A New Well-Pattern-Optimization Procedure for Large-Scale Field Development

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.