Iterative static modeling of channelized reservoirs using history-matched facies probability data and rejection of training image

Petroleum Science

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[1] 0000 0001 0436 1602, grid.410882.7, Petroleum and Marine Research Division, Korea Institute of Geoscience and Mineral Resources, 34132, Daejeon, South Korea;0000 0001 2171 7754, grid.255649.9, Department of Climate and Energy Systems Engineering, Division of Sustainable Systems Engineering, Ewha Womans University, 03760, Seoul, South Korea;0000 0004 0470 5905, grid.31501.36, Department of Energy Systems Engineering, Seoul National University, 08826, Seoul, South Korea;

关键词: History-matched facies probability map;    Training image rejection;    Iterative static modeling;    Channelized reservoirs;    Multiple-point statistics;    History matching;   

DOI  :  10.1007/s12182-018-0254-x

来源: publisher

【 摘 要 】

Most inverse reservoir modeling techniques require many forward simulations, and the posterior models cannot preserve geological features of prior models. This study proposes an iterative static modeling approach that utilizes dynamic data for rejecting an unsuitable training image (TI) among a set of TI candidates and for synthesizing history-matched pseudo-soft data. The proposed method is applied to two cases of channelized reservoirs, which have uncertainty in channel geometry such as direction, amplitude, and width. Distance-based clustering is applied to the initial models in total to select the qualified models efficiently. The mean of the qualified models is employed as a history-matched facies probability map in the next iteration of static models. Also, the most plausible TI is determined among TI candidates by rejecting other TIs during the iteration. The posterior models of the proposed method outperform updated models of ensemble Kalman filter (EnKF) and ensemble smoother (ES) because they describe the true facies connectivity with bimodal distribution and predict oil and water production with a reasonable range of uncertainty. In terms of simulation time, it requires 30 times of forward simulation in history matching, while the EnKF and ES need 9000 times and 200 times, respectively.

【 授权许可】

CC BY