Advanced Aspects of Crude Oils Correlating Data of Classical

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Advanced Aspects of Crude Oils correlating data of Classical Biomarkers and MS Petroleomics Jandyson Machado Santos, Flavia Micaella Lemos Santos, Marcos Nogueira Eberlin, and Alberto Wisniewski Jr Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.6b02362 • Publication Date (Web): 03 Jan 2017 Downloaded from http://pubs.acs.org on January 6, 2017

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Advanced Aspects of Crude Oils correlating data

2

of Classical Biomarkers and MS Petroleomics

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Jandyson Machado Santos†,‡, Flávia Micaella Lemos Santos†, Marcos Nogueira Eberlin‡,

4

Alberto Wisniewski Jr. †*

5

†

6

000, Brazil

7

‡

8

UNICAMP, Campinas, São Paulo, 13083-970, Brazil

Federal University of Sergipe, Department of Chemistry, São Cristóvão, Sergipe, 49100-

ThoMSon Mass Spectrometry Laboratory, Institute of Chemistry, University of Campinas,

9 10

ABSTRACT

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The complex geochemical characteristics of crude oils can provide important information on

12

oil generation, such as the input of organic matter and their depositional environments, also

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supporting exploration, extraction and production. This work reports the first organic

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geochemical evaluation of oils from the first commercial Brazilian onshore field

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(Carmópolis, Sergipe, Brazil) via both classical biomarkers as well as with petroleomics data

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collected via ultra-high resolution and accuracy Fourier transform ion cyclotron resonance

17

mass spectrometry (FT-ICR MS). GC/MS was used to characterize the oil composition in

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terms of n-alkanes, isoprenoids, terpanes and steranes biomarkers, whereas either positive or

19

negative electrospray ionization (ESI) and FT-ICR was used to profile the polar constituents

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of the oils. GC/MS revealed geochemical characteristics that classify the oil blends at

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different levels of thermal evolution and biodegradation. FT-ICR MS attributed molecular

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formulas to more than 3,000 polar oil constituents, allowing geochemical parameters to be

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inferred mainly from the Cn and DBE trends along for the N and O2 classes. The

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Dia27(S+R)/αααC27(S+R), %C28 and %C29 parameters, S/(S+R) αααC29, αββ (S+R)/αββ +

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ααα (S+R) C29 and %C27 biomarkers obtained from GC/MS were responsible to indicate

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similarity between samples whereas N and O2 classes provided the most distinction among

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the crude oil blends in terms of level of biodegradation and thermal maturity.

28

INTRODUCTION

ACS Paragon Plus Environment

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Crude oils are highly complex mixtures with molecules that range from small, volatile

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to large, non-volatile hydrocarbons but also include a substantial fraction of polar

3

heteroatom-containing compounds.1 Characterization of the chemical composition and

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correlation of the composition to predicted physical chemical properties of crude oils is

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challenging but is key to obtain information that could guide extraction, production,

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transportation, refining and distribution of products.2-4

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Characterization of less polar biomarkers (mostly of the n-alkanes, isoprenoids,

8

hopanes and steranes classes) have been used extensively in geochemistry studies to infer

9

crude oil characteristics such as oil-oil and oil-reservoir correlations, origin, age, maturity,

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depositional environments, and degree of biodegradation.5-11 Gas chromatography (GC/MS)12

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as well as comprehensive two-dimensional gas chromatography (GC x GC)13 coupled to mass

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spectrometry (MS) have been the major techniques used for biomarker screening in crude

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oils. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) coupled

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with electrospray ionization (ESI) has been used to characterize the most acid and basic polar

15

constituents in crude oils. The polar components, while minor, also serve as key biomarkers

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for several major oil characteristics.14,

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petroleomics has, therefore, expanded the chemical information provided by GC/MS and

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GCxGC/MS.16, 17

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Such approach known collectively as MS

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ESI FT-ICR MS petroleomics offers the unique opportunity to sort, by class, the

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thousands of polar compounds present in crude oils. From the assigned CnHmNxOxSx

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formulas, the main classes can be identified and quantitatively compared. For example, the

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relative abundances of N, NO, NS and O2 classes and their trends in terms of molecular

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weight (MW), DBE (double bond equivalent) and carbon numbers can be compared and

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associated with chemical properties.18-20 For instance, Vaz and co-workers17 and Liao and co-

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workers21 showed that the O and O2 classes can be used as indicators of the biodegradation

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level. Crude oils from some Brazilian basins have also been investigated via petroleomics

27

approach using ESI FT-ICR MS.22, 23

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The field of Carmópolis in the state of Sergipe ranks as the second largest onshore

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production site in Brazil.24 But despite its high historical, economic and social importance, of

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the crude oils from the Sergipe-Alagoas sedimentary basin have the geochemistry not been

31

thoroughly studied.25-28 Rodrigues and co-workers25 used GC/MS to classify marine


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evaporitic oils from Sergipe-Alagoas basin that are constituted of immature organic matter.

