Equivalent Alkane Carbon Number Research Articles

Comparison of Machine Learning Approaches for Prediction of the Equivalent Alkane Carbon Number for Microemulsions Based on Molecular Properties.

The chemical properties of oils are vital in the design of microemulsion systems. The hydrophilic-lipophilic difference equation used to predict microemulsions' phase behavior expresses the oils' physiochemical properties as the equivalent alkane carbon number (EACN). The experimental determination of EACN requires knowledge of the temperature dependence of the microemulsion system and the effects of different surfactant concentrations. Thus, the experimental determination is time-intensive and tedious, requiring days to months for proper separations. Furthermore, the experiments require high purity of chemicals because microemulsions are sensitive to impurities. Our work focuses on the quick and reliable predictions of the EACN with machine learning (ML) models. Due to the immaturity of ML chemical predictions, we compare three graph neural networks (GNNs) and a gradient-boosted tree algorithm, known as XGBoost. The GNNs use the molecular structures represented as simplified molecular-input line-entry system (SMILES) codes for the initial input, which allows us to assess whether geometry optimization is necessary for reliable results. The XGBoost model also begins with the SMILES representations of the molecules but uses molecular descriptors instead of geometry optimizations. The best model tested (crystal graph convolutional neural network with Merck molecular force field-94) has an error of 1.15 EACN units of the true EACN for unknown data with the errors skewed toward zero and an R2 score of 0.9.

Rapid design and optimization of complex surfactant microemulsions for high-efficiency oil extraction processes integrating experimental and modeling approaches

Microemulsions have been widely used as a means of high-efficiency oil extraction processes in the field of oil recovery, separation, deposit removal, et al. Phase behavior is considered the most reliable evidence for formulating surfactant systems. There, however, few methods exist that can predict the phase behavior of microemulsions formed by complex surfactant compositions.This work developed a modified model based on the Hydrophilic Lipophilic Deviation-Net Average Curvature (HLD-NAC) model that could rapidly and accurately predict the phase behavior and phase evolution of complex surfactant microemulsions. The phase evolution of the microemulsions with equivalent alkane carbon number (EACN), surfactant concentration, and water–oil ratio (WOR) were explored. The results indicated that the new model successfully delineated the salinity boundaries of Winsor Type III microemulsion and predicted the inversions of phase behavior. A linear correlation between the EACN and the optimal salinity was demonstrated, with a precisely defined predictive range of EACN for Winsor Type III microemulsions. A fish diagram that illustrated the evolution path of the microemulsion phase was established through concentration scanning. The WOR test suggested that Winsor Type III microemulsion could be formed through salinity regulation once the surfactant concentration exceeded the critical micelle concentration (CMC).

Reliable Equivalent Alkane Carbon Number Determination for Dead and Live Crudes in Microemulsion Systems

Reliable Equivalent Alkane Carbon Number Determination for Dead and Live Crudes in Microemulsion Systems

Exploring the impact of surfactant types and formulation variables on the EACN of crude and model oils

Characterizing the hydrophobicity of oils remains a significant challenge for the academic and industrial scientific communities, striving to optimize the formulation of Surfactant/Oil/Water (SOW) systems. Various descriptors, including the required Hydrophilic-Lipophilic Balance (required HLB) and the Equivalent Alkane Carbon Number (EACN), have been proposed to characterize oil hydrophobicity. Indeed, it is already known that the EACN of crude oils can be rapidly determined through dynamic Phase Inversion Temperature-induced (PIT) experiments. The study herein presents an alternative method to characterize the EACN of crude and model oils through dynamic Salinity-induced Phase Inversion (SPI) using two reference SOW systems, one with the ionic extended surfactant propyl heptyl propoxylated sodium sulfate (i-C3C7PO4SO4Na) and the other with the nonionic surfactant decyl tetraethylene glycol ether (C10EO4). Results reveal that the EACN of crude and model oils strongly depend not only on the chosen formulation variable but also on the nature of the surfactant. Discrepancies in EACN values for crude oils range between −0.2 and 3.3 units. These differences are justified by several factors: the segregation of the polar fraction for crude oils and its dependence with temperature, the high solubility of the nonionic surfactant in polar oils and the differing structures of both surfactants at the interface that changes the effective packing parameter of the system. When inversing a crude oil at high salinity conditions using temperature as formulation variable, a weighted average EACN value obtained with salinity from the two surfactants yields more realistic results than the EACN obtained from PIT. These findings found a direct application in the enhanced oil recovery field in which the attainment of ultra-low interfacial tension is essential to have an efficient crude oil desaturation.

