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An investigation on gas lift performance curve in an oil-producing well
Saepudin D.a,b, Soewono E.a, Sidarto K.A.a, Gunawan A.Y.a, Siregar S.a, Sukarno P.a
a Industrial and Financial Mathematics Group, Institut Teknologi Bandung, Indonesia
b Departement Sains, Sekolah Tinggi Teknologi Telkom, 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]The main objective in oil production system using gas lift technique is to obtain the optimum gas injection rate which yields the maximum oil production rate. Relationship between gas injection rate and oil production rate is described by a continuous gas lift performance curve (GLPC). Obtaining the optimum gas injection rate is important because excessive gas injection will reduce production rate, and also increase the operation cost. In this paper, we discuss a mathematical model for gas lift technique and the characteristics of the GLPC for a production well, for which one phase (liquid) is flowing in the reservoir, and two phases (liquid and gas) in the tubing. It is shown that in certain physical condition the GLPC exists and is unique. Numerical computations indicate unimodal properties of the GLPC. It is also constructed here a numerical scheme based on genetic algorithm to compute the optimum oil production. © 2007 Deni Saepudin et al.[/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][/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][/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/10.1155/2007/81519[/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]