Machine Learning Based Error Modeling for Surrogate Model in Oil Reservoir Problem

Abstract

This thesis focuses on the construction and optimization of a prediction model for the errors resulting from a model order reduction (MOR) procedure in oil reservoir simulation. MOR is a numerical technique that projects the physical based model, which is also called the high-fidelity model (HFM), into a lower dimension by using matrix decomposition, such that the computational speed can be greatly increased. The reduced order model (ROM) is also known as surrogate model. Obviously, error occurs during the projection process. We want to estimate this error and predict it through building an error model, and to fortify the surrogate model by adapting a parameter estimation. In this thesis, three statistical methods will be adapted to our problem, including least absolute shrinkage and selection operator (LASSO) and two machine learning (ML) methods: long short term memory (LSTM) and fully-connected recurrent neural network (RNN). The training data is the error of the ROM, which is defined as the difference between the ROM values and HFM values. Efforts have also been made to improve the performance of the error model, including the pre-processing of the data, and several model optimization techniques. The model order reduction method here is a non-intrusive subdomain POD-RBF algorithm, which treats subsurface oil-water flow data by adapting domain decomposition (DD), radial basis function (RBF) and proper orthogonal decomposition (POD). The high-fidelity model is generated by Matlab reservoir simulation toolbox (MRST). The error is defined as the difference between the HFM data and the ROM data. Through the comparison of several statistical models, this error can be best predicted by an optimized traditional recurrent neural network.