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Modeling multiphase solid transport velocity in long subsea tiebacks : numerical and experimental methods

Transportation of unprocessed multiphase reservoir fluids from deep/ultra deep offshore through a long subsea tieback/pipeline is inevitable. This form of transportation is complex and requires accurate knowledge of critical transport velocity, flow pattern changes, phase velocity, pressure drop, particle drag & lift forces, sand/liquid/gas holdup, flow rate requirement and tieback sizing etc at the early design phase and during operation for process optimisation. This research investigated sand transport characteristics in multiphase, water‐oil‐gas‐sand flows in horizontal, inclined and vertical pipes. Two critical factors that influence the solid particle transport in the case of multiphase flow in pipes were identified; these are the transient phenomena of flow patterns and the characteristic drag & lift coefficients ( D C , L C ). Therefore, the equations for velocity profile were developed for key flow patterns such as dispersed bubble flow, stratified flow, slug flow and annular flow using a combination of analytical equations and numerical simulation tool (CFD). The existing correlations for D C & L C were modified with data acquired from multiphase experiment in order to account for different flow patterns. Minimum Transport Velocity (MTV) models for suspension and rolling were developed by combining the numerically developed particle velocity profile models with semi‐empirical models for solid particle transport. The models took into account the critical parameters that influence particle transport in pipe flow such as flow patterns and particle drag & lift coefficients, thus eliminate inaccuracies currently experienced with similar models in public domain. The predictions of the proposed MTV models for suspension and rolling in dispersed bubble, slug flow and annular flow show maximum average error margin of 12% when compared with experimental data. The improved models were validated using previously reported experimental data and were shown to have better predictions when compared with existing models in public domain. These models have the potential to solve the problems of pipe and equipment sizing, the risk of sand deposition and bed formation, elimination of costs of sand unloading, downtime and generally improve sand management strategies.

Identiferoai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:758164
Date January 2013
CreatorsBello, Kelani
ContributorsOyeneyin, Babs ; Steel, John A. ; Oluyemi, Gbenga Folorunso
PublisherRobert Gordon University
Source SetsEthos UK
Detected LanguageEnglish
TypeElectronic Thesis or Dissertation
Sourcehttp://hdl.handle.net/10059/3138

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