Effective EOR Decision Strategies with Limited Data: Field Cases Demonstration

Abstract

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.

A Novel Enhanced-Oil-Recovery Screening Approach Based on Bayesian Clustering and Principal-Component Analysis

“…The advanced method presented in details by Alvarado and Manrique (2010) and previously documented by Alvarado et al (2002), Manrique et al (2003), and Manrique et al (2009), is basically a representation in the form of two-dimensional expert maps of a database of successful EOR projects, indexed by the set of the six parameters listed above. These maps are obtained through a multidimensional data projection and allow qualitative identification reservoir clusters of EOR projects by visual inspection.…”

Development and Testing of Advanced Methods for the Screening of Enhanced-Oil-Recovery Techniques

“…Recent works support the use of classification and clustering analysis as effective tools for data mining in the field of EOR screening. The screening method developed by Alvarado et al (2002) and also presented by Manrique et al (2009) and Alvarado and Manrique (2010) is based on the representation of a database of EOR projects, indexed in terms of the above mentioned set of six parameters, on two-dimensional expert maps, obtained by means of multidimensional projection. These expert maps allow a joint comparison of multiple variables and a qualitative identification of clusters of fields which can be classified as analogous.…”