Economic and environmental analysis of a Steam Assisted Gravity Drainage (SAGD) facility for oil recovery from Canadian oil sands

摘要

As conventional oil production becomes limited, transportation fuels are being produced from other unconventional fossil resources such as oil sands. Oil sands are a combination of clay, sand, water and bitumen. Vast quantities of oil sands resources have been found worldwide. The largest known reservoir of oil sands in the world is located in the province of Alberta (Canada). Several techniques for the extraction of the oil from oil sands have been developed in recent decades. Steam-Assisted Gravity Drainage (SAGD) is the most promising approach for recovering heavy and viscous oil resources. In SAGD, two closely-spaced horizontal wells, one above the other, form a steam-injector and producer pair. The reservoir oil is heated by the injected steam and drains to the producer under the effect of gravity. First aim of this work is an economic optimization and evaluation of an hypothetical industrial scale facility (named LINK), located in Alberta. All data relating to LINK plant have been obtained from a review of the existing literature references or have been assumed. A Discounted Cash Flow Analysis (DCFA) of LINK plant has been performed. Costs of existing projects have been found in literature. The results show that the hypothetical plant LINK is a profitable investment and that the investment cost has a significant effect on the competitiveness of the LINK facility. Second purpose of the present work is an environmental analysis of the LINK plant: in order to evaluate GHG emissions from LINK plant, a LCA analysis has been carried out. The calculated emissions from oil sand production by SAGD technology have been compared with values relating to conventional crude oil pathways and to recovery and extraction of bitumen through surface mining from literature. The comparison demonstrated that SAGD is a promising technology also from an environmental point of view. An economic-environmental model for SAGD technology optimization has been developed. (C) 2015 Elsevier Ltd. All rights reserved.