Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.691298
Title: Hydrate mitigation in sour and acid gases
Author: Hajiw, Martha
ISNI:       0000 0004 4863 5160
Awarding Body: Heriot-Watt University
Current Institution: Heriot-Watt University
Date of Award: 2015
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Abstract:
While global demand for energy is increasing, it is mostly covered by fossil energies, like oil and natural gas. Principally composed of hydrocarbons (methane, ethane, propane ...), reservoir fluids contain also impurities such as carbon dioxide, hydrogen sulphide and nitrogen. To meet the request of energy demand, oil and gas companies are interested in new gas fields, like reservoirs containing high concentrations of acid gases. Natural gas transport is done under high pressure and these fluids are also saturated with water. These conditions are favourable to hydrates formation, leading to pipelines blockage. To avoid these operational problems, thermodynamic inhibitors, like methanol or ethanol, are injected in lines. It is necessary to predict with more accuracy hydrates boundaries in different systems to avoid their formation in pipelines for example, as well as vapour liquid equilibria (VLE) in both sub-critical regions. Phase equilibria predictions are usually based on cubic equations of state and applied to mixtures, mixing rules involving the binary interaction parameter are required. A predictive model based on the group contribution method, called PPR78, combined with the Cubic – Plus – Association (CPA) equation of state has been developed in order to predict phase equilibria of mixtures containing associating compounds, such as water and alcohols. To complete database for multicomponent systems with acid gases, VLE and hydrate dissociation point measurements have been conducted. The developed model, called GC-PR-CPA, has been validated for binary systems and applied for different multicomponent mixtures. Its ability to predict hydrate stability zone and mixing enthalpies has also been tested. It has been found that the model is generally in good agreement with experimental data.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.691298  DOI: Not available
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