Optimization of Oil-Flow Scheduling in Branched Pipeline Systems

Abstract

The article considers a new approach to oil transportation scheduling in oil pipeline network systems. This new approach is based on a mathematical model that solves the optimization transport problem as a system of objective functions and equality and inequality constraints. The system can be varied depending on the needs of a given pipeline system. The approach allows one to compute oil-flow distribution during a certain time period (e.g., day, week, or month) with a given time sampling (e.g., hour, day, or week) considering pipeline characteristics (e.g., flow capacity and technological regimes, among others), oil properties (e.g., mass sulfur fraction and density, among others), and capacity of tank terminals. In addition, the approach enables one to optimize oil transportation in terms of energy consumption. The possibilities of the proposed approach are shown using a real system of 10 oil pipelines, 9 branches, 4 transitional tank terminals, 3 oil suppliers, and 6 oil consumers. The result of the flow distribution calculation in a branched system, which is the schedule of cargo flows for each pipeline in a whole pipeline system with all constraints satisfied and optimized objective function during a 1-month (744 h) time period with a 1-h time sampling, is shown.

Optimal economic operation of liquid petroleum products pipeline systems

“…Further, we assume that the liquid commodity is homogeneous, so that properties such as density and viscosity are constant and uniform throughout the system. Following precedent, we suppose that the inaccuracies caused by these homogeneity assumptions are minor 5 . We do not account for any changes in temperature and do not model maintenance scenarios, like pigging in the system.…”

“…The formulations extremize the three objective functions in Equations ) subject to nonlinear equality constraints and box constraints on the state and control variables that guarantee feasibility. The first formulation is similar to the optimization goals that have been examined in previous work, 5,28 where the flow schedule or allocation is predetermined so that the quantities s j and d j for node are fixed and the objective is to minimize the (operational) cost of pumping energy.…”

“…Following precedent, we suppose that the inaccuracies caused by these homogeneity assumptions are minor. 5 We do not account for any changes in temperature and do not model maintenance scenarios, like pigging denote subsets of the edges that represent pipes and pump stations, respectively. We assume that the pair N , E ð Þ defines a connected graph.…”

“…As a result, over the past century, considerable investments have been made throughout the world in continental‐scale pipeline networks for transporting hydrocarbons. The sustained dependence on hydrocarbons for meeting modern demands for energy has compelled growing interest in long‐standing areas of modeling and optimizing the physical and engineering function of these complex networks 3‐5 . Recent studies have considered both the stationary 6 and transient regimes, 7‐9 with the predominant goals of accurately evaluating transport capacity and optimizing operational efficiency 10…”

“…A variety of studies have examined time‐dependent scheduling of single commodity transport, 33‐36 various network topologies (i.e., single‐ and multi‐source), 35,37 and optimization under uncertainty 38 . A recent formulation for optimizing flow schedules for crude oil pipelines has been proposed to minimize energy used for pumping and accounting for pipeline flow physics and pump engineering limitations 5,39 . The goal of this latest formulation is to adhere as closely as possible to a predetermined time‐dependent transport schedule and use storage tanks throughout the system as a controllable means to buffer the flow regime.…”