Robust production forecast and uncertainty quantification for waterflooding reservoir using hybrid recurrent auto-encoder and long short-term memory neural network

Abstract

History-matching-based production forecast and uncertainty quantification are essential to achieve reliable risk assessment for waterflooding reservoir in the community of petroleum engineering, however, the conventional model-based history matching procedure presents intensive computation-cost due to numerous high-fidelity reservoir simulations. The use of direct forecast approach, e.g., data-space inversion (DSI) and its variants, is urgent to achieve direct production forecast without explicitly performing history matching. This work presents a generic data-driven post-history production forecasting framework using a novel deep recurrent neural network. We construct a hybrid recurrent neural network proxy through combining recurrent autoencoder with long short-term memory neural network, e.g., referred to as RAE-LSTM. The proposed RAE-LSTM takes history prediction and post-history well controls as input, while the post-history production responses (oil and water production, water injection rate) as output. Training samples are constructed by simulating high-fidelity models parameterized with various post-history well-controls and geomodels. Once the deep recurrent neural network proxies are trained offline, the online post-history production forecast and uncertainty quantification can be efficiently achieved by input user-specific well control sequences and history measurement. The proposed proxy model is demonstrated on two examples with varying complexity, e.g., a synthetic 2D Gaussian model and a 3D channelized EGG model. The use of our proposed RAE-LSTM neural network can be regarded as a nonlinear alternative to the DSI-type of linear statistical interpolation method. The comparisons between DSI and RAE-LSTM proxy confirm that the application of our proposed data-driven prediction method can effectively obtain more robust predictions and thus support rapid and reliable decision-making during the process of real-time waterflooding production management and optimization.