This paper presents a methodology for quantifying uncertainty in production forecasts using Logistic Growth Analysis (LGA) and time series modeling. The applicability of the proposed method is tested by history matching production data and providing uncertainty bounds for forecasts from eight Barnett Shale counties.
In the methodology presented, the trend in the production data was determined using two different non-linear regression schemes. Predicted trends were subtracted from the actual production data to generate two sets of stationary residual time series. Time series analysis techniques (Auto Regressive Moving Average models) were thereafter used to model and forecast residuals. These residual forecasts were incorporated with trend forecasts to generate our final 80% CI.
To check reliability of the proposed method, we tested it on 100 gas wells with at least 100 months of available production history. The CIs generated covered true production 84% and 92% of the time when 40 and 60 months of production data were used for history matching respectively. An auto-regressive model of lag 1 was found to best fit residual time series in each case.
The proposed methodology is an efficient way to generate production forecasts and to reliably estimate the uncertainty. The method is computationally inexpensive and easy to implement. The utility of the procedure presented is not limited to gas wells and can be applied to any type of well or group of related wells.