An efficient MCMC history matching workflow using fit-for-purpose proxies applied in unconventional oil reservoirs

Abstract

Abstract History matching is a very challenging task because tremendous amount of simulations runs are required to achieve an acceptable accuracy target. In this paper, we develop and evaluate an assisted history matching (AHM) workflow using proxy-based Markov chain Monte Carlo (MCMC) algorithm, which is fully-automated and efficient for application in unconventional reservoirs. We use a synthetic simple case to compare the accuracy between four types of proxies: quadratic polynomial, cubic polynomial, k-nearest neighboring (KNN), and kriging. Also, we investigate the accuracy of proxy in different contexts such as different measurement errors and different quantity of initial simulation points. The results show that kriging proxy is more accurate than KNN proxy and cubic proxy. The quadratic proxy was the least accurate in our evaluations. However, if larger measurement error is introduced, the distinction between accuracy of proxy of the four proxies becomes less clear in spite of their different computational costs. The appropriate number of initial simulation points is another key to improve the AHM workflow efficiency. Incorporating these findings, we implement the AHM workflow on a synthetic field case and successfully perform history matching and uncertainty quantification.