A Predictive Modeling Framework for Pressure Drop Estimation and Optimal Design of Pressure Boosting Stations in Oil and Gas Transmission Pipelines

Abstract

Accurate estimation of pressure drops is a critical component of efficient operation of transmission pipelines as well as pipeline design of oil and gas pipelines. Although they are effective in some situations, traditional techniques are not always effective and they lack flexibility and precision in multi-flow regime and structure of pipelines that are large in dimensions. According to the given research paper, a generalized predictive modeling framework can be proposed to be used to supplement the traditional hydraulic correlations with the assistance of analytically obtained, as well as computationally efficient expressions. The model is a combination of the physical variables flow rate, pipe diameter, roughness, fluid properties and length to provide a more accurate prediction of pressure losses. Besides estimation, the model is also employed in determining as well as optimization of the design and space layout of pressure boosting stations that is a key aspect of ensuring sustainability of operational pressure and energy savings through employing long distance pipelines. The sensitivity tests and the comparative analysis confirm the applicability of the framework to a broad range of operation situations. Results have shown that the enhanced model has high advantages in terms of predictive power and power efficiency and is a scalable, versatile solution to engineering issues in oil and gas infrastructures. The research will help to make the operation of the pipeline more sustainable and cost-efficient and make a wise choice in the conditions of the real life.