University of Twente Student Theses


Asset wide optimization in shell's LNG upstream value chain

Brinkhof, S. (2013) Asset wide optimization in shell's LNG upstream value chain.

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Abstract:Asset Wide Optimization (AWO), or Enterprise Wide Optimization (EWO) as it is also referred to, is a new and emerging research area that combines technical engineering disciplines such as chemical engineering with operations research techniques. It focuses on optimizing business operations on a global level, instead of optimizing assets to their individual objectives. Key feature of AWO is the integration of information and decision-making among the various assets that comprise the value chain of the company. AWO provides several benefits from a business perspective, such as: (1) Cost reduction and associated margin maximization from the integrated gas value chain (2) Maximizing exploitation of (short term) market situations, related to spot opportunities (3) Enabling a more efficient and faster response to upset situations by making optimal operational changes In general, AWO involves optimizing the operations of supply, manufacturing and distribution activities of a company to maximize operational profits. It has become a major goal in the process industries due to the increasing pressures for remaining competitive on the global market. A major focus is the optimal operation of manufacturing facilities, which often requires the use of (non-)linear process models. Major operational items include planning, scheduling, real-time optimization and inventory control. Liquefied Natural Gas (LNG) In the petroleum industry, associated Natural Gas is often found in presence of crude oil. Historically, this ‘byproduct’ was released as a waste product by burning it off in gas flares. Both environmental issues and the increase in demand for alternative energy sources make processing and selling the gas commercially attractive. The Natural Gas (mainly methane) is cooled to -160◦C and becomes a colorless, non-toxic liquid that occupies up to 600 times less space. This enables profitable shipment in special LNG carriers, each with a capacity of over 200,000 cubic meters. At its destination the liquid LNG is then returned to gaseous state at regasification facilities and distributed to homes, businesses and industries through the existing gas network. The Liquefied Natural Gas (LNG) value chain is defined as all business activities from exploration at the on- or offshore wells, until the gas reaches its final customer. The focus of this thesis is on the upstream activities, comprising production and inventory management to ensure shipments to customers. The main assets are (1) Wells, (2) Production facilities (3) Storage, (4) Contracting and (5) the relation with the Oil market. We set the scope of the project to short term decisions (operational), with a planning horizon of about 30 days taking into account risks (uncertainty) where possible. Besides definitions, historical facts and market and economic details, we describe the value of an Asset Wide Optimization (AWO) model for Shell's LNG supply chain. Construction of a mathematical framework This section might be rather technical due to mathematical terminology. In Chapter 3, references can be found for a comprehensive overview of mathematical models and definitions. Based on the detailed value chain description, we have constructed a mathematical framework to support integrated AWO decision making. An obvious starting point for decision making optimization is a Dynamic Programming (DP) framework, since it is of lower computational complexity than for example the Simplex method, in combination with branch and bound in case of Mixed Integer Linear Programs (MILPs) Eventually, we propose a Mixed Integer Linear Programming (MILP) framework that is equivalent to the dynamic program. Although we lose the ‘nice’ structure of having smaller sub problems to solve the overall problem, this approach is not subject to the so called curse of dimensionality as we can use both continuous and integer variables in these models to get around the discretization step. Two stage stochastic programming To include uncertainty in the model, we proposed a two-stage stochastic program (with recourse costs) that is based on the deterministic MILP that was constructed previously. The first stage represents the decisions to be made today on contract delivery, production levels and flaring. We assume that the current (today’s) state of the system is known with certainty and the associated transition function is deterministic. The decisions on production rates at the wells, as well as shipments affect the stock level at the start (tomorrow) of the remaining planning horizon (second stage). We maximize the sum of (deterministic) direct revenue in the first stage and expected future profits over all scenarios in the second stage. The first stage decisions represent those decisions that are to be executed immediately. Second stage decisions represent future decisions, given the possible realization of scenarios from a pre-defined scenario set. If this pre-defined set is large, computational complexity of the increases drastically since all variables and constraints in the two stage stochastic MILP are duplicated for each scenario.
Item Type:Essay (Master)
ORTEC, the Netherlands
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:31 mathematics
Programme:Applied Mathematics MSc (60348)
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