The development of shale plays requires accurate forecasting of production rates and expected ultimate recoveries, which is challenging due to the complexities associated with production from hydraulically fractured horizontal wells in unconventional reservoirs. Traditional empirical models like Arps decline are inadequate in capturing these complexities, and long-term forecasting is hindered by the challenges posed by 3 phase flow. In response, a new physics-augmented, data-driven forecasting method has been proposed that efficiently captures these complexities.

The proposed PI-based forecasting (PIBF) method combines data-driven techniques with the physics of propagation of dynamic drainage volume under transient flow conditions observed by unconventional wells for a prolonged period. The model requires only routinely measured inputs such as production rates and wellhead pressure, and efficiently captures the trend shift in gas-to-oil ratio caused by free gas liberation in the near-wellbore region. By using material balance and productivity index models, the proposed approach can forecast well performance and handle changing operational conditions during the well's lifecycle.

Compared to existing empirical or analytical methods like Arps decline and RTA, the proposed method yields more accurate forecasting results, while still using easily available inputs. Empirical methods like Arps decline have low input requirements but lack physical insights, leading to inaccuracies and inability to handle changing operational conditions. Pure physics-based methods like RTA and reservoir simulation require more input properties that are often difficult to obtain, resulting in a low range of applicability.

Overall, the proposed method offers a promising alternative to existing methods, effectively combining data-driven techniques with physics-based insights to accurately forecast well performance and handle changing operational conditions in unconventional reservoirs.

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