Rapid production forecasting for heterogeneous gas-condensate shale reservoir

Abstract

In the upstream oil and gas industry, production forecasting is crucial for decision making, investment allocation, reservoir management, and field development. While traditional methods like empirical models and numerical simulators have been employed for production forecasting, their limitations become pronounced when dealing with large, heterogeneous reservoirs. Though traditional simulators provide detailed and accurate models of the reservoir, they can be computationally expensive and time consuming. Machine-learning based production forecasting can offer a faster yet accurate approach that is useful in scenarios where quick estimates are needed or thousands of scenarios need to be investigated within an optimization and history-matching workflow. We propose a machine learning-driven workflow that integrates a massive geomodel compression followed by neural-network-based regression to rapidly forecast the production of a well in a large, complex reservoir. The proposed workflow accounts for spatial heterogeneities in porosity, permeability, and fluid saturation as well as uncertainties in induced and natural fracture properties and their distributions. The newly developed geomodel compression followed by neural-network-based rapid production forecasting reduces the computation time to generate a 5-year forecast per realization by an order of 6. Overall, the newly developed rapid production forecasting per realization takes approximately 0.0003 s as compared to 1037 s or 17 min per realization for a traditional simulator.