Shohreh Amini, SPE, West Virginia University, Shahab Mohaghegh, SPE, West
Virginia University and Intelligent Solutions Inc., Razi Gaskari, SPE,
Intelligent Solution Inc, Grant Bromhal, SPE, USA Department of Energy
SPE Western Regional Meeting,
21-23 March 2012,
Bakersfield, California, USA
While CO2 Capture and Sequestration (CCS) is considered a part of the solution
to overcoming the ever increasing level of CO2 in the atmosphere, one must be
sure that significant new hazards are not created by the CO2 injection process.
The risks involved in different stages of a CO2 sequestration project are
related to geological and operational uncertainties. This paper presents the
application of a grid-based Surrogate Reservoir Model (SRM) to a real case CO2
sequestration project in which CO2 were injected into a depleted gas reservoir.
An SRM is a customized model that accurately mimics reservoir simulation
behavior by using Artificial Intelligence & Data Mining techniques. Initial
steps for developing the SRM included constructing a reservoir simulation model
with a commercial software, history matching the model with available field
data and then running the model under different operational scenarios or/and
different geological realizations. The process was followed by extracting some
static and dynamic data from a handful of simulation runs to construct a
spatio-temporal database that is representative of the process being modeled.
Finally, the SRM was trained, calibrated, and validated.
The most widely used Quantitative Risk Analysis (QRA) techniques, such as Monte
Carlo simulation, require thousands of simulation runs to effectively perform
the uncertainty analysis and subsequently risk assessment of a project.
Performing a comprehensive risk analysis that requires several thousands of
simulation runs becomes impractical when the time required for a single
simulation run (especially in a geologically complex reservoir) exceeds only a
few minutes. Making use of surrogate reservoir models (SRMs) can make this
process practical since SRM runs can be performed in minutes.
Using this Surrogate Reservoir Model enables us to predict the pressure and CO2
distribution throughout the reservoir with a reasonable accuracy in seconds.
Consequently, application of SRM in analyzing the uncertainty associated with
reservoir characteristics and operational constraints of the CO2 sequestration
project is presented.
Despite all the efforts in shifting the energy sources to the renewable and
atmosphere friendly source of energy, fossil fuels are still the most essential
source of energy for industries and transportation. Considering the demand
growth it is believed that fossil fuel consumption will continue to increase
through the next century. As a result, concerns about the greenhouse gas
emission and its impact on global warming and climate change are increasing.
This has encouraged focus on two different approaches of reducing CO2 in the
atmosphere. The first one is the preventive methods which aim at minimizing CO2
emission in to the atmosphere through improved efficiency, renewable energy
supplies, carbon-free fuel consumption and nuclear fission, and the second
approach is to apply the remedial methods through which the CO2 concentration
in the atmosphere is reduced [1,2].