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Pumps are critical infrastructure in the Oil and Gas industry, and have been widely used in pipeline transportations of petroleum products. The electrical energy needed by a pump to meet the minimum pipeline operational requirement plays an important role in the overall cost and evaluation of pumping systems performance, which has become an important issue in pump energy management and pump station designs. This paper provides a quantitative and analytical method using Bernoulli’s equation for studying energy dependence between two pumps (Booster and Mainline pumps) in series within a pump station as a function of pump’s head, flow-rate, and density. Using actual parameters from a pump station, the derived equations are validated on four different products. The densities of products are 1000 kg/cm3, 835 kg/cm3, 800 kg/cm3 and 660 kg/cm3 for Water, Automotive Gas Oil (AGO), Dual Purpose Kerosene (DPK), and Premium Motor Spirit (PMS) respectively. The results show that the energy requirement of the Booster pump is determined by the energy demand of the Mainline pump as a function of flowrate, density and pump’s head. The study is essential for developing energy saving strategy in pipeline operations and in electrical consideration when selecting the right electric motors for pumps in pump station design.

Operational efficiency and reliability of equipment directly impact production and revenue streams. Maintenance, an essential aspect of Oil/Gas operations, is a set of activities that aim at preserving the condition of equipment to reduce the probability of failure and increase operating life cycle of equipment. The culture of maintenance evolves considerably from run to failure and corrective to preventive maintenance practices. These set of activities are expensive and significantly affect the cost of sales and operating expenditure especially in Oil and Gas sector. With the need to optimize operating cost, use of data in prioritizing equipment maintenance becomes necessary. This paper provides a novel approach for prioritizing critical equipment maintenance activities using Analytical Hierarchical Process (AH). A family of Weibull distribution function are use to define five parameters (criteria) namely the Weibull Continuous Distribution Function (CDF), Weibull Probability Density Function, Reliability function, failure rate and equipment availability. To validate the use of AHP method, data from Nine (9) critical equipment from a pump station in Port-Harcourt, Nigeria was used to prioritize maintenance activities. The slope shape parameter values of \(\gamma\) \(\epsilon\){0.5,1,2} are considered, which affects the shape of the distribution functions. The results show that multi-criterion AHP-approach supports subjective decision making by providing quantitative weighted ranking of equipment based on priority. The result indicates that equipment with best values has lowest priority ranking.