Compressing Time-Dependent Reservoir Simulations Using Graph-Convolutional Neural Network G-CNN
- Srinath Madasu (Halliburton) | Shameem Siddiqui (Halliburton) | Keshava Prasad Rangarajan (Halliburton)
- Document ID
- Society of Petroleum Engineers
- Abu Dhabi International Petroleum Exhibition & Conference, 11-14 November, Abu Dhabi, UAE
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- Neural network, Reservoir Modeling, deep learning, data analytics, graph comvolutional neural network
- 10 in the last 30 days
- 194 since 2007
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Reservoir simulation results currently provide the basis for important reservoir engineering decisions; grid complexity and non-linearity of these models demand high computational time and memory. The physics-based simulation process must be repeated to increase model prediction accuracy or to perform history matching; consequently, the simulation process is often time-consuming. This paper describes a new methodology based on a deep neural network (DNN) technique, the graph convolutional neural network (G-CNN). G-CNN increases the modeling prediction speed and efficiency by compressing the computational time and memory usage of the reservoir simulation. A G-CNN model was used to perform the reservoir simulations described.
This new methodology combines physics-based and data-driven models in reservoir simulation. The workflow generates training datasets, enabling intelligent sampling of the reservoir production data in the G-CNN training process. Bottomhole pressure constraints were set for all simulations. The production data generated by the reservoir model, with the mesh connectivity information, is used to generate the G-CNN model. This approach can be viewed as hybrid data-driven, retaining the underlying physics of the reservoir simulator. The resulting G-CNN model can perform reservoir simulations for any computational grid and production time horizon. The method uses convolutional neural network and mesh connections in a fully differentiable scheme to compress the simulation state size and learns the reservoir dynamics on this compressed form.
G-CNN analysis was performed on an Eagle Ford-type reservoir model. The transmissibility, pore volume, pressure, and saturation at an initial time state were used as input features to predict final pressure and saturations. Prediction accuracy of 95% was obtained by hyperparameter tuning off the G-CNN architecture. By compressing the simulation state size and learning the time-dependent reservoir dynamics on this compressed representation, reservoir simulations can be emulated by a graph neural network which uses significantly less computation and memory. The G-CNN model can be used on any computational grid because it preserves the structure of the physics. The G-CNN model is trained by mapping an initial time state to a future prediction time state; consequently, the model can be used for generalizing predictions in any grid sizes and time steps while maintaining accuracy. The result is a computationally and memory efficient neural network that can be iterated and queried to reproduce a reservoir simulation.
This novel methodology combines field-scale physics-based reservoir modeling and DNN for reservoir simulations. The new reservoir simulation workflow, based on the G-CNN model, maps the time state predictions in a resampled grid, reducing computational time and memory. The new methodology presents a general method for compressing reservoir simulations, assisting in fast and accurate production forecasting.
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Brewer, M., Camilleri, D., Ward, S., & Wong, T. (2015, February 23). Generation of Hybrid Grids for Simulation of Complex, Unstructured Reservoirs by a Simulator with MPFA. Society of Petroleum Engineers. doi:10.2118/173191-MS
Mayerhofer, M. J., Lolon, E., Warpinski, N. R., Cipolla, C. L., Walser, D. W., & Rightmire, C. M. (2008, January 1). What is Stimulated Rock Volume? Society of Petroleum Engineers. doi:10.2118/119890-MS
Odeh, A. S. (1981, January 1). Comparison of Solutions to a Three-Dimensional Black-Oil Reservoir Simulation Problem (includes associated paper 9741). Society of Petroleum Engineers. doi:10.2118/9723-PA
Palke, M. R., Palmer, B. A., & Rietz, D. C. (2012, January 1). A Novel Simulation Model Review Process. Society of Petroleum Engineers. doi:10.2118/159274-MS
Schmidhuber J., "Deep learning in Neural Networks: An overview", 2015, Neural Networks 61, 86–117. DOI: https://doi.org/10.1016/j.neunet.2014.09.003.
Siddiqui, S., & Kumar, A. (2016, November 7). Well Interference Effects for Multiwell Configurations in Unconventional Reservoirs. Society of Petroleum Engineers. doi:10.2118/183064-MS