Failures in pipeline transportation of crude oil have numerous adverse effects, such as ecological degradation, environmental pollution and a decrease in revenue for the operators, to mention a few. Efficient data and service management can predict and prevent these failures, reducing the downtime of the pipeline infrastructure, among other benefits. Thus, we propose a two-stage approach to data and service management in Leakage Detection and Monitoring Systems (LDMS) for crude oil pipelines. It aims to maximise the accuracy of leakage detec- tion and localisation in a fault-tolerant and energy-efficient manner. The problem is modelled as a Markov Decision Process (MDP) based on the historical incident data from the Nigerian National Petroleum Corporation (NNPC) pipeline networks. Results obtained guarantee detection in at least two deployed nodes with a minimum localisation accuracy of 90%. Additionally, we achieved approximately 77% and 26% reduction in energy consumption compared to a pessimistic strategy and a globalised heuristic approach, respectively.