Skip to main content

Sempra Energy subsidiary signs refined fuels terminal contract

Published by , Editorial Assistant
Hydrocarbon Engineering,


Sempra Energy has announced that its Mexican subsidiary, Infraestructura Energética Nova S.A.B. de C.V. (IEnova), has signed a long-term contract with a subsidiary of Marathon Petroleum Corp. (MPC) for approximately 50% of the 1 million bbls initial capacity of the Topolobampo refined fuels marine terminal in Sinaloa, Mexico.

Under the agreement, MPC's subsidiary will have storage capacity of 500 000 bbls of refined fuels that will provide access to new international supplies of fuel to meet the growing demand in the West Coast region of Mexico. Commercial operations of the approximately US$150 million marine terminal are expected to commence in late 2020.

Last month, IEnova announced that it signed a long-term contract with Chevron Combustibles de Mexico S. de R.L. de C.V. for 50% of the initial capacity of the terminal. IEnova will be responsible for the development of the liquid fuels project, including obtaining permits, engineering, procurement, construction, financing as well as maintenance and operations.

IEnova also announced last month that it is constructing a 1 million bbls liquid fuels project in Baja California, Mexico.

Read the article online at: https://www.hydrocarbonengineering.com/tanks-terminals/24102018/sempra-energy-subsidiary-signs-refined-fuels-terminal-contract/

You might also like

The Hydrocarbon Engineering Podcast - Education and training for every phase of the insulating system design process

In this episode of the Hydrocarbon Engineering Podcast, Brandon Stambaugh, Owens Corning Director for Technical Services, joins us to discuss engineers’ demand for education and training to support the critical phases that affect the performance and longevity of insulating systems.

Tune in to the Hydrocarbon Engineering Podcast on your favourite podcast app today.

Apple Podcasts  Spotify Podcasts  YouTube

 
 

Embed article link: (copy the HTML code below):