Horizontal well placement determination within a reservoir is a significant and difficult
step in the reservoir development process. Determining the optimal well location is a
complex problem involving many factors including geological considerations, reservoir
and fluid properties, economic costs, lateral direction, and technical ability. The most
thorough approach to this problem is that of an exhaustive search, in which a simulation
is run for every conceivable well position in the reservoir. Although thorough and
accurate, this approach is typically not used in real world applications due to the time
constraints from the excessive number of simulations.
This project suggests the use of a genetic algorithm applied to the horizontal well
placement problem in a gas reservoir to reduce the required number of simulations. This
research aims to first determine if well placement optimization is even necessary in a gas
reservoir, and if so, to determine the benefit of optimization. Performance of the genetic
algorithm was analyzed through five different case scenarios, one involving a vertical well and four involving horizontal wells. The genetic algorithm approach is used to
evaluate the effect of well placement in heterogeneous and anisotropic reservoirs on
reservoir recovery. The wells are constrained by surface gas rate and bottom-hole
pressure for each case.
This project's main new contribution is its application of using genetic algorithms to
study the effect of well placement optimization in gas reservoirs. Two fundamental
questions have been answered in this research. First, does well placement in a gas
reservoir affect the reservoir performance? If so, what is an efficient method to find the
optimal well location based on reservoir performance? The research provides evidence
that well placement optimization is an important criterion during the reservoir
development phase of a horizontal-well project in gas reservoirs, but it is less significant
to vertical wells in a homogeneous reservoir. It is also shown that genetic algorithms are
an extremely efficient and robust tool to find the optimal location.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2010-05-399 |
| Date | 2010 May 1900 |
| Creators | Gibbs, Trevor Howard |
| Contributors | Zhu, Ding, Nasrabadi, Hadi |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | thesis, text |
| Format | application/pdf |
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