Equivalent alkane carbon number of crude oils: A predictive model based on machine learning

Abstract : In this work, we present the development of models for the prediction of the Equivalent Alkane Carbon Number of a dead oil (EACNdo) usable in the context of Enhanced Oil Recovery (EOR) processes. Models were constructed by means of data mining tools. To that end, we collected 29 crude oil samples originating from around the world. Each of these crude oils have been experimentally analysed, and we measured property such as EACNdo, American Petroleum Institute (API) gravity and $ {\mathrm{C}}_{{20}^{-}}$ , saturate, aromatic, resin, and asphaltene fractions. All this information was put in form of a database. Evolutionary Algorithms (EA) have been applied to the database to derive models able to predict Equivalent Alkane Carbon Number (EACN) of a crude oil. Developed correlations returned EACNdo values in agreement with reference experimental data. Models have been used to feed a thermodynamics based models able to estimate the EACN of a live oil. The application of such strategy to study cases have demonstrated that combining these two models appears as a relevant tool for fast and accurate estimates of live crude oil EACNs.
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Benoit Creton, Isabelle Lévêque, Fanny Oukhemanou. Equivalent alkane carbon number of crude oils: A predictive model based on machine learning. Oil & Gas Science and Technology - Revue d'IFP Energies nouvelles, Institut Français du Pétrole, 2019, 74, pp.30. ⟨10.2516/ogst/2019002⟩. ⟨hal-02076397⟩

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