Summary
The optimization of large-scale multiwell field-development projects is
challenging because the number of optimization variables and the size of the
search space can become excessive. This difficulty can be circumvented by
considering well patterns and then optimizing parameters associated with the
pattern type and geometry. In this paper, we introduce a general framework for
accomplishing this type of optimization. The overall procedure, which we refer
to as well-pattern optimization (WPO), includes a new well-pattern description
(WPD) incorporated into an underlying optimization method. The WPD encodes
potential solutions in terms of pattern types (e.g., five-spot, nine-spot) and
pattern operators. The operators define geometric transformations (e.g.,
stretching, rotating) quantified by appropriate sets of parameters. It is the
parameters that specify the well patterns and the pattern operators, along with
additional variables that define the sequence of operations, that are
optimized. A technique for subsequent well-by-well perturbation (WWP), in which
the locations of wells within each pattern are optimized, is also presented.
This WWP represents an optional second phase of WPO. The overall optimization
procedure could be used with a variety of underlying optimization methods.
Here, we combine it with a particle-swarm-optimization (PSO) technique because
PSO methods have been shown recently to provide robust and efficient
optimizations for well-placement problems.
Detailed optimization results are presented for several example cases. In
one case, multiple reservoir models are considered to account for geological
uncertainty. For all examples, significant improvement in the objective
function is observed as the algorithm proceeds, particularly at early
iterations. The use of well-by-well perturbation (following determination of
the optimal pattern) is shown to provide additional improvement. Limited
comparisons with results using standard well patterns of various sizes
demonstrate that the net present values (NPVs) achieved by the new algorithm
are considerably larger. Taken in total, the optimization results highlight the
potential of the overall procedure for use in practical field development.
© 2011. Society of Petroleum Engineers
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History
- Original manuscript received:
5 July 2009
- Meeting paper published:
5 October 2009
- Revised manuscript received:
23 July 2010
- Manuscript approved:
6 August 2010
- Published online:
25 March 2011
- Version of record:
15 September 2011
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