

1998

Energy Fuels

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Hydrates are known to occur in a variety of natural-gas handling facilities and processing equipment in oil fields, refineries, and chemical plants when natural gas and water coexist at elevated pressure and reduced temperature. Prevention of hydrate formation costs large amounts of capital and results in large operating expenses. Hydrate inhibition using chemical inhibitors is still the most widely used method. Accurate prediction of hydrate inhibition is required for cost-effective design and operation. Available models have limitations in ranges of application and types and compositions of the fluids and inhibitors used. This paper describes the development and application of neural networks for the prediction and optimization of naturalgas hydrate inhibition. Neural network models have been used to accurately determine the temperature depression of gas hydrates for a variety of types and concentrations of inhibitors. Experimental data covering wide ranges of hydrate formation conditions, gas compositions, and concentrations of various types of inhibitors have been used in model validation. The factors that may affect the inhibition process, such as gas gravity and pressure, were investigated. An optimization study has been carried out on the selection of inhibitor type and concentration using the developed neural network models. Optimization was based on economical and technical performance considerations concerning inhibitor losses in vapor and liquid hydrocarbons. The results indicate that optimal design depends on water content, operating conditions of pressure and temperature, and gas composition. Optimized hydrate inhibition strategies have been recommended for various gas composition systems.

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Section: Optimal Injection Modelmentioning

confidence: 99%

Optimal Hydrate Inhibition Policies with the Aid of Neural Networks

Elgibaly

Elkamel

1998

Energy Fuels

View full text Add to dashboard Cite

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“…The problem of pigging in export plants reveals a significant wall thickness loss up to about 36% at some points in pipeline systems. [4], [6], [7] Presence of water in these systems in reaction with CO 2 can cause corrosion. Routine pigging assessment showed that 4 -9% of the liquid recovered from plant in the Niger Delta spur of the gas transmission system (GTS) as free water.…”

Section: Introductionmentioning

confidence: 99%