Equivalent Alkane Carbon Number of Live Crude Oil: A Predictive Model Based on Thermodynamics

Abstract

-We took advantage of recently published works and new experimental data to propose a model for the prediction of the Equivalent Alkane Carbon Number of live crude oil (EACNlo) for EOR processes. The model necessitates the a priori knowledge of reservoir pressure and temperature conditions as well as the initial gas to oil ratio. Additionally, some required volumetric properties for hydrocarbons were predicted using an equation of state. The model has been validated both on our own experimental data and data from the literature. These various case studies cover broad ranges of conditions in terms of API gravity index, gas to oil ratio, reservoir pressure and temperature, and composition of representative gas. The predicted EACNlo values reasonably agree with experimental EACN values, i.e. determined by comparison with salinity scans for a series of n-alkanes from nC 8 to nC 18 . The model has been used to generate high pressure high temperature data, showing competing effects of the gas to oil ratio, pressure and temperature. The proposed model allows to strongly narrow down the spectrum of possibilities in terms of EACNlo values, and thus a more rational use of equipments.Résumé -Développement d'un modèle pour prédire l'alcane équivalent d'une huile vive -En se basant sur de récents travaux de la littérature ainsi que sur de nouvelles données expérimentales, nous proposons un modèle permettant de prédire l'EACNlo (Equivalent Alkane Carbon Number of live crude oil) -alcane équivalent d'une huile vive (pétrole brut contenant des gaz dissous) -dans des contextes de recherche de tensioactifs pour des activités EOR. Pour mettre en oeuvre ce modèle, il faut s'assurer de la connaissance des conditions de pression et de température dans le réservoir et du rapport gaz sur huile. En outre, des propriétés volumétriques d'hydrocarbures nécessaires pour alimenter le modèle, sont estimées par l'utilisation d'équations d'état. La validation de notre modèle a été réalisée à partir de données expérimentales de la littérature et nos propres données obtenues pour des pétroles bruts. Ces différents cas d'études couvrent de larges gammes en termes de degrés API, de rapports gaz sur huile, de pressions et températures en condition de fond (réservoir) et de compositions pour le gaz représentatif. L'utilisation du modèle conduit à des valeurs d'EACNlo en accord avec les valeurs expérimentales, i.e. obtenues par comparaisons avec des analyses de salinités sur des séries d'alcanes linéaires de nC 8 à nC 18 . Le modèle a ensuite été utilisé pour générer des prédictions et ainsi étudier l'impact sur les prédictions des conditions de pressions et de températures et de la nature du gaz. Le modèle permet de restreindre la gamme de valeurs d'EACNlo et ainsi une meilleure utilisation des équipements expérimentaux.

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

“…Live oils are oils containing dissolved gases at specific temperature and pressure conditions. Creton and Mougin proposed a model based on thermodynamics to predict the EACN of a live oil (EACNlo) knowing the EACN of the dead oil (stock tank oil), reservoir pressure (P) and temperature (T) conditions, as well as the gas to oil ratio (R si ) [14]. This model is based on a volumetric mixing law applied to EACNdo and EACN of gas (EACNg), as follows:…”

“…Molar volumes were calculated using the SRK [15] Equation of State (EoS) applied with the volume correction proposed by Péneloux et al [16]. Creton and Mougin validated their model for several crude oils covering broad ranges of reservoir characteristics, and studied impact of pressure, temperature, and gas composition on predicted live oil EACNs [14]. These latter crude oils are part of our database (Tab.…”

“…To determine EACN of hydrocarbon mixtures, Cayias et al [12] and Cash et al [13] proposed the use of a mixing rule in which individual hydrocarbon EACNs are weighted according to corresponding mole fractions. In the case of live oilsoils containing dissolved gases at specific temperature and pressure conditionswe recently proposed an approach to predict the EACN of live oil (EACNlo) on the basis of volumetric fractions of oil and gas [14]. Indeed, Marliere et al have experimentally shown that EACN linearly varies with the gas volumetric fraction [7].…”

“…The volumetric fractions of light hydrocarbons are estimated using the Soave-Redlich-Kwong (SRK) equation of state [15] with the volume correction as proposed by Péneloux et al [16]. The use of our model for the prediction of live crude oil EACN [14] would gain in relevancy developing methods to predict crude oil EACNdo. A crude oil contains thousands of diverse chemicals and the exact composition is never known, as a consequence the combined use of above mentioned models and mixing rules to predict EACNdo is unrealistic.…”

Chemoinformatics at IFP Energies Nouvelles: Applications in the Fields of Energy, Transport, and Environment

“…In the frame of research performed by “The EOR Alliance“, we proposed the development of QSPRs to predict the optimal salinity for some surfactant families, and a temperature of 40 °C, a brine (H 2 O + NaCl), and an oil (n‐dodecane). [82],[84] In the objective to speed up the ASP formulation procedure, we also proposed models based on thermodynamics …”