Selecting the optimum combination of enhanced oil recovery (EOR) technologies is a critical activity during preparation of a successful business plan. This is usually accomplished by using protocols with technical parameters at reservoir formation levels from which candidates EOR technologies are screened and then subject to investment and risk assessment evaluation for final decision by management.

The previous approach very often misses the effect of operational expenditures, wearing and failure related downtime and learning curve during pilot testing on oil recovery efficiency and EOR economics which is usually expressed in terms of net present value.

This paper proposes an alternate simplified approach using an acceptable representation of life cycle phases of natural and physical asset components functioning as a system of assets for a particular subject reservoir. We introduce a taxonomic solution to help in classifying candidates EOR technologies. Four major functional indices are used for handling uncertainties and risks using data from analogs for defined scenarios in the subject reservoir: 1-Accesability) Footprint effects for constructing and operating physical assets (wells and surface infrastructure), 2-Contactability) Volume of resources contacted from a surface location, 3-Produceability) Volume of producible resources from drainage area to surface and 4- Effectiveness) Combined effect of availability, reliability, maintainability y and capability in efficiency of oil recovery.

The four major indices are cross referenced with life cycle cost (LCC) and estimated ultimate recovery (EUR) using Hubbert peak oil theory and the Petroleum Resources Management System classification for resources and reserves. Uncertainties and risks are modeled with Monte Carlo simulation (MCS) or Systems Dynamics (SD) depending on the complexity of the system of assets.

We present examples using synthetic data to illustrate the method.

This approach is worth using during EOR project definition and planning as an alternate to complex data hungry methods.

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