The global LNG industry is changing. Evolving market structures and transformation of the value chain are challenging conventional industry models.
On one hand, the opportunity drivers – such as the emergence of spot and near term markets and the proliferation of new markets – are enabling LNG players to realise the arbitrage in their portfolios. On the other hand, the margin drivers, led by the commoditisation of LNG and the convergence of gas markets, are eroding traditional margins. As a result, players are having to adopt new strategies to drive profitability by extracting value from imbedded optionality.
Figure 1: Opportunity and Margin Drivers for Optimisation in LNG
This optionality is specific to individual portfolios, and how – and the extent to which – market participants choose to optimise depends on their strategic ambitions and risk appetite. For example, this can involve a simple voyage optimisation or more complex destination and diversion optimisations. Some players may even look to integrate their LNG portfolio with their gas business, e.g., using storage capacity as a time spread option on the price of regional gas markets.
A player with a well-optimised portfolio would be able not only to execute such individual use cases but also to perform these together as a series of interdependent portfolio-level optimisations.
Optimisations by their very nature are computational exercises and reliant on robust systems and data-processing capability. The key to capitalising on portfolio optionality is to invest in the right technology architecture and data governance.
We set out five fundamental requirements drivers to help identify the technology and data needs:
Figure 2: Optimisation Capability Considerations
Hence, depending on the requirements, optimisation capability can mean very different things for different players.
If the requirements are to run a simple voyage optimisation, an off-the-shelf software might be sufficient. But if the ambitions are to do more complex destination/diversion optimisations alongside capacity optimisations, players might need to invest in a series of tools, which can include a custom-built optimisation solution.
Any optimisation tool should not be viewed as a standalone piece of software; rather, it is a business capability that needs to integrate with an organisation’s trading and operating systems architecture.
Organisations that have successfully managed to build optimisation solutions achieved this by having a clear view of the portfolio optionality and arbitrage they were looking to harvest, and how those fit within their risk mandate. They develop their optimisation tools around their existing infrastructure, and any legacy system gaps are identified upfront.
Figure 3: Imbedding Optimisation in the Technology and Data Ecosystem
An optimisation solution needs to function in an ecosystem of data flows, and the output of any optimisation is only going to be as good as the data provided. Industries at the forefront of data analytics innovation see data as an asset. The scale and diversity of data for an LNG optimiser are broad, ranging from publicly available weather and price data to contractual commitments within Sales and Purchase Agreements (SPAs) and asset-specific information, such as production cycles boil-off rates, regasification rates, etc. All this data comes in a variety of formats (structured vs. unstructured), from multiple sources, and is updated at different times. It is therefore important to have an effective data governance and maintenance strategy.
In summary, a good optimisation solution will seamlessly bring technology and data together with rapid processing to allow for timely execution on output.
When it comes to portfolio management and optimisation, the LNG industry is less advanced than other commodity industries – at least in part because of a greater operational complexity. If we draw parallels with oil, pipeline gas or power trading, LNG has a long way to go, but this also means that there is a lot of potential value to be captured. Successful optimisation – and capturing the value associated with it – relies on portfolio players’ ability to manage the operational complexity of LNG by effectively harnessing data and leveraging technology.