Waterflood optimization via rate control is receiving increased interest because
of rapid developments in the smart well completions and I-field technology. The use of
inflow control valves (ICV) allows us to optimize the production/injection rates of
various segments along the wellbore, thereby maximizing sweep efficiency and delaying
water breakthrough. It is well recognized that field scale rate optimization problems are
difficult because they often involve highly complex reservoir models, production and
facilities related constraints and a large number of unknowns. Some aspects of the
optimization problem have been studied before using mainly optimal control theory.
However, the applications to-date have been limited to rather small problems because of
the computation time and the complexities associated with the formulation and solution
of adjoint equations. Field-scale rate optimization for maximizing waterflood sweep
efficiency under realistic field conditions has still remained largely unexplored.
We propose a practical and efficient approach for computing optimal injection
and production rates and thereby manage the waterflood front to maximize sweep
efficiency and delay the arrival time to minimize water cycling. Our work relies on
equalizing the arrival times of the waterfront at all producers within selected sub-regions
of a water flood project. The arrival time optimization has favorable quasi-linear
properties and the optimization proceeds smoothly even if our initial conditions are far
from the solution. We account for geologic uncertainty using two optimization schemes.
The first one is to formulate the objective function in a stochastic form which relies on a
combination of expected value and standard deviation combined with a risk attitude coefficient. The second one is to minimize the worst case scenario using a min-max
problem formulation. The optimization is performed under operational and facility
constraints using a sequential quadratic programming approach. A major advantage of
our approach is the analytical computation of the gradient and Hessian of the objective
which makes it computationally efficient and suitable for large field cases.
Multiple examples are presented to support the robustness and efficiency of the
proposed optimization scheme. These include several 2D synthetic examples for
validation purposes and 3D field applications.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2009-05-456 |
| Date | 16 January 2010 |
| Creators | Alhuthali, Ahmed Humaid H. |
| Contributors | Datta Gupta, Akhil |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation |
| Format | application/pdf |
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