441 research outputs found
Solving multiphysics-based inverse problems with learned surrogates and constraints
Solving multiphysics-based inverse problems for geological carbon storage
monitoring can be challenging when multimodal time-lapse data are expensive to
collect and costly to simulate numerically. We overcome these challenges by
combining computationally cheap learned surrogates with learned constraints.
Not only does this combination lead to vastly improved inversions for the
important fluid-flow property, permeability, it also provides a natural
platform for inverting multimodal data including well measurements and
active-source time-lapse seismic data. By adding a learned constraint, we
arrive at a computationally feasible inversion approach that remains accurate.
This is accomplished by including a trained deep neural network, known as a
normalizing flow, which forces the model iterates to remain in-distribution,
thereby safeguarding the accuracy of trained Fourier neural operators that act
as surrogates for the computationally expensive multiphase flow simulations
involving partial differential equation solves. By means of carefully selected
experiments, centered around the problem of geological carbon storage, we
demonstrate the efficacy of the proposed constrained optimization method on two
different data modalities, namely time-lapse well and time-lapse seismic data.
While permeability inversions from both these two modalities have their pluses
and minuses, their joint inversion benefits from either, yielding valuable
superior permeability inversions and CO2 plume predictions near, and far away,
from the monitoring wells
Extended source imaging, a unifying framework for seismic & medical imaging
We present three imaging modalities that live on the crossroads of seismic
and medical imaging. Through the lens of extended source imaging, we can draw
deep connections among the fields of wave-equation based seismic and medical
imaging, despite first appearances. From the seismic perspective, we underline
the importance to work with the correct physics and spatially varying velocity
fields. Medical imaging, on the other hand, opens the possibility for new
imaging modalities where outside stimuli, such as laser or radar pulses, can
not only be used to identify endogenous optical or thermal contrasts but that
these sources can also be used to insonify the medium so that images of the
whole specimen can in principle be created.Comment: Submitted to the Society of Exploration Geophysicists Annual Meeting
202
3D seismic survey design by maximizing the spectral gap
The massive cost of 3D acquisition calls for methods to reduce the number of
receivers by designing optimal receiver sampling masks. Recent studies on 2D
seismic showed that maximizing the spectral gap of the subsampling mask leads
to better wavefield reconstruction results. We enrich the current study by
proposing a simulation-free method to generate optimal 3D acquisition by
maximizing the spectral gap of the subsampling mask via a simulated annealing
algorithm. Numerical experiments confirm improvement of the proposed method
over receiver sampling locations obtained by jittered sampling
De-risking geological carbon storage from high resolution time-lapse seismic to explainable leakage detection
Geological carbon storage represents one of the few truly scalable
technologies capable of reducing the CO2 concentration in the atmosphere. While
this technology has the potential to scale, its success hinges on our ability
to mitigate its risks. An important aspect of risk mitigation concerns
assurances that the injected CO2 remains within the storage complex. Amongst
the different monitoring modalities, seismic imaging stands out with its
ability to attain high resolution and high fidelity images. However, these
superior features come, unfortunately, at prohibitive costs and time-intensive
efforts potentially rendering extensive seismic monitoring undesirable. To
overcome this shortcoming, we present a methodology where time-lapse images are
created by inverting non-replicated time-lapse monitoring data jointly. By no
longer insisting on replication of the surveys to obtain high fidelity
time-lapse images and differences, extreme costs and time-consuming labor are
averted. To demonstrate our approach, hundreds of noisy time-lapse seismic
datasets are simulated that contain imprints of regular CO2 plumes and
irregular plumes that leak. These time-lapse datasets are subsequently inverted
to produce time-lapse difference images used to train a deep neural classifier.
The testing results show that the classifier is capable of detecting CO2
leakage automatically on unseen data and with a reasonable accuracy
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