Research Papers: Petroleum Engineering

Regeneration of Initial Ensembles With Facies Analysis for Efficient History Matching

[+] Author and Article Information
Byeongcheol Kang

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

Jonggeun Choe

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

1Corresponding author.

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received November 17, 2016; final manuscript received March 24, 2017; published online April 17, 2017. Assoc. Editor: Egidio Marotta.

J. Energy Resour. Technol 139(4), 042903 (Apr 17, 2017) (11 pages) Paper No: JERT-16-1463; doi: 10.1115/1.4036382 History: Received November 17, 2016; Revised March 24, 2017

Reservoir characterization is needed for estimating reservoir properties and forecasting production rates in a reliable manner. However, it is challenging to figure out reservoir properties of interest due to limited information. Therefore, well-designed reservoir models, which reflect characteristics of a true field, should be selected and fine-tuned. We propose a novel scheme of generating initial reservoir models by using static data and production history data available. We select representative reservoir models by projecting reservoir models onto a two-dimensional (2D) plane using principal component analysis (PCA) and calculating errors of production rates against observed data. These selected models, which will have similar geological properties with the reference, are used to regenerate models by perturbing along the boundary of the different facies. These regenerated models have all the different facies distributions but share principal characteristics based on the selected models. We compare cases using 400 ensemble members, 100 models with unbiased uniform sampling, and 100 regenerated models by the proposed method. We analyze two synthetic reservoirs with different permeability distributions: one is a typical heterogeneous reservoir and the other is a channel reservoir with a bimodal permeability distribution. Compared to the cases using all the 400 models with ensemble Kalman filter (EnKF), the simulation time is dramatically reduced to 4.7%, while the prediction quality on oil and water productions is improved. Even in the more complex reservoir case, the proposed method shows great improvements with reduced uncertainties against the other cases.

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Fig. 1

Example dataset of PCA process: (a) dataset in 3D space and (b) data in the PCA-assisted 2D

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Fig. 2

Reservoir models in this study with different channel geometries in PCA plane

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Fig. 3

Sampling procedures in this study using PCA and K-means clustering: (a) 400 models in PCA-assisted 2D plane, (b) candidate models (thick dot) and the selected model for the lowest RMSE (cross in the circle), and (c) the five representative models for model regeneration

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Fig. 4

Procedures of the proposed model generation scheme: (a) selected channel reservoir model with two facies, (b) nine subgrid sections of the model for perturbation, (c) perturbation results of sand fraction for each subgrid section, and (d) nine examples of regenerated reservoir models

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Fig. 5

Information on the reference reservoir model: (a) positions and permeability data used and (b) reference reservoir model and the well positions

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Fig. 6

Five representative ensemble members selected (left) and their permeability histogram (right)

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Fig. 7

Ten reservoir models regenerated using the representative members

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Fig. 8

Performances in wells P2, P4, P7, and P8 of each case: (a) oil production rates and (b) watercuts

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Fig. 9

The boxplots of total production at the 1000th day of each case. Values are normalized using the reference performance. (a) oil and (b) water.

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Fig. 10

Information on the reference model in channel reservoir case: (a) static data used (left) and the training image (right) and (b) reference field and the well positions

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Fig. 11

Five reservoir models selected (left) and their permeability histograms (right)

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Fig. 12

Ten examples of regenerated ensemble members in the channel reservoir case

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Fig. 13

Performances in wells P2, P4, P5, and P7 of each case in the channel reservoir case: (a) oil production rates and (b) watercuts

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Fig. 14

Boxplots of total production at the 1000th day in the channelized reservoir case: (a) oil and (b) water




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