Constructing Dual-Porosity Models from High-Resolution Discrete-Fracture Models Using Deep Neural Networks
KAUST DepartmentEarth Science and Engineering Program
Energy Resources & Petroleum Engineering
Physical Science and Engineering (PSE) Division
Energy Resources and Petroleum Engineering Program
Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC)
Online Publication Date2021-10-19
Print Publication Date2021-10-19
Permanent link to this recordhttp://hdl.handle.net/10754/672971
MetadataShow full item record
AbstractDetailed geological description of fractured reservoirs is typically characterized by the discrete-fracture model (DFM), in which the rock matrix and fractures are explicitly represented in the form of unstructured grids. Its high computation cost makes it infeasible for field-scale applications. Traditional flow-based and static-based methods used to upscale detailed geological DFM to reservoir simulation model suffer from, to some extent, high computation cost and low accuracy, respectively. In this paper, we present a novel deep learning-based upscaling method as an alternative to traditional methods. This work aims to build an image-to-value model based on convolutional neural network to model the nonlinear mapping between the high-resolution image of detailed DFM as input and the upscaled reservoir simulation model as output. The reservoir simulation model (herein refers to the dual-porosity model) includes the predicted fracture-fracture transmissibility linking two adjacent grid blocks and fracture-matrix transmissibility within each coarse block. The proposed upscaling workflow comprises the train-validation samples generation, convolutional neural network training-validating process, and model evaluation. We apply a two-point flux approximation (TPFA) scheme based on embedded discrete-fracture model (EDFM) to generate the datasets. We perform trial-error analysis on the coupling training-validating process to update the ratio of train-validation samples, optimize the learning rate and the network architecture. This process is applied until the trained model obtains an accuracy above 90 % for both train-validation samples. We then demonstrate its performance with the two-phase reference solutions obtained from the fine model in terms of water saturation profile and oil recovery versus PVI. Results show that the DL-based approach provides a good match with the reference solutions for both water saturation distribution and oil recovery curve. This work manifests the value of the DL-based method for the upscaling of detailed DFM to the dual-porosity model and can be extended to construct generalized dual-porosity, dual-permeability models or include more complex physics, such as capillary and gravity effects.
CitationHe, X., Santoso, R., Alsinan, M., Kwak, H., & Hoteit, H. (2021). Constructing Dual-Porosity Models from High-Resolution Discrete-Fracture Models Using Deep Neural Networks. Day 1 Tue, October 26, 2021. doi:10.2118/203901-ms
SponsorsWe would like to thank Saudi Aramco for funding this research. We would also like to thank King Abdullah University of Science and Technology (KAUST) for providing license for MATLAB.