Deep-learning inversion to efficiently handle big-data assimilation: Application to seismic history matching
Title
Deep-learning inversion to efficiently handle big-data assimilation: Application to seismic history matching
Subject
Big data
Convolution
Convolutional neural networks
Network architecture
Geology
Learning systems
Stochastic systems
3D modeling
Deep neural networks
Seismology
Matrix algebra
Description
Seismic history matching can play a key role in geological characterization and uncertainty quantification. However, challenges related to intensive computational demands and complexity restricts its application in many practical cases. This paper presents a conceptual deep-learning-based framework fully deployed in the popular Pytorch architecture to accelerate the seismic history matching. We introduce a surrogate model based on a deep convolutional neural network with a stack of dense blocks, specifically a conditional deep convolutional autoencoder-decoder architecture (cDCAE). The surrogate model allows us to fully deploy data assimilation algorithms in Pytorch architecture and hence to easily make full use of the efficient computing units, in particular GPU's for the matrix-matrix and matrix-vector multiplications. The feature of built-in automatic differentiation (AD) provided by Pytorch also makes is possible to evaluate gradient information efficiently in a parallel manner. Furthermore, it has been acknowledged to benefit from the deep learning practice of using stochastic gradient (SG) optimizers, e.g., Adam, instead of full gradient optimizers, e.g., Quasi-Newton, as is most common in conventional big-data assimilation. The proposed framework is tested on a benchmark 3D model in the context of petroleum engineering. This surrogate model is demonstrated to be capable of accurately predicting the quantity of interest, e.g., dynamic saturation maps for new geological realizations. Assessments demonstrating high surrogate-model accuracy are presented for an ensemble of test models. The robustness and dramatic speedup provided by the surrogate model suggests that it can help facilitate the application of large-scale seismic history matching. Copyright ECMOR 2020. All rights reserved.
Creator
Xiao, C.
Heemink, A.
Lin, H.
Leeuwenburgh, O.
Publisher
17th European Conference on the Mathematics of Oil Recovery, ECMOR 2020, September 14, 2020 - September 17, 2020
Date
2020
Type
conferencePaper
Identifier
10.3997/2214-4609.202035158
URL
http://dx.doi.org/10.3997/2214-4609.202035158
Citation
Xiao, C. et al., “Deep-learning inversion to efficiently handle big-data assimilation: Application to seismic history matching,” Lamar University Midstream Center Research, accessed May 18, 2024, https://lumc.omeka.net/items/show/28946.