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A hybrid approach based on locally linear neuro-fuzzy modeling and TOPSIS to determine the quality grade of gas well-drilling projects

Ahari, R. M ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1016/j.petrol.2014.01.010
  3. Abstract:
  4. Evaluation of a project and its contractors has considerable importance in gas well-drilling projects due to their high investments and worth. In this paper, the quality of some gas well-drilling projects is analyzed in order to evaluate and grade project tasks. A neuro-fuzzy network is utilized to learn the grading process and generate models. To select among these models, a ranking method, namely technique for order of preference by similarity to ideal solution (TOPSIS) is employed. During seven gas well-drilling projects, 77 tasks are studied based on quality practitioners[U+05F3] points of view. After generating the primary models, three indices namely, root mean square error (RMSE), mean absolute percentage error (MAPE), and a newly introduced Q-index are selected to prioritize 31 models in optimistic, pessimistic, and average modes
  5. Keywords:
  6. Local linear model tree (LOLIMOT) ; Multi-criteria decision making (MCDM) ; Nonlinear dependency ; Project evaluation ; Quality factors
  7. Source: Journal of Petroleum Science and Engineering ; Vol. 114 , 2014 , pp. 99-106 ; ISSN: 09204105
  8. URL: http://www.sciencedirect.com/science/article/pii/S0920410514000229