A database and workflow integration methodology for rapid evaluation and selection of Improved Oil Recovery (IOR) technologies for heavy oil fields

Conventional crude oil, as the current dominant energy source, is an unrenewable resource. Despite the improvement of alternative energy technology such as wind and solar, there is still a large gap between the capability of these systems for supplying energy and global energy requirements. Therefore, until technological innovations facilitate sufficient energy generation through alternative fuels, other means of sustaining crude oil production such as improved oil recovery methods (IOR) could be utilised. In addition to increasing production of conventional oil, IOR methods have the potential to extract oil from unconventional reservoirs such as heavy oil fields. This capability is of importance due to the large size of global heavy oil reserves. There are several IOR technologies available and each of them is suitable for a certain type of oil field. The aim of this paper is to suggest an alternative low cost and quick screening method to the more technical and costly methods for selecting the most suitable technology for heavy oil extraction project utilising a limited range of data. After reviewing the mostly applied IOR methods and available empirical reservoir modelling correlations, it was concluded that a two-stage screening method, the first one based on literature data of previous projects and the second one based on the simple empirical oil production methods such as Marx and Langenheim model coupled with Ingen’s RAVE (Risk and Value Engineering) and Schlumberger’s PIPESIM, could give reasonable results and eliminate the unsuitable methods effectively with minimum cost and time during the preliminary stages of a project. This effectiveness was tested via a comprehensive case study.