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Research Papers: Petroleum Engineering

Initial Ensemble Design Scheme for Effective Characterization of Three-Dimensional Channel Gas Reservoirs With an Aquifer

[+] Author and Article Information
Sungil Kim, Hyungsik Jung

Department of Energy Systems Engineering,
Seoul National University,
Seoul 08826, South Korea

Kyungbook Lee

Petroleum and Marine Research Division,
Korea Institute of Geoscience and
Mineral Resources,
Daejeon 34132, South Korea

Jonggeun Choe

Department of Energy Systems Engineering,
Seoul National University,
Seoul 08826, South Korea
e-mail: johnchoe@snu.ac.kr

1Corresponding author.

Manuscript received August 1, 2016; final manuscript received December 9, 2016; published online January 16, 2017. Editor: Hameed Metghalchi.

J. Energy Resour. Technol 139(2), 022911 (Jan 16, 2017) (10 pages) Paper No: JERT-16-1314; doi: 10.1115/1.4035515 History: Received August 01, 2016; Revised December 09, 2016

Reservoir characterization is a process of making models, which reliably predict reservoir behaviors. Ensemble Kalman filter (EnKF) is one of the fine methods for reservoir characterization with many advantages. However, it is hard to get trustworthy results in discrete grid system ensuring preservation of channel properties. There have been many schemes such as discrete cosine transform (DCT) and preservation of facies ratio (PFR) for improvement of channel reservoirs characterization. These schemes are mostly applied to 2D cases, but cannot present satisfactory results in 3D channel gas reservoirs with an aquifer because of complex production behaviors and high uncertainty of them. For a complicated 3D channel reservoir, we need reliable initial ensemble members to reduce uncertainty and stably characterize reservoir models due to the assumption of EnKF, which regards the mean of ensemble as true. In this study, initial ensemble design scheme is suggested for EnKF. The reference 3D channel gas reservoir system has 200 × 200 × 5 grid system (250 × 250 × 100 ft for x, y, and z, respectively), 15% porosity, and two facies of 100 md sand and 1 md shale. As the first step, it samples initial ensemble members, which show similar water production behaviors with the reference. Then, grid points are randomly selected for high and low 5% from the mean of sampled members. As a final step, initial ensemble members are remade using the selected data, which are assumed as additional known data. This proposed method reliably characterizes 3D channel reservoirs with an aquifer.

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Figures

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Fig. 1

(a) Procedure of initial ensemble design scheme and (b) examples according to the steps

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Fig. 2

An example of DCT application to a reservoir model

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Fig. 3

Entire procedure of the combination of IEDS, DCT, and PFR with EnKF

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Fig. 4

Conditions for the reference and initial permeability field with channels: (a) training image, (b) known data of the first layer, (c) reference field, layer L1–L3, and (d) three samples of initial ensemble, L1 of each

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Fig. 5

Gas rate predictions of the initial and updated ensemble models: (a) initial ensemble, (b) DCT and PFR, (c) initial ensemble by IEDS, and (d) the proposed method

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Fig. 6

Water rate predictions of the initial and updated ensemble models: (a) initial ensemble, (b) DCT and PFR, (c) initial ensemble by IEDS, and (d) the proposed method

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Fig. 7

Total gas and water productions of the initial and updated ensemble models: (a) initial ensemble, (b) DCT and PFR, (c) initial ensemble by IEDS, and (d) the proposed method

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Fig. 8

The initial and final updated permeability in sequence of L1–L3 and its histogram: (a) reference, (b) initial ensemble, (c) updated by DCT and PFR, (d) initial ensemble by IEDS, and (e) updated by DCT, PFR, and IEDS

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Fig. 9

Comparison of initial reservoir models by a conventional design and IEDS. One reservoir model consists of three layers of L1–L3. The three models with the box consist of one ensemble member: (a) ten examples of initial ensemble and (b) ten examples of initial ensemble by IEDS.

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Fig. 10

Characterization results of aquifer parameter (MULTPV) and their RMSE: (a) MULTPVs of the initial ensemble, (b) updated MULTPV by DCT and PFR, and (c) updated MULTPVs by the proposed method

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