The Characteristic Curvature (Cc) of Ionic Surfactants Assessed via Small-Angle X-ray Scattering.

The characteristic curvature (Cc), within the hydrophilic-lipophilic difference + net (Hn) - average (Ha) curvature (HLD-NAC) framework, is the dimensionless net curvature, -Hn·L (L is the surfactant's tail length parameter), that a surfactant acquires at the characteristic condition (T = 25 °C, no added cosurfactants, oil with an equivalent alkane carbon number (EACN) of zero and for ionic surfactants, a total salinity (S) of 1 g NaCl/100 mL). A recent article demonstrated the validity of the Cc concept, where Hn was assessed via oil and water solubilization radii. Here, we assess Hn from the characteristic length (ξ) obtained from the analysis of SAXS profiles of microemulsions produced at semicharacteristic conditions (characteristic condition but varying S). The predicted relationship, -L·Hn,semicharacteristic = Ccbi + bi·ln(S), was confirmed with the five ionic surfactants explored. The SAXS-assessed Cc (Cc = Ccbi/bi) values are consistent with those obtained from solubilization studies and phase inversion scans. The Cc-SAXS method provides a way to assess the hydrophobicity of ionic surfactants directly, avoiding the bias that could be introduced by cosurfactants in phase inversion studies and minimizing the impact of potential uncertainties in the surfactant volume to area ratio (vs/as) required to calculate the solubilization radii in the solubilization method.

Microemulsion phase behavior of active live oil

Microemulsion phase behavior of live crude oil has been investigated at 1700 psi and 55 °C using an active crude oil with a total acid number of 1.18 mg-KOH/g-oil. The live crude oil was reconstituted by adding 36.0 mol% methane to dead crude oil. Sodium carbonate was used for a salinity scan, and also as an alkali to convert the acid components in crude oil to natural soap. The equivalent alkane carbon number (EACN) of dead crude oil was determined by measuring optimum salinity of dead crude oil and several surrogate crude oils diluted with pure n-alkanes. The optimum salinity of live crude oil was compared to those of dead and surrogate crude oils to determine the EACN of the live crude oil. To keep the surfactant pseudo-phase composition constant in the ternary phase diagram, the molar ratio of crude oil to synthetic surfactant was controlled. The natural soap and synthetic surfactant were kept the same for each volume ratio of crude oil to surfactant solution. The EACN of the crude oil was found to be independent of the soap-to-surfactant molar ratio, which means EACN is an intrinsic property of oil under the experimental conditions investigated. Based on the optimum salinity shift from dead crude oil to live crude oil, the compositional effect of methane with active crude oil was measured to be about the same as for inactive crude oil. The apparent EACN of methane was found to be equal to about 10.2. The solubilization ratio of the live oil at optimum salinity was greater than the value for the dead crude oil. The results indicate that the microemulsion phase behavior of live crude oil with acidic components can be interpreted using classical EACN concepts by the same criteria as that of inactive crude oil. Thus, the effect of the soap generation reaction can be decoupled from the physical interactions.

An exception to linearity in EACN framework: Twin-tail lipophiles and n-alkanes interactions

Microemulsion phase behaviors were measured using a binary surfactant mixture consisting of twin-tail C28(O)-propoxy-epoxy-carboxylate and C19–23 internal olefin sulfonate at temperatures from 55 °C to 90 °C. Pure n-alkanes and their binary mixtures were used for the oil phase to investigate the effect of oil. By salinity scans of the model oils while keeping all other formulation variables constant, it was found that only n-tetradecane showed a remarkable negative deviation from a linear relationship between optimum salinity and alkane carbon number (ACN) besides a positive deviation in solubilization ratios at optimum compared to the neighboring n-alkanes. To our knowledge, this is the first reporting of the mismatch between ACN and apparent EACN for n-alkanes larger than n-hexane. The unexpected deviations could be attributed to a similarity in sizes between n-tetradecane and the two fourteen-atom twin-tail strands in the C28(O) lipophile, i.e. C14 and C12-O-C. Unlike other surfactants with branched lipophiles, the symmetric nature of the two strands in the C28(O) lipophile seems to play a key role. The experimental observations of the study can provide valuable insights into surfactants and microemulsions research such as practical implications on classical equivalent alkane carbon number (EACN) framework; and how to improve or optimize lipophile-oil interaction by tailoring the molecular structure of the lipophiles.

