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

Abstract

Conventional crude oil is the currently dominant but a non-renewable energy resource. Despite the development and improvement of alternative energy technologies, there is still a large gap between the capability of renewable energy systems to capture and reliably supply power, and the ever-increasing global energy demand requirements. Therefore, until technological innovations facilitate sufficient energy generation through alternative fuels, other means of sustaining crude oil production, such as Improved Oil Recovery (IOR) methods, must be systematically explored. Beyond increasing production of conventional oil, IOR methods can effectively facilitate the extraction of oil from unconventional reservoirs, such as heavy oil fields. This capability is of high strategic importance due to the considerably large size of global heavy oil reserves.There are several IOR technologies available, but each of them is suitable only for certain oil field types. The aim of this paper is to illustrate an alternative, low-cost, quick screening method which is competitive to more technically laborious and costly methods for selecting the most suitable technology for a given heavy oil extraction project, using a limited dataset. A two-stage technology screening method is hereby proposed: the first stage is based on previous project literature data evaluation, and the second stage is based on simple empirical oil production correlation methods (such as the Marx & Langenheim model) coupled with Ingen's RAVE (Risk and Value Engineering) and Schlumberger's PIPESIM software applications. The new method can achieve reasonably accurate results and minimise cost and time requirements during the preliminary stages of an oilfield development project, as evidenced via a comprehensive case study.