A Model for Short-Term Supply Chain Management of a LNG Production System

Selot, A.; Kuok, Loi Kwong; Robinson, Miranda; Mason, Thomas L.; Barton, Paul I.

doi:10.3997/2214-4609-pdb.147.iptc11795

Cited by 5 publications

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“…The optimisation of capital allocation using linear programming was addressed for instance by Santos and Harman (1999). With respect to the gas industry, Selot et al (2007) for example developed an optimisation model for a gas production system for a short term (two days to two weeks) supply planning periods.…”

Section: Literature Reviewmentioning

confidence: 99%

A stochastic optimisation framework for analysing economic returns and risk distribution in the LNG business

Furlonge

2011

International Journal of Energy Sector Management

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Purpose -The liquefied natural gas (LNG) business comprises a number of economic activities with inherent risks. The purpose of this paper is to propose an integrated modelling approach, as part of the investment decision-making process, for optimising economic returns from LNG whilst taking into account uncertainty in various key input parameters. Design/methodology/approach -Inter-linked cash flow and pricing models of the LNG chain were constructed. Net present value was maximised based on selection of netback pricing variables and level of investment shareholding. Constraints were placed on the minimum acceptable returns. The risk affinity of the decision maker was captured in the form of a chance-constrained optimisation problem. A genetic algorithm was applied for numerical optimisation, in combination with Monte Carlo simulations to account for the stochastic nature of the problem. Findings -Based on the results of a case study, the deterministic solution, having no consideration to uncertainty, was found to be both sub-optimal and provided an unsatisfactory risk outcome. The stochastic approach yielded an optimal solution with due consideration to risk. Various scenarios show that the choice of the decision variables significantly impacts the trade-off between risk and returns along the LNG chain to government and investor.Research limitations/implications -The suitability of the methodology to the operational phase of the LNG business which incorporates different elements of risk, such as market dynamics and logistics, is as yet untested. Originality/value -This framework may be useful in the formulation of optimal commercial structure of firms, investment portfolio and gas/LNG pricing arrangements for host governments involved in the LNG business.

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Section: Literature Reviewmentioning

confidence: 99%

A stochastic optimisation framework for analysing economic returns and risk distribution in the LNG business

Furlonge

2011

International Journal of Energy Sector Management

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“…The downstream boundary is typically a constant pressure on the inlet separator. A few publications on RTPO for the production chain from the reservoir to export are available; Foss and Halvorsen (2009), Selot et al (2007). Commercial products for RTPO are available, but not widely used.…”

Section: Introductionmentioning

confidence: 99%

Dantzig-Wolfe decomposition for real-time optimization - applied to the Troll west oil rim

Gunnerud¹,

Foss²,

Nygreen

et al. 2009

IFAC Proceedings Volumes

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Reducing Time to First Gas: Lessons Learned in Expediting and Informing International Investment Chain Decisions

Resley¹,

Reinsvold²

2008

SPE Asia Pacific Oil and Gas Conference and Exhibition

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Long-lead and risk issues in global gas developments are primary threats to value and project efficiency. Appropriate decision focus and a value-chain context provide practical ways to shorten the time to first gas as well as illuminate options to add value. Decision-based uncertainty management skills become paramount for success. The monetization of gas, particularly in liquefied natural gas (LNG) projects, requires large capital investment in most if not all links of the gas value chain from production to end use. Companies which invest in a single component of the value chain, such as non-integrated gas producers, must understand the value of and risks inherent in every link in the chain, as gas will be priced in relation to its final point of sale. Final sale price as well as the effects of processing and capacity limitations ripple their way upstream to alter the timing, demand, and viability of the upstream investment. Investment decisions and the associated risks inherent in LNG projects are legion. Although operating companies can mitigate some of the risks through experience and partnering, many above-ground factors (uncertainties) are outside of investors' control, including market evolution, regulatory changes in either the producers' or the end users' markets, technological advances/failures, as well as run-ups in capital costs of raw materials such as steel. Because of these and other uncertainties, to assess or plan the monetization of gas using an advocacy-led set of assumptions is not optimal. A clear, unbiased and integrated decision management process both optimizes the investment and shortens the time to first gas. Introduction Liquefied natural gas (LNG) projects are good examples of complex global gas development projects with huge capital investment requirements and are the focal point of this paper. To build a commercial LNG project from gas production to point of final sale actually requires investment in and development of several capital projects in the global gas chain. These linked or integrated projects serve to produce, treat, transport and sell the gas into specific markets as illustrated in Figure 1.1. These projects are joined together into a value chain via at least 16 major commercial contracts, beginning with a production sharing contract at the wellhead and ending with a gas sales agreement into the end use market. The entire set of investment decisions in the LNG chain may be driven by objectives such as being able to book gas reserves by a certain date, optimizing the entire gas portfolio of assets, and in the case of companies with a portfolio of LNG assets, the potential for arbitrage and being able to leverage and trade around each LNG asset.

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A Model for Short-Term Supply Chain Management of a LNG Production System

Cited by 5 publications

References 0 publications

A stochastic optimisation framework for analysing economic returns and risk distribution in the LNG business

A stochastic optimisation framework for analysing economic returns and risk distribution in the LNG business

Dantzig-Wolfe decomposition for real-time optimization - applied to the Troll west oil rim

Reducing Time to First Gas: Lessons Learned in Expediting and Informing International Investment Chain Decisions

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