The characteristic curvature (Cc) definition and its use in assessing Cc for single ionic surfactants

AbstractThe hydrophilic–lipophilic‐difference (HLD) is a set of empirical equations that correlate the formulation conditions at phase inversion (HLD = 0). Based on partition studies for nonionic surfactants, the HLD can be interpreted as a normalized chemical potential difference between the surfactant dissolved in water and oil. The net‐average curvature (NAC) model extrapolates this interpretation into a curvature form that has been used to fit and predict the phase behavior of surfactant‐oil–water (SOW) systems. The curvature interpretation led to renaming the HLD surfactant parameter, sigma (σ), as the characteristic curvature (Cc). This work tests the validity of the curvature interpretation of the HLD, and the Cc concept, for single ionic surfactants and the use of this concept as a method to assess the Cc without the use of reference surfactants or alcohols. To this end, the net curvature of six anionic and two cationic surfactants was evaluated from solubilization data at the characteristic condition of 25°C, no added cosolvent, in the presence of an oil mixture with equivalent alkane carbon number (EACN) of zero, and as a function of salinity. These studies showed that the original HLD equation for ionic surfactant could not be interpreted as chemical potential or curvature because a salinity prefactor (coefficient) “bi” was missing. The revised equation, HLDbi = bi∙ln(S)‐kbi∙EACN+Ccbi ‐aTbi∙(T‐25°C), could now be interpreted as a curvature expression, and it was demonstrated that Cc could be obtained from curvature using the expression Cc = Ccbi/bi. This single surfactant method produces uncertainties that, for most surfactants, ranged from 0.2 to 1 Cc units, similar to the uncertainty obtained with the conventional method of Cc determination using mixtures of test and reference surfactants.

Fast Prediction of the Equivalent Alkane Carbon Number Using Graph Machines and Neural Networks.

The hydrophobicity of oils is a key parameter to design surfactant/oil/water (SOW) macro-, micro-, or nano-dispersed systems with the desired features. This essential physicochemical characteristic is quantitatively expressed by the equivalent alkane carbon number (EACN) whose experimental determination is tedious since it requires knowledge of the phase behavior of the SOW systems at different temperatures and for different surfactant concentrations. In this work, two mathematical models are proposed for the rapid prediction of the EACN of oils. They have been designed using artificial intelligence (machine-learning) methods, namely, neural networks (NN) and graph machines (GM). While the GM model is implemented from the SMILES codes of a 111-molecule training set of known EACN values, the NN model is fed with some σ-moment descriptors computed with the COSMOtherm software for the 111-molecule set. In a preliminary step, the leave-one-out algorithm is used to select, given the available data, the appropriate complexity of the two models. A comparison of the EACNs of liquids of a fresh set of 10 complex cosmetic and perfumery molecules shows that the two approaches provide comparable results in terms of accuracy and reliability. Finally, the NN and GM models are applied to nine series of homologous compounds, for which the GM model results are in better agreement with the experimental EACN trends than the NN model predictions. The results obtained by the GMs and by the NN based on σ-moments can be duplicated with the demonstration tool available for download as detailed in the Supporting Information.

Estimating Equivalent Alkane Carbon Number Using Abraham Solute Parameters

The use of equivalent alkane carbon numbers (EACN) to characterize oils is important in surfactant-oil-water (SOW) systems. However, the measurement of EACN values is non-trivial and thus it becomes desirable to predict EACN values from structure. In this work, we present a simple linear model that can be used to estimate the EACN value of oils with known Abraham solute parameters. We used linear regression with leave-one-out cross validation on a dataset of N = 80 oils with known Abraham solute parameters to derive a general model that can reliably estimate EACN values based upon the Abraham solute parameters: E (the measured liquid or gas molar refraction at 20 °C minus that of a hypothetical alkane of identical volume), S (dipolarity/polarizability), A (hydrogen bond acidity), B (hydrogen bond basicity), and V (McGowan characteristic volume) with good accuracy within the chemical space studied (N = 80, R2 = 0.92, RMSE = 1.16, MAE = 0.90, p < 2.2 × 10−16). These parameters are consistent with those in other models found in the literature and are available for a wide range of compounds.

