Outlier detection and selection of representative fluid samples using machine learning: a case study of Iranian oil fields
Mahdi Hosseini,
Seyed Hayan Zaheri,
Ali Roosta
Abstract:During the development of a field, many fluid samples are taken from wells. Selecting a robust fluid sample as the reservoir representative helps to have a better field characterization, reliable reservoir simulation, valid production forecast, efficient well placement and finally achieving optimized ultimate recovery. First, this paper aims to detect and separate the samples that have been collected under poor conditions or analyzed in a non-standard way. Moreover, it introduces a novel ranking method to scor… Show more
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