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

论文摘要

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.

论文目录

• 1 Introduction
• 2 Methodology
•   2.1 Procedure of the proposed method
•   2.2 Three strategies for iterative static modeling
•   2.3 Synthetic channelized reservoir cases
•   2.4 Verification of the distance-based method
• 3 Example A
•   3.1 Characterization of channel connectivity
•   3.2 Uncertainty quantification in productionforecasts
• 4 Example B
•   4.1 Characterization of channel connectivity
•   4.2 Uncertainty quantification in productionforecasts
• 5 Conclusions

文章来源

类型: 期刊论文

作者: Kyungbook Lee,Sungil Kim,Jonggeun Choe,Baehyun Min,Hyun Suk Lee

来源: Petroleum Science 2019年01期

年度: 2019

分类: 工程科技Ⅰ辑

专业: 石油天然气工业

单位: Petroleum and Marine Research Division, Korea Institute of Geoscience and Mineral Resources,Department of Climate and Energy Systems Engineering,Division of Sustainable Systems Engineering, Ewha Womans University,Department of Energy Systems Engineering, Seoul National University

基金: supported by Korea Institute of Geoscience and Mineral Resources(Project No.GP2017-024),Ministry of Trade and Industry [Project No.NP2017-021(20172510102090)],funded by National Research Foundation of Korea(NRF)Grants(Nos.NRF-2017R1C1B5017767,NRF-2017K2A9A1A01092734)

分类号: TE319

页码: 127-147

总页数: 21

文件大小: 3670K

下载量: 6