Selection of Optimum Surfactant Formulations with Ultralow Interfacial Tension for Improving the Oil Washing Efficiency.

Ultralow oil–water interfacial tension (IFT) has provided an important basis for screening optimum surfactant formulation for improving the oil washing efficiency. Thus, it is of great significance to further investigate the selection method for surfactant systems with ultralow IFT. In this study, a selection of surfactant systems with ultralow IFT was simplified by a method of comparing the equivalent alkane carbon number (EACN) of crude oil with the minimum alkane carbon number (nmin) of surfactant mixtures. The results show that the ultralow IFT can be achieved when the nmin of optimum surfactant formulation is equal to the EACN of crude oil. Meanwhile, the oil washing efficiency experiments show that the oil washing efficiency increases with the decrease of IFT, and the optimum surfactant formulation with ultralow IFT has the highest oil washing efficiency. This study provides a more efficient way for selecting optimum surfactant formulation systems with ultralow IFT for improving the oil washing efficiency.

Effect of Ethylene Oxide Group in the Anionic–Nonionic Mixed Surfactant System on Microemulsion Phase Behavior

AbstractThe phase behavior of microemulsions stabilized by a binary anionic–nonionic surfactant mixture of sodium dihexyl sulfosuccinate (SDHS) and C12‐14 alcohol ethoxylate (C12 − 14Ej) that contains an ethylene oxide (Ej) group number, j, of either 1, 5, or 9 was investigated for oil remediation. The oil–water interfacial tension (IFT) and optimal salinity of the microemulsion systems with different equivalent alkane carbon numbers (EACN) were examined. The anionic–nonionic surfactant ratio was found to play a pivotal role in the phase transition, IFT, and optimal salinity. The minimum IFT of mixed SDHS − C12 − 14Ej systems were about three times lower than those of neat SDHS systems. A hydrophilic–lipophilic deviation (HLD) empirical model for the mixed anionic–nonionic surfactant system with the characteristic parameter was proposed, as represented in the excess free energy term . The results suggested that the mixed system of SDHS − C12 − 14E1 was more lipophilic, while SDHS − C12 − 14E9 was more hydrophilic than the ideal mixture (no excess free energy during the microemulsion formation), and the SDHS − C12 − 14E5 system was close to the ideal mixture. The findings from this work provide an understanding of how to formulate mixed anionic–nonionic microemulsion systems using the HLD model for oils that possess a wide range of EACN.

Correlation between Detergency of Different Oily and Solid Non‐Particulate Soils and Hydrophilic–Lipophilic Deviation

AbstractThis research examined the correlation between the detergency of soils with varying equivalent alkane carbon numbers (EACN) and hydrophilic–lipophilic deviation (HLD) values. The detergency of oily soils with EACN ranging from 5.2 to 16.6 was evaluated using C10‐4PO‐SO4Na as a primary surfactant system and a 1:1 binary mixture of C10‐4PO‐SO4Na and AOT as a confirmatory surfactant system (with 65/35 polyester/cotton at 25°C). These surfactant systems were characterized using HLD concepts which showed that C10‐4PO‐SO4Na was more hydrophilic (had a higher negative Cc value) than that of the mixed surfactant system. Detergency of the selected soils was evaluated at different salinities corresponding to HLD ranging from negative to positive values. The results showed that detergency of all soils increased with increasing salinity (starting with an HLD = −3.0 (Winsor Type I microemulsion)), reached the maximum at widely different optimum salinity (S*) but at an identical HLD value of zero (optimum Type III), and then decreased with further increasing salt levels corresponding to positive HLD values (Type II). The preferred HLD range from −3.0 to 0.0 showed detergency levels exceeding 80% removal with interfacial tension values (IFT) below 1 mN m−1 for all oily soils studied. Detergency of octadecane (EACN = 18, solid at 25°C) was further conducted and demonstrated that performing detergency at HLD = −3.0 to 0.0 likewise revealed superior soil removal (over 80%) versus systems with HLD values outside this range. Thus, this work highlighted the utility of using the HLD approach in designing surfactant formulations for detergency of soils with widely varying EACN.

