Chemical Logistics

Abstract

Marine transportation of crude oil has been a subject of recurrent study over a long time. Owing to the huge costs borne during marine transportation of crude, crude oil scheduling remains an issue of particular interest to both the Maritime Industry and the Oil and Gas Sector. In this work, we propose a novel approach for crude oil scheduling using the Floating, Production, Storage and Offloading Platforms (FPSOPs). Decisions based on crude transportation, crude handling, crude blending and crude delivery provide optimization opportunities to the FPSOP to minimize huge expenses which could otherwise be worth millions of dollars per day. FPSOPs can facilitate crude storage, transportation and blending in waters where either pipeline infrastructure is not cost effective or Ultra Large Crude Carriers (ULCCs) and Very Large Crude Carriers (VLCCs) are unable to enter due to shallow draughts. Scheduling in the Oil and Gas Industry using FPSOPs as the central procurement unit is typically order driven. Crude schedule is determined according to the forecasted (weekly or monthly) demand from the refineries as well as the current availability of crude in the tanks of the FPSOP. Economic and operability benefits associated with the delivery of right blend of crude at the right time are numerous. Thus, the idea is to ensure that only right quality of crude reaches the refineries from the FPSOP as per refineries? specifications. The concept of crude blending is the cause for modeling complexities in this crude scheduling problem. In order to overcome this challenge we develop a suitable model and devise a solution strategy to solve this problem closest to global optima. Multilinear problems are chiefly nonconvex in nature. The need for convex relaxation of such functions is essential to combat the infeasibilities generated by them owing to such nonconvexities. We address an effective technique for generating the tightest convex relaxation of multilinear functions using the concept of hyperplanes. We start analytically with the simple cases of bilinear, trilinear terms and extend to quadrilinear functions and numerically solve for pentalinear functions with known bounds. We also compare our convex hull of quadrilinear functions with ones existing in literature and find that ours is attractive from the standpoint of tightness and closeness from the function.