Application of proxy-based MCMC and EDFM to history match a shale gas condensate well

History matching is an important process for uncertainty quantification of reservoir performance. Hundreds of reservoir simulation cases, or more, are typically required to sample the posterior probability density function of the uncertain parameters. The process is extremely challenging for shale reservoirs due to large uncertainties including reservoir heterogeneity, wide range of permeability, gas desorption, and fracture properties. Direct application of stimulated rock volume or dual continuum model fails to capture fracture connectivity and fracture conductivity that dominate the fluid transport in most shale developments. Embedding discrete fractures in reservoir simulation model is thus a preferable method to attain more realistic reservoir behavior. However, the option of using local grid refinement to embed the fractures is computationally expensive as well as intrusive to a reservoir model. Even more challenging is generating a huge set of fracture-embedded reservoir cases during the history matching process. Nevertheless, recent developments in a methodology called Embedded Discrete Fracture Model (EDFM) have successfully overcome the computational complexity to include discrete fractures in reservoir simulations. We demonstrate the implementation of the assisted history matching workflow coupled with EDFM to history match a real field case of shale gas condensate reservoir in Duvernay Shale, Canada. In the workflow, the EDFM preprocessor is fully coupled with a commercial reservoir simulator and proxy-based Markov chain Monte Carlo (MCMC) algorithm. Moreover, to further reduce the computational cost, the single-fractured submodel is used to represent the full reservoir model for this case study. Using the proposed assisted history matching workflow and the appropriate submodel enables efficient and successful uncertainty quantification of this shale play.