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Estimating oil reservoir permeability and porosity from two interacting wells
Sutawanira, Gunawan A.Y.a, Fitriyati N.b, Fahmi I.a, Septiani A.a, Marwati R.c
a Statistics Research Division Institut Teknologi Bandung Indonesia, Indonesia
b Mathematics Department, UIN Syarif Hidayatullah, Indonesia
c Universitas Pendidikan Indonesia Bandung Indonesia, 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 Ensemble Kalman Filter (EnKF) can be used as a method to estimate reservoir parameters, such as permeability and porosity. These parameters play an important role in characterizing reservoir performance. The EnKF is a sequential estimation method that uses the parameters at t – 1 (called prior) to estimate the parameters at t adjusted by observations at t (called posterior). In this paper, the EnKF was used to estimate the reservoir parameters for the case of a linear flow of two interacting production-injection oil wells. The Laplace transform was used to obtain an analytical solution of the diffusivity equation. A state space representation was generated using the analytical solution. A simulation study showed that the proposed method can be used successfully to estimate the reservoir parameters using well-pressure observations. © 2013 Published by ITB Journal Publisher.[/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]Ensemble kalman filter,Flow model,Interacting well,Laplace transform,Sequential 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/10.5614/j.math.fund.sci.2013.45.2.4[/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]