Application of assisted-history-matching workflow using proxy-based MCMC on a shale oil field case

History matching is a crucial step for reservoir simulation and for decision-making process in field development under uncertainty. The history-matching task is well known to be technically and computationally challenging. Several algorithms have been studied for more than a decade to assist history matching. One of the algorithms used is Markov chain Monte Carlo (MCMC), which is capable of providing accurate posterior probability density (PPD) of the history-matched realizations. While several researchers have applied and studied MCMC for assisted history matching (AHM) in many conventional reservoirs, only few studies have been performed on unconventional reservoirs. Since the difference in physics of the two reservoir types are important, it is worthwhile investigating the performance of AHM in unconventional reservoirs. For this purpose, we apply an AHM workflow using proxy-based MCMC on a shale oil well in Vaca Muerta formation to demonstrate application of the workflow and highlight the lessons learnt. The direct MCMC is also performed on the same field case to compare accuracy and efficiency of the first method. In this study, design of experiment (DOE) is used for selecting the most influential uncertain parameters before performing either of the two MCMC methods. It is found that the direct MCMC cannot find enough solutions to construct the statistically meaningful PPD in an efficient manner. By contrast, the proxy-based MCMC is less computationally demanding than the direct MCMC and efficient enough to construct the PPD. The tested workflow was then used to probabilistically forecast the cumulative oil and water production as well as the oil recovery factor for the Vaca Muerta well.