Abstract
Pilot-scale enhanced oil recovery in hydrocarbon field development is often implemented to reduce investment risk due to geological uncertainties. Selection of the pilot area is important, since the result will be extended to the full field. The main challenge in choosing a pilot region is the absence of a systematic and quantitative method. In this paper, we present a novel quantitative and systematic method composed of reservoir-geology and operational-economic criteria where a cluster analysis is utilized as an unsupervised machine learning method. A field of study will be subdivided into pilot candidate areas, and the optimized pilot size is calculated using the economic objective function. Subsequently, the corresponding Covariance (COV) matrix is computed for the simulated 3-D reservoir quality maps in the areas. The areas are optimally clustered to select the dominant cluster. The operational-economic criteria could be applied for decision making as well as the proximity of each area to the center of dominant cluster as a geological-reservoir criterion. Ultimately, the Shannon entropy weighting and the reference ideal method are applied to compute the pilot opportunity index in each area. The proposed method was employed for a pilot study on an oil field in south west Iran.
Highlights
Maximizing the Recovery Factor (RF) and economic profit are the main goals of Enhanced Oil Recovery (EOR) methods
Six important conclusions have been found: 1. An innovative quantitative and systematic approach consisting of reservoir-geology and operationaleconomic criteria has been presented
Corresponding covariance matrix and recovery factor arrays of the areas are optimally clustered in 5 and 2 clusters based on simulated annual 3-D reservoir quality maps
Summary
Maximizing the Recovery Factor (RF) and economic profit are the main goals of Enhanced Oil Recovery (EOR) methods. Pilot design plays a vital role in the road map of EOR planning and risk reduction. EOR pilots are conducted to reduce uncertainty in EOR performance [1,2,3,4]. In the last 50 years, a significant number of EOR field pilots has been studied and the gained experience has been used in the oil and gas industry [5]. The results of the implementation of EOR pilots may increase consciousness regarding hydrocarbon field behavior and EOR scenarios designed for entire hydrocarbon field. Pilot area selection is one of the imperative factors in designing an EOR [6]. The objective of the pilot location studies is determining how to narrow down the pilot candidate areas from the field extent to only an optimum area of interest
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