Quantification of Uncertainty in Reservoir Connectivity for Field Test Evaluation

Production forecasts for petroleum reservoirs are essentially uncertain due to the lack of data. The unknown parameters are calibrated so that the simulated profile can match the observed data. A Bayesian framework has been applied to the evaluation of CO2 injection test in a tight oil reservoir. The observed data used for history-matching include the bottom-hole flowing pressure at the injector well and the gas composition at the wellhead of the producer wells. The key is starting with a simple model, because it is much quicker to adjust large-scale heterogeneity in a simple model than in a detailed model. The in-place volumes and connectivity between the wells have been calibrated in the simple models using a stochastic sampling method called the Neighbourhood Approximation algorithm. The aim of our study is to quantify uncertainty of reservoir connectivity. A Bayesian framework along with Markov Chain Monte Carlo and Neighbourhood Approximation in parameter space is used to calculate the posterior probability. We showed the best fit model for the gas breakthrough and the P10-90 envelopes in the forecast of the CO2 mole fraction in the produced gas. Our results contribute to the evaluation of the pilot test for a continuous CO2 injection.