Waterflooding is a common type of oil recovery techniques where water is
pumped into the reservoir for increased productivity. Reservoir states change
with time, as such, different injection and production settings will be required to
lead the process to optimal operation which is actually a dynamic optimization
problem. This could be solved through optimal control techniques which
traditionally can only provide an open-loop solution. However, this solution is
not appropriate for reservoir production due to numerous uncertain properties
involved. Models that are updated through the current industrial practice of
‘history matching’ may fail to predict reality correctly and therefore, solutions
based on history-matched models may be suboptimal or non-optimal at all.
Due to its ability in counteracting the effects uncertainties, direct feedback
control has been proposed recently for optimal waterflooding operations. In this
work, two feedback approaches were developed for waterflooding process
optimization. The first approach is based on the principle of receding horizon
control (RHC) while the second is a new dynamic optimization method
developed from the technique of self-optimizing control (SOC). For the SOC
methodology, appropriate controlled variables (CVs) as combinations of
measurement histories and manipulated variables are first derived through
regression based on simulation data obtained from a nominal model. Then the
optimal feedback control law was represented as a linear function of
measurement histories from the CVs obtained.
Based on simulation studies, the RHC approach was found to be very sensitive
to uncertainties when the nominal model differed significantly from the
conceived real reservoir. The SOC methodology on the other hand, was shown
to achieve an operational profit with only 2% worse than the true optimal
control, but 30% better than the open-loop optimal control under the same
uncertainties. The simplicity of the developed SOC approach coupled with its
robustness to handle uncertainties proved its potentials to real industrial
applications.
| Identifer | oai:union.ndltd.org:CRANFIELD1/oai:dspace.lib.cranfield.ac.uk:1826/9263 |
| Date | 10 1900 |
| Creators | Grema, Alhaji Shehu |
| Contributors | Cao, Yi |
| Publisher | Cranfield University |
| Source Sets | CRANFIELD1 |
| Language | English |
| Detected Language | English |
| Type | Thesis or dissertation, Doctoral, PhD |
| Rights | © Cranfield University 2014. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright owner. |
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