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

Benoit Creton,Fanny Oukhemanou,Isabelle Lévêque

doi:10.2516/ogst/2019002

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.

Highlights

Microemulsions are commonly encountered in many of products or formulations dedicated to various domains such as pharmaceuticals, cosmetics, or petroleum applications
We report various Quantitative Property Property Relationships (QPPR) models to predict Equivalent Alkane Carbon Number (EACN) of dead crude oil knowing a series of experimental data such as American Petroleum Institute (API) gravity, C20À fraction, and fractions of saturates, aromatics, resins, and asphaltenes, see Table 1
In order to assist and speed up experiments necessary for the formulation design, we recently proposed a model based on thermodynamics to predict EACN of live crude oil

Summary

Introduction

Microemulsions are commonly encountered in many of products or formulations dedicated to various domains such as pharmaceuticals, cosmetics, or petroleum applications Among these latter, crude oil extraction after applying primary and secondary recovery methods can be roughly estimated to half of the initial oil reservoir content, according to the considered field [1]. The Chemical Enhanced Oil Recovery (cEOR) technique involving combinations of alkali, surfactants and/or polymers aims at decreasing water/oil Interfacial.

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