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Reserve estimation
Sutawanira, Gunawan A.Y.a, Permadi A.K.a, Fitriyati N.a
a Statistic Research Division, 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]Estimation of recoverable reservoir reserve should be as accurate as possible. Detailed reservoir parameter estimation is needed to ensure good estimate. Many uncertainties exist in recovery estimation due to reservoir rock properties such as permeability and porosity. The estimate of such parameters may change with the available observations and time. This paper reports the use of Bayesian ensemble Kalman filter (EnKF) to estimate the permeability. It is a method in which a sample of state are generated and updated sequentially. Two simulated experiments are presented to investigate the applicability of EnKF. The first concern is using EnKF for parameter estimation in reservoir models such as line source, bounded reservoir, 1D flow numerical model. The second concern is using EnKF in one dimensional two interacting wells. The solutions are derived analytically. The state consists of the parameters and the analytical solution. The estimation process consists of two steps: forecast and update. Forecast step is calculated by using forward function which can be analytical or numerical solution. Update step uses Kalman filter scheme where the gain is calculated from sample covariance. Asymptotic studies as the basis for the validity of EnKF is well established and summarized in this report. The study concludes that EnKF can provide satisfactory results for reservoir reserve parameter estimation. Parameter estimation for reservoir simulation is an important future issue in order to improve the forecast. © 2014 AIP Publishing LLC.[/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]Bayesian estimate,Ensemble Kalman Filter,Flow numerical models,Fluid displacement,Permeability and porosities,Reservoir rock properties,Reservoir simulation,Simulated experiments[/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]Bayesian estimate,fluid displacement,reserve estimation[/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/10.1063/1.4868837[/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]