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Scale Build-Up Prediction of FeS and FeCO3 in Gas Production Pipes
Santoso R.K.a, Rahmawati S.D.a, Gadesa A.a, Wahyuningrum D.a
a Petroleum Engineering Study Program, Institut Teknologi Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© Published under licence by IOP Publishing Ltd.The existence of passive layer is important to control the corrosion-erosion rate in gas production pipes. However, passive layer can lead to two possible conditions: corrosion-erosion protection or scale build-up. Accurate prediction of scaling tendency is needed to plan treatment and operating condition during the production from gas field. In this study, we develop mathematical model to predict the scaling tendency in gas production pipes. The model consists of two basic equations: precipitation rate and erosion equation. Precipitation rate is calculated using semi-empirical correlation and erosion is calculated using Salama (2000) equation. Then, a modified parameter of scaling tendency (ST), which is the ratio between net precipitation rate and corrosion rate, is introduced to measure the scaling tendency in each segment of production pipe. From simulation, it was found that the interaction between pressure, temperature and fluid composition affected the scaling tendency at most. However, when sand was introduced in the pipe flow, scale formation occurred at low rate. Every segment of production tubing and pipeline gave different tendency condition.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Accurate prediction,Corrosion erosions,Modified parameters,Operating condition,Precipitation rates,Production tubing,Scaling tendencies,Semi-empirical correlation[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]This research was funded by DIKTI 2016.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1088/1742-6596/877/1/012029[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]