A New Extended Structural Parameter Set for Stochastic Molecular Reconstruction: Application to Asphaltenes

Abstract

The modeling of complex hydrocarbon mixtures is a current issue. The presently available analytical techniques are insufficient alone to fully characterize the molecular details of heavy oil fractions to the level for new development of a molecular-level kinetic model. Stochastic reconstruction (SR) methods which build a set of molecules that mimic the properties of complex mixtures by using partial analytical data help to overcome this drawback. Although the classical SR algorithm produces reasonable molecule sets for light and medium fractions, performance degrades for heavier fractions. The main reason for this is the lack of structural parameters needed to define the variations in side-chain and ring configurations. As an extension, a novel structural parameter set including specific parameters for ring and chain configurations was implemented to the SR algorithm. In addition to this, in order to ensure an extensive structural connection between the generated molecules and the experimental data, the 1H NMR spectrum was divided into six different regions, and these hydrogen types were used in an objective function. In order to validate the SR with an extended parameter set, it has been applied to six different petroleum asphaltenes. The extended parameter set resulted in a decrease in the objective function value between 45% and 85% compared to the basic parameter set. Moreover, the extended parameter set increases the fitting ability of the SR algorithm without sacrificing the compositional space of the generated molecules.

Catalogue of Plausible Molecular Models for the Molecular Dynamics of Asphaltenes and Resins Obtained from Quantitative Molecular Representation

“…Important structural attributes including the number of aromatic rings, naphthenic rings and different lengths of aliphatic chains were adjusted according to their quantitative probability Recently, the method was further refined and applied to Turkish asphaltenes. 68 A pair of the generated molecules is shown in Figure 16. Figure 16.…”

“…Figure 16. Island (left) and archipelago (right) models of asphaltenes generated by stochastic molecular reconstruction proposed by structure generated through stochastic assembly proposed by Denitz et al 68 Sheremata et al 36 further developed the QMR generation model to incorporate structural data from 13 C NMR spectroscopy, a distribution of molecular building blocks following the archipelago-type structural framework, as well as both thioether and alkyl bridges. The authors reported a series of QMR-generated archipelago-type asphaltenes: C 283 H 337 N 3 O 4 S 9 , C 230 H 302 N 4 O 2 S 10 and C 318 H 395 N 6 O 6 S 8 V, with molecular weights of 4133 Da, 3476 Da and 4705 Da, respectively, of which the final molecule is shown in Figure 17.…”

Applications of Noncontact Atomic Force Microscopy in Petroleum Characterization: Opportunities and Challenges

“…Many structures in the literature (Figure ) can be classified as physical or chemical models. , For example, the asphaltene model proposed by Yen in 1961 is a physical model which was mainly derived from assumptions on rods, disks, and dots and physical principles of diffraction. , Many other structures can be considered as chemical models, including the average structures derived from NMR, MS, or elemental compositions. − Those deduced average structures from analyses should also be confirmed by comparing them with mixtures of known structures at known concentrations. Although these averaged or representative structures are convenient representations of the sample, they are limited by the lack of the full diversity of individual molecules and their unique features. , It should be noted that some of these models also represent the organization of structures or architectures (aggregates or clusters), or incorporation of both chemical and physical elements, and hence it is important for the reader to consult the original references to understand what information each model represents …”

Effect of Boiling Point and Density Prediction Methods on Stochastic Reconstruction

“…This process is repeated N times to produce N molecules. The number of molecules ( N ) is determined by taking into account the stochastic structure of the algorithm (reproducibility) and efficient use of time. − When the molecule generation process is complete, mixing rules are applied to calculate the properties of the mixture. An objective function that compares these calculated values with analytical data is defined.…”

“…An objective function that compares these calculated values with analytical data is defined. This objective function is nondifferentiable due to the stochastic nature of the problem and is generally minimized via simulated annealing − and genetic algorithm methods. ,, Throughout the optimization process, the location and scale parameters of each structural identifier’s PDF are adjusted to form the most compatible set of molecules with analytical data.…”