TY - JOUR
T1 - Wax deposition and prediction in petroleum pipelines
AU - Alnaimat, Fadi
AU - Ziauddin, Mohammed
N1 - Funding Information:
The authors acknowledge financial support received from the UAE University from Grant no. 31N265 , and Grant no. 31R168 .
Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2020/1
Y1 - 2020/1
N2 - Wax prediction is crucial because accumulation of wax in the crude oil pipelines is a wide concern in the petroleum industry. In the subsea pipelines, the cold temperature of the pipeline walls causes the wax particles in the oil to crystalize over time and deposit on the inner surface. It can even cause a complete blockage of pipe affecting the supply. The process of cleaning the wax buildup is called pigging in which the shutdown and maintenance cost is extremely prohibitive. Wax prediction can assist in avoiding such blockage. Hence, it becomes crucial to monitor the wax deposition thickness to determine when cleaning process is necessary or not and avoid blockage issue. This article presents various wax prediction methods that are implemented to estimate the wax deposition.
AB - Wax prediction is crucial because accumulation of wax in the crude oil pipelines is a wide concern in the petroleum industry. In the subsea pipelines, the cold temperature of the pipeline walls causes the wax particles in the oil to crystalize over time and deposit on the inner surface. It can even cause a complete blockage of pipe affecting the supply. The process of cleaning the wax buildup is called pigging in which the shutdown and maintenance cost is extremely prohibitive. Wax prediction can assist in avoiding such blockage. Hence, it becomes crucial to monitor the wax deposition thickness to determine when cleaning process is necessary or not and avoid blockage issue. This article presents various wax prediction methods that are implemented to estimate the wax deposition.
KW - Neural networks
KW - Non-destructive testing
KW - Single phase flow
KW - Wax appearance temperature
KW - Wax deposition
KW - Wax prediction
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U2 - 10.1016/j.petrol.2019.106385
DO - 10.1016/j.petrol.2019.106385
M3 - Review article
AN - SCOPUS:85071363790
SN - 0920-4105
VL - 184
JO - Journal of Petroleum Science and Engineering
JF - Journal of Petroleum Science and Engineering
M1 - 106385
ER -