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Estimation of Oil Recovery for Hydrocarbon Injection EOR Method using a Newly-Developed Predictive Model
Merdeka M.G.a, Syihab Z.a, Rahmawati S.D.a, Gaol A.H.L.a, Yasutra A.a, Setiawan M.B.a, Metra F.a, Winata D.T.b, Sitompul V.b, Aziz M.C.b, Buhari A.b
a Institut Teknologi Bandung, Bandung, Indonesia
b Pertamina Research and Technology Center, Jakarta, 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]© 2020 IOP Publishing Ltd. All rights reserved.Application of predictive model in enhanced oil recovery has been mainly to obtain an estimate of production performance. The advantage of a predictive model over reservoir simulation are the computational speed and its significantly lower cost. A predictive model is able to provide results instantly, comparing to the whole process of reservoir simulation. However, it should be noted that predictive model is not a substitute for reservoir simulation, but rather as a starting approach for field development planning purposes. Hydrocarbon injection is an EOR method which incorporates the injection of hydrocarbon gas to increase oil production. Presently, there has been little to none predictive models developed to estimate oil production for hydrocarbon injection method. In this study, a predictive model of the hydrocarbon injection method is presented. The predictive model was developed using commercial software CMG. Based on an inverted 5-spot reservoir model, thousands of reservoir simulation cases with different parameter values were conducted as a sensitivity analysis. Cumulative oil production result was captured from each simulation case to build the predictive model. Polynomial regression and neural network predictive models were built. The neural network model fitted the simulation data better than the regression model. R-square value for both predictive models exceeded 90%. This predictive model can be confidently used to estimate cumulative oil production as long as the input parameter values are within the parameter intervals of the model.[/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]Commercial software,Cumulative oil production,Enhanced oil recovery,Neural network model,Parameter intervals,Predictive modeling,Production performance,Reservoir simulation[/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]The authors would like to acknowledge Pertamina RTC for funding this research and Petroleum Engineering Department of Bandung Institute of Technology for providing the facilities.[/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/10.1088/1755-1315/519/1/012048[/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]