Modelling approach for multi-criteria decision-making selection process for artificial lift systems in crude oil production
Artificial Lift system selection is a key factor in enhancing energy efficiency, increasing profit and expanding asset life in any oilproducing well. Theoretically, this selection has to consider an extensive number of variables, making hard to select the optimal Artificial Lift System. However, in practice, a limited number of variables and empirical knowledge are used in this selection process. The latter increases system failure probability due to pump – well incompatibility. The multi-criteria decision-making methods present mathematical modelling for selection processes with finite alternatives and high number of criteria. These methodologies make it feasible to reach a final decision considering all variables involved.
In this paper, we present a software application based on a sequential mathematical analysis of hierarchies for variables, a numerical validation of input data and, finally, an implementation of Multi-Criteria Decision Making (MCDM) methods (SAW, ELECTRE and VIKOR) to select the most adequate artificial lift system for crude oil production in Colombia. Its novel algorithm is designed to rank seven Artificial Lift Systems, considering diverse variables in order to make the decision. The results are validated with field data in a Case
study relating to a Colombian oilfield, with the aim of reducing the Artificial Lift Failure Rate.
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