Decline Curve Analysis (DCA) has been widely applied in production forecasting of wells in unconventional hydrocarbon reservoirs. However, traditional curve-fit-based methods fall short of forecast accuracy due to three weaknesses: first, they cannot capture the reservoir signals not modeled by the underlying DCA model formulas; second, when predicting the production of a target well, the production history of other wells in the geologic formation (which is valuable information) are not considered; third, the wells' geographic, geologic, wellbore, well spacing, and completion properties, which are highly relevant to production capability, are not utilized. More recent approaches have begun replacing traditional DCA with machine-learning methods (e.g., Random Forest, Support Vector Regression, etc.) for production forecast. Nevertheless, these methods are still sub-optimal in detecting similar production trends in different wells, leading to large forecast error.

A simple and novel method called Dynamic Production Rescaling (DPR) is developed to improve the accuracy of machine-learning DCA (ML-DCA). By combining DPR with common ML-DCA methods, we observe that the error mean, deviation, and skewness can be significantly reduced by 15% to 35% compared with ML-DCA without DPR. The error reduction is 30% to 60% compared with automatic curve fit of traditional Modified Arps DCA model. DPR has been tested successfully on monthly production data of over 20,000 unconventional horizontal wells in the Permian and Appalachian basins for both long- and short-term forecasts. The significant error reduction is consistent across different basins and formations. DPR is computationally efficient, so large number of wells can be analyzed automatically and quickly. Moreover, the effectiveness and efficiency of DPR is independent of the underlying machine-learning algorithm, further demonstrating its robustness.


Operation and management of unconventional hydrocarbon reservoirs requires production forecast of each well. This enables better development planning, economic outlook, reserve estimates, and business decisions such as trading and pricing strategies. A methodology called Decline Curve Analysis (DCA) has been widely applied in production forecast of wells in unconventional reservoirs, and many analytical DCA models are available for use. They describe the production physics in analytical equations of flow rate versus time, and the coefficients of the equations are computed from curve-fitting the production history.

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