Abstract
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.
Highlights
For reliable decision-making in the petroleum industry, reservoir characterization is implemented to estimate the distribution of reservoir parameters of interest
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
We propose a novel iterative static modeling scheme for channelized reservoirs, which have uncertainty in channel geometry
Summary
For reliable decision-making in the petroleum industry, reservoir characterization is implemented to estimate the distribution of reservoir parameters of interest. Conventional history matching is to characterize model parameters of interest, but updated models cannot preserve static information because inverse algorithms may ignore given hard data, soft data, and geological concept (Jafarpour and Khodabakhshi 2011; Hu et al 2013; Hou et al 2015; Satija et al 2017). To solve this problem, updated models from inverse modeling are used to generate pseudo-static data which are coupled with static data given to rebuild reservoir models. According to TI rejection and the soft data, three strategies are tested in two channelized reservoir cases to optimize the iterative static modeling
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