Equivalent alkane carbon number of crude oils: A predictive model based on machine learning - Archive ouverte HAL Access content directly
Journal Articles Oil & Gas Science and Technology - Revue d'IFP Energies nouvelles Year : 2019

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

(1) , (1) ,
1
Benoit Creton
• Function : Correspondent author
• PersonId : 940486

Connectez-vous pour contacter l'auteur
Isabelle Lévêque
• Function : Author
Fanny Oukhemanou
• Function : Author

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.

Domains

Physics [physics]

Dates and versions

hal-02076397 , version 1 (22-03-2019)

Identifiers

• HAL Id : hal-02076397 , version 1
• DOI :

Cite

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, 2019, 74, pp.30. ⟨10.2516/ogst/2019002⟩. ⟨hal-02076397⟩

37 View