A Flow Network Model Based on Time of Flight for Reservoir Management

Abstract

Abstract Closed loop reservoir management is challenged with building reliable and fast predictive reservoir models to make field decisions. Traditional numerical simulation models can be difficult to characterize, tedious to build and calibrate, and at times computationally prohibitive for short term decision cycles in field applications. On the other hand, pure data-driven methods often lack physical insights and have limited range of applicability. For operational scenarios such as short-term production forecasting, waterflood optimization, production control and understanding major reservoir mechanisms, it is desirable to use a reservoir modeling methodology that is easy to build, history match, compute and interpret. In this work, we propose to use a hybrid and efficient reservoir graph network (RGNet) modeling approach based on time of flight concept that can be built using routinely measured field measurements (such as pressure and rates) and can be used for real-time forecasts, scenario modeling, production optimization and control. We propose a gridding method based on discretized time of flight for multi-well scenario with interference. It simplifies the 3D reservoir flow problem into a graph network representation that can be solved with any commercial reservoir simulator, which enables the RGNet model to be readily applied for various types of fluid physics. The parameters in RGNet model are obtained through assimilating observed data. The RGNet model has a very compact model representation that requires significantly less complexity compared with full-physics 3D models, which leads to very fast simulation. The efficiency of RGNet makes it appealing for applications where many simulation runs are needed. We applied the proposed approach on SPE benchmark reservoir simulation models for single well, multi-well with interference and injector-producer pairs. The calibrated models are used to quantify uncertainty for production forecasting. In all cases, the range of uncertainty is reduced effectively and efficiently with data assimilation. The posterior RGNet models are also shown to provide reasonable estimates of reservoir and well drainage volumes. By virtue of the reduced complexity, the modeling methodology is highly scalable while still retains physical interpretability (in terms of pore volume and transmissibility). We also discuss the potential applications of the method such as reservoir connectivity analysis and well control optimization. The proposed reservoir graph network (RGNet) modeling approach provides a unique and sustainable way to combine advanced analytics and physics to develop an explainable dynamic reservoir model that can be effectively used to understand reservoir behavior and optimize performance. The lightweight model lends itself naturally to fast computation that are required for scenario analysis and optimization.

Topics & Concepts