The phase behavior and solubilization ability of nonionic surfactant-distillate fraction of crude oil microemulsion system

In this paper, phase behavior in temperature scanning of E1305/distillate fractions/water/n-butyl alcohol microemulsion system were investigated. And the effect of surfactant concentration and oil type on microemulsion solubilization ability was studied. In the research, the content of saturates, aromatics, resins and asphaltenes in distillate fractions of crude oil was analyzed. The change of phase inversion temperature (PIT), solubilized ability and temperature caused by different compositions of distillate fraction was investigated. According to the experimental results, the phase behavior of temperature scanning was Winsor Ⅰ → Winsor Ⅲ → Winsor Ⅱ. The ability of solubilizing microemulsion was improved by increasing the surfactant content. The temperature of optimum solubilization was decreased with the increase of surfactant content. When the type of oil phase was changed, the solubilized ability of microemulsion was decreased with the increase of distillation temperature. And the optimum solubilization temperature was increased. According to the results, this phenomenon was caused by the increase of wax content and equivalent alkane carbon number.

Determination of equivalent alkane carbon number for West Siberian oils as a stage of optimisation in surfactant-polymer compositions for chemical flooding

The hydrophobicity of and products can be characterised in terms of its equivalent alkane carbon number (EACN). This characteristic can be determined on the basis of the correlation between the interfacial tension data and other characteristics for homologous oils and a number of alkanes having subsequent interpretation for and products. The EACN is a useful metric for selecting an effective surfactant for the emulsification of and products. The research is aimed at determining the equivalent alkane car-bon number of various crude samples obtained in the fields of Western Siberia using standard high-performance compositions of imported and domestic industrial sulphonate surfactants. In order to determine the EACN of and products, the S* characteristic was applied representing the optimal NaCl concentration (optimum salinity) in the aqueous surfactant phase, as well as providing the minimum surface tension and formation of the maximum microemulsion volume during the phase experiment at the interface with the hydro-carbon phase. Direct determination of the interfacial tension at the oil / surfactant solution interface was car-ried out with a tensiometer using the spinning drop method at a temperature of 87 ° С . Linear dependencies are identified in accordance to the empirical correlation equations between the EACN, surfactant parameters and phase behaviour parameters of aqueous surfactant solutions and or a mixture of hydrocarbons. The K characteristic parameter of the proposed three standard surfactant compositions is determined to be consistent with the literature data for individual surfactants. The composition of industrial surfactants for determining the EACN of and products is proposed. The equations of linear regression for the logS* ~ EACN dependency with high correlation coefficients (R² = 0.9444-0.9999) are obtained, resulting in the determination of the EACN for kerosene and seven samples from Western Siberian fields. Promising surfactants can be selected on the basis of this indicator for reducing interfacial tension in the hydrocarbon / water solution system, as well as for predicting the most effective composition for obtaining emulsions.

Effect of Live-Crude-Oil Composition on Optimal Salinity of a Surfactant Formulation: Experiments and Modeling

SummaryThe objective of aqueous-surfactant formulation design is to achieve ultralow interfacial tension (IFT) with the oil in place at reservoir conditions. Several parameters have to be investigated, and among them, it is well-known that the presence of gas dissolved in crude oil can greatly affect the surfactant/brine/crude-oil-microemulsion phase behavior. Omitting it might degrade the formulation performance. In this work, we present a combined experimental and theoretical investigation of optimal-salinity evolution as a function of live-oil compositions and conditions, varying the pressure independently of the gas/oil ratio (GOR) (i.e., the amount of gas dissolved in crude oil).A specific high-pressure/high-temperature (HP/HT) sapphire cell with a mobile piston is used to separately study the effect on the optimal salinity of the GOR by adding different amounts of n-alkanes methane (C1), ethane (C2), and propane (C3) at the corresponding saturation pressure, and the effect of pressure (up to 500 bar) is studied in a second cell by varying the cell volume (without changing the live-crude-oil composition).Using the HP/HT sapphire cell, we show that GOR variations (tested values up to 135 std m3/m3) induce important modifications of the brine/surfactant/oil-microemulsion phase behavior. In the case of the studied fluids, experimental data indicate that the optimal salinity of the brine/surfactant/oil system decreases linearly when increasing the amount of gas dissolved in the live crude oil. As a consequence of the Salager relation (Salager et al. 1979), the equivalent alkane carbon number (EACN) of the live crude oil varies linearly with the GOR. We demonstrate hereafter that the cell pressure alone (up to 500 bar), for a fixed composition (i.e., fixed GOR), affects neither the formation nor the stability of the Winsor III (WIII) microemulsion. Furthermore, results suggest that the composition of the dissolved representative gas can have an effect on the microemulsion phase behavior.Three models have been evaluated to estimate EACN values for 13 live crude oils and for the two live oils of interest in this study [Crude Oil 1 and n-tetradecane (n-C14)]. We compared predictions with our new experimental data. The Creton and Mougin (2016) model quantitatively predicts the behavior of live oils and agrees with the negligible effect of pressure on microemulsion formation and stability.

