Analytical models available in Rate-Transient-Analysis (RTA) packages are widely used as tools for history matching and forecasting production in unconventional resources. There has also been an increasing interest in the use of numerical simulation of unconventional reservoirs. In this study, we use both methods to history match the production of hydraulically fractured unconventional wells, followed by forecasting future production to establish a well's EUR (Estimated Ultimate Recovery) for reserves determination purposes. This study's goal is to quantify the differences one might expect to encounter in a well's EUR when using analytical model-based RTA vs numerical simulation-based workflows in unconventional reservoirs.
First, we consider an undersaturated shale oil reservoir as a base model for this study. The base case also satisfies all assumptions inherent to analytical solution-based methods such as homogenous reservoir properties and fully-penetrating planar fractures. An excellent match between results of both methods for the base model validates the numerical simulation approach. We then impose a series of real-world deviations from RTA assumptions and investigate reliability of EUR predictions made by both approaches. In all cases, historical data and reference EURs are derived from finely-gridded numerical simulations.
Example results show that, in the presence of real-world deviations from RTA assumptions, analytical models can still match the historical production data; however, key reservoir and fracture parameters need to be modified drastically to compensate for the lack of sufficient physics in the analytical models. Results show that the analytical solution-based history-matched models are not predictive for future production, and somewhat surprisingly provide pessimistic EURs in all real-world scenarios investigated in this work. For the cases presented in this study, analytical models under-predict EURs by 6-17% when two years of production history is available for matching. The underestimation of EUR increases drastically (up to 60%) as the length of available historical data decreases from 2 years to 3 months.
For all cases, we also apply an efficient numerical simulation-based workflow for probabilistic forecasting of EURs. This workflow provides multiple history-matched models that are constrained by historical production data. The probabilistic forecast method employed in this work provides P90 (conservative), P50 (most likely), and P10 (optimistic) values for EUR. In all examples, the range of P90 to P10 EUR values includes the reference EUR, and the P50 values are within 2.2% of the reference EUR.