Bayesian variable pressure decline-curve analysis for shale gas wells

Decline-curve analysis and production forecasting are usually performed from a deterministic standpoint (point estimation). This approach does not quantify the uncertainty of the model's parameters and thus, the model's estimated ultimate recovery. In addition, decline-curve models do not consider the variations in the bottomhole flowing pressure, which can greatly impact the accuracy of the model's predictions. This work combines a new technique that incorporates variable bottomhole flowing pressure conditions into decline-curve models with Bayesian inference to improve the accuracy of production history-matches while quantifying the uncertainty of the model's parameters and its future production prediction. The method provides fast production history-matches and forecasts of shale gas wells (taking around 1 min per well) and it is more accurate than traditional decline-curve analysis for wells subject to variable bottomhole flowing pressure conditions while quantifying the uncertainty in the model's parameters and estimated ultimate recovery. The main contribution of this work is the illustration of a new method for probabilistic variable pressure decline-curve analysis. We present the application of this workflow for shale gas wells.