aAdding another fraction to an initial fractional factorial design is often required to resolve ambiguities with respect to aliasing of factorial effects from the initial experiment and/or to improve estimation precision. Multiple techniques for design follow-up exist; the choice of which is often made on the basis of the initial design and its analysis, resources available, experimental objectives, and so on. In this paper, we compare four design follow-up strategies: foldover, semifoldover, D-optimal, and Bayesian (MD-optimal) in the context of a metal-cutting case study previously utilized to compare fractional factorials of different run sizes. Follow-up designs are compared for each of a 2 6 3 III , 2 6 2 IV , and Plackett-Burman initial experiments. Our empirical results suggest that a single followup strategy does not outperform all others in every situation. This case study serves to illustrate design augmentation possibilities for practitioners and provides some basis for the selection of a follow-up experiment.
Twenty-first-century oil and gas operations are facing a unique set of challenges that require new workflows and processes, and new ways to leverage technology. The objective is to help petrotechnical professionals manage huge volumes of data to improve understanding and analysis, and support sound decision making—with an ultimate goal of improving asset value.
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