Uncertainty Assessment in Production Forecasting and Optimization for a Giant Multi-Layered Sandstone Reservoir Using Optimized Artificial Neural Network Technology

Wei, Chenji (PetroChina) | Wang, Yuhe (Texas A&M University at Qatar) | Ding, Yutao (PetroChina) | Li, Yong (PetroChina) | Shi, Jing (PetroChina) | Liu, Shuangshuang (PetroChina) | Tian, Changbing (PetroChina) | Li, Baozhu (PetroChina) | Xiong, Lihui (PetroChina) | Zhang, Qi (PetroChina)

OnePetro 

This paper presents an uncertainty assessment project using Artificial Neural Network (ANN) for a giant multi-layered sandstone reservoir in Middle East, which contains several uncertainties and associated risks. Uncertainty quantification in history matching, production forecasting and optimization approaches often requires hundreds of thousands of forward flow simulations to explore the uncertain parameter space, causing forbidden computational time requirement, especially for large-scale reservoir models. In order to bypass this limitation, one can use a proxy to replace the time-consuming flow simulator. In this work, an optimized ANN is used as the proxy and an uncertainty assessment workflow is implemented for the giant Cretaceous multi-layered sandstone reservoir using a global optimizer. Using the ANN based uncertainty assessment framework, the impacts of the main uncertain parameters on production forecasting are assessed for this multi-layered sandstone reservoir. Then, field development optimization is also performed to optimize wells injection and production rates to maximize the economic measures considering uncertainties.