S-Space College of Engineering/Engineering Practice School (공과대학/대학원) Dept. of Energy Systems Engineering (에너지시스템공학부) Journal Papers (저널논문_에너지시스템공학부)
Iterative static modeling of channelized reservoirs using history-matched facies probability data and rejection of training image
Cited 10 time in Web of Science Cited 11 time in Scopus
- Issue Date
- Springer Open
- Petroleum Science, pp.1-21
- History-matched facies probability map ; Training image rejection ; Iterative static modeling ; Channelized reservoirs ; Multiple-point statistics ; History matching
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.
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