SPE 113269 Effective EOR Decision Strategies with Limited Data: Field Cases Demonstration

Enhanced-Oil Recovery (EOR) for asset acquisition or rejuvenation involves intertwined decisions. In this sense, EOR operations are tied to a perception of high investments that demand EOR workflows with screening procedures, simulation and detailed economic evaluations. Procedures have been developed over the years to execute EOR evaluation workflows. We propose strategies for EOR evaluation workflows that account for different levels of available information. These procedures have been successfully applied to oil property evaluations and EOR applicability in a variety of oil reservoirs. The methodology relies on conventional and unconventional screening methods, emphasizing identification of analogues to support decision making. The screening phase is combined with analytical or simplified numerical simulations to estimate full-field performance while maintaining rational reservoir segmentation procedures. This paper fully describes the EOR decision-making procedures using field case examples from Asia, Canada, Mexico, South America and the United States. The type of assets evaluated includes a spectrum of reservoir types, from oil sands to light oil reservoirs. Different stages of development and information availability are discussed. Results show the advantage of flexible decision-making frameworks that adapt to the volume and quality of information by formulating the correct decision problem and concentrate on projects and/or properties with apparent economic merit. Our EOR decision-making approaches integrate several evaluation tools, publicly or commercially available, whose combination depends on availability and quality of data. The decision is laid out using decision-analysis tools coupled with economic models and numerical simulation. This allows integrated teams to collaborate in the decision making process without over-analyzing the available data. One interesting aspect is the combination of geologic and engineering data, minimizing experts' bias and combining technical and financial figures of merit rationally. The proposed methodology has proved useful to screen and evaluate projects/properties very rapidly, identifying whether or not upside potential exists. Introduction This paper illustrates a set of strategies developed over a number of years to deal with improved/enhanced-oil recovery (IOR/EOR) decisions. In this paper, we are not concerned as to what IOR or EOR are or are not. Almost any strategy that leads to increase recovery is under consideration for EOR/IOR decisions. The current oil market has triggered a significant increase in property acquisition and the launching of enhanced-oil recovery projects. Tied to this competitive scenario is the scarcity of a properly trained workforce to deal with some decision challenges. In this scenario, the saying “Time is money” could not more factual. This will be referred to as one of the time constraints that decision makers face. Excessive emphasis on prescriptive approaches to decision analysis in the absence of context bypasses the real purpose of decision analysis. What decision makers require is recommendations (support) based on realistic information on the state of knowledge. Lack of context in decision making can be particularly effective in destroying value in development plans based on EOR strategies. Inadequate focus can cause decision delays, which in turn lead to the risk of losing windows of opportunity. In practice, reservoirs do not remain static during any exploitation phase, even if we do not do anything. The latter is one of the possible time constraints for a decision. Bidding for property acquisition is another. On the other hand, it has been shown that falling into the over-analysis trap actually destroys value, because chances for success can diminish if the number of decisions is perilously limited (Begg and Bratvold, 2003). One of the reasons for attachment to over-analysis probably comes from a deeply rooted belief in the Oil and Gas industry that analysis through modeling can reduce uncertainty. The overconfidence bias associated with the belief that numerically accurate reservoir dynamic models can overcome the hurdles of ambiguity or even uncertain data sources is baseless. The