This study focuses on evaluating the differences between describing the asphaltene fraction using a unique average molecular structure compared to a multistructural approach by examining its effects on aggregation behavior using molecular dynamics (MD). Three asphaltene representations were considered: a hydrocarbon skeleton (without heteroatoms), a single average asphaltene molecular structure (with heteroatoms), and a multistructural approach (several molecular structures with different characteristics and heteroatom contents). Representative aromatic and resin molecules were also employed to describe the maltene content. In total, four systems were evaluated by MD simulations: the three asphaltene representations dissolved in Heptol-50 (1:1 mixture of n-heptane and toluene) at a concentration of 7 wt %, as well as the multistructural representation of asphaltenes dissolved in its respective maltene. Asphaltene aggregation behavior was assessed by using the radial distribution function (RDF), the asphaltene− asphaltene interaction energies, and the size distribution of the asphaltene aggregates. Our results showed that a multistructural approach to describing asphaltenes could more accurately reproduce the elemental analysis data than the conventional unistructural approach. In addition, the size of the asphaltene aggregates was found to double when a multistructural representation was used compared to a unistructural approach. Furthermore, heteroatom content increased the average size of the asphaltene aggregates by a factor of 1.5. Interestingly, maltenes reduced aggregate sizes by a factor of 0.4, presumably due to the role of the resins and the structure of the aromatic compounds proposed for the synthetic crude oil. Consequently, this study presents a promising approach to describe the complexity of oil by using a multistructural representation of the asphaltenes, which can be extensively implemented in molecular dynamics studies.
Asphaltene precipitation can promote a drastic reduction in oil production because of asphaltene precipitation and deposition damage. Therefore, screening models to predict the risk of asphaltene damage and equations of state (EoS) to predict the asphaltene onset pressure (AOP) are useful to prevent production drops and optimize the management of oil resources. Most asphaltene screening models have been focused on the oil compositions (SARA analysis); however, these screening models do not consider key variables for asphaltene stability such as the temperature, pressure, well depth, gas–oil ratio, and so on. As the EoS are typically based on experimental data, to fit parameters needed to reproduce the experimental AOP, expensive laboratory analyses are required for this objective. In this study, a classification machine learning (CML) model based on support vector machines was proposed to predict the asphaltene damage risk from the asphaltene stability class index data and the in situ live crude oil densities. In addition, a model based on linear regression (MLR) to predict AOP from the reservoir pressure, saturation pressure, temperature, and some in situ live crude oil compositions was proposed. In total, 24 crude oils were evaluated experimentally to propose the classification model, and a perfect classification accuracy (100%) was obtained in both cases. The CML results were compared to compositional screenings, where classification accuracy was between 29 and 88%, the best accuracy being obtained from the well-known de Boer plot. The MLR model was obtained from data from 53 live crude oils, using hypothesis tests to select the statistically representative characteristics regarding AOP, and a determination coefficient of 0.77 was obtained. The proposed integrated ensemble model contributes to predicting the potential risk of damage due to asphaltene precipitation and estimating a pressure range where these asphaltenes precipitate, allowing the necessary preventive measures to be taken to avoid an oil production decline.
Summary In this study, an integrated machine learning (ML) model was proposed that allows to identify the risk of organic precipitation damage and estimate the asphaltene onset pressure (AOP). In addition, an estimation of the association parameters to estimate the AOP using a Cubic-Plus-Association (CPA) equation of state (EoS) using stochastics (Monte Carlo) and ML approach was carried out. To predict the asphaltene damage risk the asphaltene stability class index (ASCI) data and the in-situ live crude oil densities were used along with the support vector machines (SVM) method. To propose the AOP-ML model a dataset of 53 samples was considered, evaluating different ML methods. In both cases, 80 % of the dataset was used to train the model, whereas 20% was to validate it. In the Monte Carlo (MC) simulations, 6 fluids taken from literature were used. The ML classification model had a perfect accuracy (100 %), which was compared to conventional compositional asphaltene screening models, with a classification accuracy of 33% for the resin/asphaltene ratio, 29% for ASI, 67% for CII, and 88% for de Boer plot. The AOP-ML model described properly the 77% of the variation of the experimental AOP of the 6 fluids evaluated using a stepwise bidirectional linear regression with 9 input features. Finally, the MC results indicated that several combinations of association energies and volumes reproduce the experimental AOP, obtaining a linear model for estimating the cross-interaction energy with a coefficient of determination of 0.934. This study provides disruptive findings since it opens the possibility of formulating predictive EoS, obtaining the association parameters from a fluid's compositional and structural characteristics. This approach is an opportunity for a comprehensive understanding of asphaltene precipitation damage that allows to understand the mechanisms of formation damage and therefore look for promising solutions to restore the productivity of fields affected by asphaltene precipitation formation damage.
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