Real-time inverse-model analysis and control on data collection
Creators: name: Vesselinov, Velimir
name: Robinson, Bruce
name: Vrugt, Jasper
name: Zyvoloski, George
name: Vesselinov, Velimir
Publisher: name: XVI International Conference on Computational Methods in Water Resources
Year: 2006

description: Sophisticated numerical models are commonly used to simulate fluid and chemical flow
in the subsurface. The science of flow in porous media is composed of general
physical principles (transferable knowledge) and site-specific details. All sites are
unique, so even if the physics is well understood, we need detailed, site-specific
information to develop a model for each site (subsurface heterogeneity, initial and
boundary conditions, etc.). In this respect, at each new site, we “start over”. The
most time- and resource-consuming step in reducing predictive uncertainty bounds in
subsurface systems is the process of uncovering the site-specific details. The
current paradigm is to perform a lengthy reconnaissance phase to understand the site,
followed by additional data collection and modeling to synthesize the information.
Model development methods are slow and labor-intensive for complex sites; therefore,
model results generally lag behind the data collection by a considerable length of
time. This delay limits the usefulness of the model as a tool to guide data
collection: any given iteration of the model is out of date by the time it is
completed. The whole process is unacceptably protracted in an era in which, for
example, we may ultimately need hundreds of sites to implement CO2 geologic
sequestration.
Our technical capabilities for efficiently collecting and organizing subsurface data
have progressed recently with the advent of modern data collection and transmission
systems. However, our ability to process this information in the form of numerical
models has lagged behind. We propose a new paradigm for the development of complex
subsurface flow and transport models in which the inverse analysis is performed in
real time, simultaneously with the data collection. Furthermore, we propose to use
the inverse model to control the data collection or the operating conditions of an
extraction system in real time. This is extremely important because post mortem
examination of many field studies illustrates that much of the collected information
is redundant and does not further reduce the model uncertainty. Some of these studies
have also uncovered missed opportunities to collect information that might have
substantially reduced model uncertainty. Often in hydrogeology, the repetition of the
data collection is prohibitively expensive or even impossible (e.g. 100-year flood
event, the movement of contaminant plumes). By integrating the model development and
data collection processes, we believe we can radically reduce the cost and time
required for site characterization.
We demonstrate the applicability of this concept using simple synthetic analyses
representing groundwater flow and contaminant transport. We use two separate models:
(1) a forward model representing our synthetic reality, and (2) an inverse model that
assimilates the accumulated data and guides the data collection in real time. Our
initial results demonstrate the capabilities and challenges in the proposed approach.