Abstract
Abstract The minimum fluid flow velocity to ensure particle transport is an essential design and operation consideration for pipelines of oil and gas production. This flow velocity is difficult to estimate due to complex nature of the physical processes. It has been shown that the model predictions may vary several orders of magnitude for the same inputs. This paper introduces a systematic approach to reduce this discrepancy using data clustering, model selection, and cluster identification techniques. The case study results show that the H-spread of the error percentages between the predictions and experimental velocities are reduced from 71.6 % to 34.4 %.
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