Characterizing the Oil-like and Surfactant-like Behavior of Polar Oils

In this work, a bifunctional model was developed to fit and predict the phase inversion point (PIP) of microemulsions containing polar oils. This model incorporated the hydrophilic-lipophilic difference (HLD) equations, where HLD = 0 at the PIP. The model uses a Langmuir isotherm to account for the interfacial segregation of polar oils as a function of their concentration in the bulk oil phase. The segregated polar oil was treated as being surfactant-like, having a characteristic curvature (Cc). The polar oil in the bulk oil phase was characterized via an equivalent alkane carbon number (EACN). The Cc value was obtained considering deviations in the PIP at low polar oil concentrations. The EACN was determined considering PIP deviations at high polar oil concentrations. Naphthenic acid and dodecanol were used as model polar oils mixed with ionic and nonionic surfactants and nonpolar oils. The EACN of the polar oil was shown to be independent of the EACN of the nonpolar oil and likely independent of the surfactant. The Cc for dodecanol was likely independent of the surfactant used. For naphthenic acid, the Cc was independent of the nonpolar oil, and within a certain surfactant type (ionic, nonionic, or extended ionic), it was likely independent of the surfactant. For the naphthenic acid systems, the segregation predicted via the bifunctional model was consistent with experimental measurements of this segregation. Given that the bifunctional model only involves phase inversion experiments, it is a convenient method to determine the oil-like and surfactant-like nature of polar oils.

A Continuous and Predictive Viscosity Model Coupled to a Microemulsion Equation of State

SummarySurfactant floods can attain high oil recovery if optimal conditions with ultralow interfacial tensions (IFT) are achieved in the reservoir. A recently developed equation-of-state (EoS) phase-behavior net-average-curvature (NAC) model based on the hydrophilic-lipophilic difference (HLD-NAC) has been shown to fit and predict phase-behavior data continuously throughout the Winsor I, II, III, and IV regions. The state-of-the-art for viscosity estimation, however, uses empirical nonpredictive based on of fits to salinity scans, even though other parameters change, such as the phase number and compositions. In this paper, we develop the first-of-its-kind microemulsion viscosity model that gives continuous viscosity estimates in composition space. This model is coupled to our existing HLD-NAC phase-behavior EoS.The results show that experimentally measured viscosities in all Winsor regions (two- and three-phase) are a function of phase composition, temperature, pressure, salinity, and the equivalent alkane carbon number (EACN). More specifically, microemulsion viscosities associated with the three-phase invariant point have an M shape as formulation variables change, such as from a salinity scan. The location and magnitude of viscosity peaks in the M are predicted from two percolation thresholds after tuning to viscosity data. These percolation thresholds as well as other model parameters change linearly with EACN and brine salinity. We also show that the minimum viscosity in the M shape correlates linearly with EACN or the viscosity ratio. Other key parameters in the model are also shown to linearly correlate with the EACN and brine salinity. On the basis of these correlations, two- and three-phase microemulsion viscosities are determined in five-component space (surfactant, two brine components, and two oil components) independent of flash calculations. Phase compositions from the EoS flash calculations are entered into the viscosity model. Fits to experimental data are excellent, as well as viscosity predictions for salinity scans not used in the fitting process.

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

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.

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

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