Title
A Deep-Learning-Based Geological Parameterization for History Matching Complex Models.
Abstract
A new low-dimensional parameterization based on principal component analysis (PCA) and convolutional neural networks (CNN) is developed to represent complex geological models. The CNN–PCA method is inspired by recent developments in computer vision using deep learning. CNN–PCA can be viewed as a generalization of an existing optimization-based PCA (O-PCA) method. Both CNN–PCA and O-PCA entail post-processing a PCA model to better honor complex geological features. In CNN–PCA, rather than use a histogram-based regularization as in O-PCA, a new regularization involving a set of metrics for multipoint statistics is introduced. The metrics are based on summary statistics of the nonlinear filter responses of geological models to a pre-trained deep CNN. In addition, in the CNN–PCA formulation presented here, a convolutional neural network is trained as an explicit transform function that can post-process PCA models quickly. CNN–PCA is shown to provide both unconditional and conditional realizations that honor the geological features present in reference SGeMS geostatistical realizations for a binary channelized system. Flow statistics obtained through simulation of random CNN–PCA models closely match results for random SGeMS models for a demanding case in which O-PCA models lead to significant discrepancies. Results for history matching are also presented. In this assessment CNN–PCA is applied with derivative-free optimization, and a subspace randomized maximum likelihood method is used to provide multiple posterior models. Data assimilation and significant uncertainty reduction are achieved for existing wells, and physically reasonable predictions are also obtained for new wells. Finally, the CNN–PCA method is extended to a more complex nonstationary bimodal deltaic fan system, and is shown to provide high-quality realizations for this challenging example.
Year
DOI
Venue
2018
10.1007/s11004-019-09794-9
Mathematical Geosciences
Keywords
Field
DocType
Geological parameterization, History matching, Deep learning, Principal component analysis
Histogram,Subspace topology,Convolutional neural network,Algorithm,Regularization (mathematics),Artificial intelligence,Deep learning,Nonlinear filter,Mathematics,Machine learning,Principal component analysis,Binary number
Journal
Volume
Issue
ISSN
abs/1807.02716
6
1874-8961
Citations 
PageRank 
References 
1
0.36
3
Authors
3
Name
Order
Citations
PageRank
Yimin Liu115825.46
Wenyue Sun210.36
Louis J. Durlofsky3588.64