Abstract
Bubble point pressure (BPP) not only is a basic pressure–volume–temperature (PVT) parameter for calculation nearly all of the crude oil characteristics, but also determines phase-type of oil reservoirs, gas-to-oil ratio, oil formation volume factor, inflow performance relationship, and so on. Since the measurement of BPP of crude oil is an expensive and time-consuming experiment, this study develops a committee machine-ensemble (CME) paradigm for accurate estimation of this parameter from solution gas-oil ratio, reservoir temperature, gas specific gravity, and stock-tank oil gravity. Our CME approach is designed using a linear combination of predictions of four different expert systems. Unknown coefficients of this combination are adjusted through minimizing deviation between actual BPPs and their associated predictions using differential evolution and genetic algorithm. Our proposed CME paradigm is developed using 380 PVT datasets for crude oils from different geological regions. This novel intelligent paradigm estimates available experimental databank with excellent accuracy i.e., absolute average relative deviation (AARD) of 6.06% and regression coefficient (R2) of 0.98777. Accurate prediction of BPP using our CME paradigm decreases the risk of producing from a two-phase region of oil reservoirs.
TOWARD PREDICTIVE MODELS FOR ESTIMATION OF BUBBLE-POINT PRESSURE AND FORMATION VOLUME FACTOR OF CRUDE OIL USING AN INTELLIGENT APPROACH
