Prediction under uncertainty in reservoir modeling

Reservoir simulation is routinely employed in the prediction of reservoir performance under different depletion and operating scenarios. Usually, a single history-matched model, conditioned to production data, is obtained. The model is then used to forecast future production profiles. Because the history match is non-unique, the forecast production profiles are therefore uncertain, although this uncertainty is not usually quantified. This paper presents a new approach for generating uncertain reservoir performance predictions and quantifying the uncertainty associated with forecasting future performance. Firstly, multiple reservoir realizations are generated using a new stochastic algorithm. This involves adaptively sampling the model parameter space using an algorithm, which biases the sampling towards regions of good fit. Using the complete ensemble of models generated, the posterior distribution is resampled in order to quantify the uncertainty associated with forecasting reservoir performance in a Bayesian framework. The strength of the method in performance prediction is demonstrated by using an upscaled model to history match fine scale data. The maximum likelihood model is then used in forecasting the fine grid performance, and the uncertainty associated with the predictions is quantified. It is demonstrated that the maximum likelihood model is highly accurate in reservoir performance prediction.