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Coupled Time‐Lapse Full‐Waveform Inversion for Subsurface Flow Problems Using Intrusive Automatic Differentiation
Author(s) -
Li Dongzhuo,
Xu Kailai,
Harris Jerry M.,
Darve Eric
Publication year - 2020
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2019wr027032
Subject(s) - inversion (geology) , automatic differentiation , inverse problem , reservoir simulation , inverse , reservoir modeling , computer science , waveform , flow (mathematics) , mathematical optimization , partial differential equation , algorithm , mathematics , geology , geophysics , geotechnical engineering , mathematical analysis , petroleum engineering , geometry , radar , telecommunications , computation , paleontology , structural basin
We describe a novel framework for estimating subsurface properties, such as rock permeability and porosity, from time‐lapse observed seismic data by coupling full‐waveform inversion (FWI), subsurface flow processes, and rock physics models. For the inverse modeling, we handle the back propagation of gradients by an intrusive automatic differentiation strategy that offers three levels of user control: (1) At the wave physics level, we adopted the discrete adjoint method in order to use our existing high‐performance FWI code; (2) at the rock physics level, we used built‐in automatic differentiation operators from the TensorFlow backend; (3) at the flow physics level, we implemented customized partial differential equation (PDE) operators for the multiphase flow equations. The three‐level coupled inversion strategy strikes a good balance between computational efficiency and programming efforts, and when the gradients are chained together, it constitutes a coupled inverse system. Our numerical experiments demonstrate that the three‐level coupled inverse problem is superior in terms of accuracy to a traditional decoupled inversion strategy. Additionally, our method is able to simultaneously invert for parameters in empirical relationships such as the rock physics models. Our proposed inverted model can be used for reservoir performance prediction and reservoir management/optimization purposes.