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The business processes and decisions of oil and gas operations generate large amounts of data, which causes surveillance engineers to spend more time gathering, and analyzing them. To do this manually is inefficient. Hence, this study is proposed to leverage on data driven surveillance by adopting the principle of management by exception (MBE). The study aims to minimize the manual interaction between data and engineers; hence will focus on monitoring well production performance through pre-determined parameters with set of rules. The outcome of this study is a model that can identify any deviations from the pre-set rules and the model will alert user for deviations that occur. The model will also be able to predict on when the well be offline if the problem keeps on persisting without immediate action from user. The objective of this paper is to present a literature review on the prediction and management by exception for the above mentioned well management. The results presented in this paper will help in the development of the proposed prediction and management model. The literature review was conducted based on structured literature review methodology, and a comparative study among the collected works is analyzed and presented in this paper.