Probabilistic decline curve analysis (PDCA) methods have recently been developed to quantify uncertainty in production forecasts for hydraulically fractured horizontal shale gas wells. The Markov Chain Monte Carlo (MCMC) method has been proposed as a fast and reliable probabilistic method to quantify uncertainty regardless of the decline curve analysis model employed and the amount of production data available for forecasting. In this paper, we integrate other sources of information with PDCA in a Bayesian framework to enhance the reliability of production forecasts for hydraulically fractured horizontal shale gas wells.
Hindcasts using the logistic-growth-curve model coupled to MCMC and two different sources of prior information were performed to assess the reliability of the improved PDCA method. In a hindcast, only a portion of the historical data is matched; predictions are made for the remainder of the historical period and compared to the actual historical production. The logistic-growth-curve model was moderately well calibrated when used with the MCMC probabilistic method and a non-informative prior, regardless of the amount of production data available to match. When EUR distributions from other sources of information were provided as an informative prior, accuracy and calibration of the probabilistic production forecasts were further enhanced. The use of informative prior distributions improves calibration particularly when little production data are available.
The combination of the MCMC method, the logistic-growth DCA model, and use of prior EUR distributions provides an integrated procedure to reliably quantify the uncertainty in production forecasts in shale gas reservoirs. Reliable quantification of uncertainty should yield more reliable expected values of reserves estimates. This can be particularly valuable early in the development of a play, because decisions regarding continued development are based to a large degree on production forecasts for early wells in the play.