Atomistic Modelling of Oil Shale Kerogens and Asphaltenes

Transcription

1 Atomistic Modelling of Oil Shale Kerogens and Asphaltenes Julio Facelli*, Anita Orendt, Ian Pimienta, Shyam Badu, Ronald Pugmire 31st Oil Shale Symposium October 17-21, 2011 This work has been partially supported by United States Department of Energy, National Energy Technology Laboratory Award DE-FE and by and allocation of computer time at the University of Utah Center for High Performance Computing.

2 Goals Use modern computational chemistry tools to: Obtain high fidelity 3D models for kerogens and for asphaltenes Validate models by calculating spectroscopic properties and comparing to actual experimental data Use the models for further studies of behavior, including modeling of pyrolysis and the study of the interaction with the inorganic substrates Use the models to test approaches to more efficiently recover the organic material from oil shale/sands

3 2D Models 2D models in the literature are based on a variety of different experimental data Elemental analyses NMR Mass spectroscopy X-ray photoelectron spectroscopy Pyrolysis/cracking products Chemical analyses results However the 2D models are not suitable for computational modeling studies

4 3D structures are needed to: Confirm that the 2D models actually reproduce experimental data on the samples they are based upon Determine which experimental measurements are sensitive to structural differences between models Explore the chemical nature of the interaction between organic material and inorganic matrix Explore what is happening during in situ pyrolysis

5 Methodology Used Molecular mechanics (HyperChem/ReaxFF) For starting structures Optimization of larger systems Pyrolysis modeling Ab initio calculations (Gaussian/GAMESS) For structure refinement Calculation of spectroscopic properties Specialty packages to calculate other observables (DIFFUSE for pairwise distribution functions, CRYSOL for x-ray scattering curves)

6 Siskin s D model for a type I kerogen from a Green River oil shale 2D Kerogen model Siskin et al, in Composition, Geochemistry and Conversion of Oil Shales (NATO ASI Series C volume 455) 1995

7 2D models of asphaltene Siskin s D models for six representative asphaltenes Siskin et al, Energy & Fuels, 2006, 20, 1227

8 3D Kerogen Model Single unit of Siskin s 2D model after four cycles of thermal annealing and energy optimization

9 3D Kerogen Model 12 units of Siskin s 2D model over 20,000 atoms

10 Representative 3D Asphaltene models Geometry optimized paying attention to flexible linkage between aromatic and aliphatic portions Campana Mid-Continent US

11 Interaction with mineral matrix Illite (aluminum silicate clay) used Still need to determine best way to explore lowest E orientation of asphaltene on clay

12 Calculation of Experimental Data 13 C NMR chemical shifts IR frequencies Pairwise distribution functions Small angle x-ray scattering (SAXS) Pyrolysis products

13 13 C NMR of kerogen '())*+,-.)(+/)(01)*2+3(04+50()2+!6# 5!$+5%& Intensity 50() # ()! Chemical Shift $"" #""!"" %%& "!""

14 13 C NMR 14 Comparison of the spectra of the six Siskin asphaltenes Intensity Campana HC LW Maya MC SJ Chemical Shift (ppm)

15 Possible Structures of Mid Continent Asphaltene

16 13 C NMR spectra for the eight different configurations of single unit of mid continent asphaltene obtained by simulated annealing Experimental from: Siskin, M.; Kelemen, S.R.; Eppig, C.P.; Brown, L.D.; Afeworki, M. Energy & Fuels, 2006, 20,

17 13 C NMR spectra for the five different configurations of trimmers of midcontinent asphaltene obtained by simulated annealing Experimental from: Siskin, M.; Kelemen, S.R.; Eppig, C.P.; Brown, L.D.; Afeworki, M. Energy & Fuels, 2006, 20,

18 13 C NMR spectra for average of eight different configurations of single unit of mid continent asphaltene obtained by simulated annealing Average exp Experimental from: Siskin, M.; Kelemen, S.R.; Eppig, C.P.; Brown, L.D.; Afeworki, M. Energy & Fuels, 2006, 20,

19 IR Vibrational Spectroscopy Vibrational bands can be related of detailed information about functional groups present as they have different vibrational frequencies Quick and easy to obtain experimental spectra

20 Sensitivity of calculated IR spectrum Campana versus Mid-Continent US

21 Pairwise Distribution Function (PDF) Obtained from X-ray scattering from samples working with Dr. Randy Winans at the Advanced Photon Source (APS) at Argonne National Laboratory Provides information about the distribution of atom-atom distances Can predict pattern based on atomic positions from 3D model and details of shape of system

22 Calculated PDF showing the interatomic distance distribution for the different sized kerogen models

23 Comparison of Experimental and Calculated PDFs Dodecamer Model Experimental PDF 1 G(r) Radius [Å] Darren R. Locke, Randall E. Winans, Karena W. Chapman, and Peter J. Chupas. X-ray Science Division, Advanced Photon Source, Argonne National Laboratory, 9700 S. Cass Avenue, Lemont, Illinois Use of the Advanced Photon Source was supported by the U. S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE- AC02-06CH DRL acknowledges support by the Chevron Energy Technology Company through a contract with University of Utah.

24 Campana Asphaltene Pyrolysis Results Number of molecules K 2250K 2500K 2750K 3000K Time (Ps) Fragments from 3000K simulation C41H27 C5H5 C2H5 HN O C 2C3H3 5C2H3 CH3 C2H2 C5H9 C3H4 C2H CH2O 7H 2H2

25 Pyrolysis Modeling on Campana Asphaltene

26 NVT-MD simulation of (a) single unit, (b) parallel stack (c) anti-parallel stack and (d) inverted stack at different temperatures

27 Conclusions Using modern computational tools it is possible to develop 3D models of kerogens and asphaltenes Calculated spectroscopic properties show good sensitivity with respect of the models Calculated spectroscopic properties show overall agreement with our limited experimental information Even this limited comparison with experimental measurements shows need for improvement of the models