The vast amount of historical and real-time data in intelligent/smart fields can be used to make efficient decisions for improving field production. However, large volumes of data may not necessarily result in high-quality decisions, especially when uncertainty exists in the field development. In this paper, a decision-making approach that can be applied to the problems related to key performance indicators in intelligent/smart fields is presented. The proposed system involves two novel indices, well present performance index (WPPI) and well future potential index (WFPI), which integrate the present and future performance, value, and contribution of each well (production and injection) in the intelligent field. WPPI is an indicator of how the well is performing currently, and WFPI determines the future value of the well to help make workover or shut-in decisions. WPPI and WFPI offer more global solutions, while known key performance indicators focus on field-, pattern-, or well-level analysis. The proposed system can rank the wells in the field using a digital oilfield workflow which automates the decision-making process and helps reservoir management. The main issue in using multiple attributes or performance indicators is the dependency of the attributes on each other and the low-degree representation of the significance of each attribute. As parameters are not ‘mutually-exclusive’, it becomes difficult to avoid using correlated attributes. The exclusiveness and significance of each attribute or indicator together with varying engineering judgment make the automation more difficult. It is challenging to standardize engineering judgment, but automation might be more clear and applicable with a method that tries to simplify a multivariate ranking problem.

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