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A wax deposition test device was designed to investigate the anti-wax property of different surfaces.
Prediction of asphaltene deposition test results, gathered by ANN, is much time and cost saving than the conventional experimental studies.
Present study describes an innovative method for easy and fast prediction of the asphaltene deposition test by use of artificial neural network (ANN).
After wax deposition test, the conversion coating has low wax deposition weight which is 2.9 mg/cm2 and high wax deposition reduction rate (80% in average).
An experimental campaign has been performed in the wax deposition test rig at Statoil's Research Center Porsgrunn: a fluid sample of a North Sea gas condensate (obtained during the well clean-up) was used for wax deposition tests with increasing concentrations of a commercial wax inhibitor.
Thus, one additional isotopic composition was determined for both the 'single' and 'double' deposition test samples.
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A steady state condition was selected to run the engine for the deposition tests.
Drop deposition tests are performed on the obtained surfaces following two methods.
To this end laboratory-scale deposition tests have been useful to understand wax deposition.
Recently, several dynamic and static asphaltene deposition tests were conducted by Kord et al. [4] in which live oil and core samples from target reservoirs were used.
The main limit of the available models is that their predictions depend on a number of parameters which are usually adjusted to fit the experimental data obtained from laboratory deposition tests.
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