Case study:
Multi-site optimisation of natural gas operations

Maximising economic performance and asset utilisation under different scenarios

Gathering significant volumes of natural gas usually involves connecting to wells in different oil and/or gas fields that can be spread across vast geographical areas.

Supplying processed gas and associated liquids, such as LPG and NGL, to consumers often requires an extensive network of degassing stations, compressor stations, pipelines and gas processing plants, as well as storage facilities and shipping terminals for the liquids.

Using network modelling and optimisation technologies for strategic decision-making can yield substantial benefits, not only in economic and environmental terms but also in an improved understanding of the interaction between the various components of the process and the overall business. Such benefits cannot be achieved by the usual trial-and-error simulation methods, because of the complexity involved.

Optimising multisite complexity

The attached article, published in the Gas Processing October 2016 edition, describes how Shell applied such technologies to the multi-site natural gas network of Basrah Gas Co.’s (BGC’s) network in southern Iraq. BGC processes associated natural gas from four oilfields situated in West Qurna, North and South Rumaila and Zubair; products include dry gas and LPG for the domestic Iraqi market and LPG and condensate for export.

Using a gPROMS ProcessBuilder model of the underlying facilities, various optimisations were performed using different objective functions to cover various scenarios. These included 'normal operation' objectives – for example, maximise profit, total production or specific product yields, or minimise flaring under &nash; and abnormal or failure scenario objectives such as maximise production under equipment failure for various trains.

The results of the various optimisations were nothing short of spectacular. Under 'normal operation' scenarios, an increase in profitability of nearly 5% was identified, simply by optimising the operation all sites simultaneously to maximise asset utilisation. The results from abnormal or failure scenarios were even more pronounced.

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