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Reservoir characterization and asset management require comprehensive information about formation fluids. In fact, it is not possible to find accurate solutions to many petroleum engineering problems without having accurate pressure-volume-temperature (PVT) data. Traditionally, fluid information has been obtained by capturing samples and then by measuring the PVT properties in a laboratory. In recent years, neural network has been applied to a large number of petroleum engineering problems. Indoi:10.22050/ijogst.2017.70576.1373 doaj:0132e5396948475795594bdecb7c6a24 fatcat:sngw6eh2uvc3blq5btkpzj7xvi