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They suggested that the formation of the detected alkyl-steranes and monoaromatic alkyl-

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steroids was due to decarboxylation of carboxylic acids during diagenesis. Rodrigues and co-

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workers26 also found via GC/MS a series of cyclohexyl-undecanoic acids in marine evaporitic

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oil samples from the same basin. More recently, Sousa Junior and co-workers28 focused their

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study on the class of isoprenoid biomarkers, and found that the low GC/MS pristane/phytane

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ratios in crude oils from the Sergipe-Alagoas Basin point to a depositional anoxic and saline

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environment. Santos and co-workers29 has previously studied crude oil blends from Sergipe-

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Alagoas basin by the view of the application of classic techniques such as thermogravimetric,

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infrared and ultraviolet spectroscopy and energy dispersive X-ray fluorescence spectrometry.

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It is also known that crude oils blends from onshore wells in Sergipe-Alagoas basin presents

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distinct physicochemical characteristics (higher concentration of water and gas from the

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upstream process and viscosity), and the injection of greater amounts of demulsifiers is

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required to treat some blends.29

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In view of the importance that the Carmópolis field for the Brazilian production of

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crude oil, and the limited knowledge of the chemical composition of its crude oils, we

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investigated the geochemistry of four crude oil blends from this field using both the classical

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geochemical GC/MS biomarker analysis, and advanced FT-ICR MS petroleomics approach

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to profile the most polar constituents.

20 21

EXPERIMENTAL SECTION

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Sergipe-Alagoas basin. The four onshore crude oils blends (CEOL1, CEOL2,

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CEOL3 and CEOL4) were provided by Petrobras UO-SEAL and were collected from the

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Oiteirinhos II station, in the Carmópolis Field on Sergipe-Alagoas basin, Sergipe state,

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Brazil. The blends are a combination of oils from many wells arriving at the primary

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treatment station through four pipelines and were collected in the same time. The blends were


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identified as CEOL1, CEOL2, CEOL3 and CEOL4, respectively, for lines 1, 2, 3 and 4 of the

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station. These samples have been studied and have been described in detail in a previous

3

work.29

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Gas chromatography/mass spectrometry (GC/MS). The GC/MS analysis were

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performed in a Shimadzu, GCMS-QP5050A instrument operated with a capillary column

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[DB-5MS 5% diphenyl, 95% dimethyl polysiloxane - 30 m; 0.25 mm ID; 0.25 µm]. The

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oven temperature program was 60 °C (3.0 min); 7 °C/min at 310 °C (14.0 min), and the

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injector temperature was 250 °C. He (99.999%) as used as the carrier gas (flow of 1.20

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mL/min – 71.8 kPa) with an interface temperature of 300 °C and a total analysis time of

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52.71 min. Analyses were performed in the linear scan mode (SCAN) in the range of m/z 40-

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550. Selective ion monitoring (SIM) was also performed for the ions of m/z 191 and 217

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(biomarker ions for terpanes and steranes, respectively). Samples were prepared with

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solutions at a concentration of 10 mg/mL in n-heptane and using an injection volume of 1 µL

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in the splitless mode (1.0 min).

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Petroleomics analysis by FT-ICR MS. Solutions for the four crude oils were

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prepared using 1 mg of the crude oil sample in toluene/methanol mixture (1:1 v:v) with a

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final concentration of 1 mg mL-1. The FT-ICR MS analysis was performed using a 7.2T LTQ

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FT Ultra mass spectrometer (Thermo Scientiﬁc, Bremen, Germany) equipped with a direct

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infusion electrospray ionization source (ESI) in both the positive and negative ion modes.

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The follow conditions were used: capillary voltage +3.5 and -3.1 kV, tube lens + and - 160 V

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and temperature 300 °C. N2 was used as the nebulization gas. Mass resolving power was

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400,000@m/z 400 and the acquisition in the ICR cell was done with 100 µscan in each run.30

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Data acquisition was performed along the range of m/z 100-2000 by the Xcalibur 2.0

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software. Identification of the ions was done by comparing its m/z values with a library of

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compounds present in the database of the PetroMS18 software based on literature search and

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standards. We considered a match between the experimental and the theoretical m/z values

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from our library when the mass error was < 1 ppm and then the molecular formulas were

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assigned for each m/z.

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Multivariate analysis. For the multivariate approach, we used the percentage of

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classes assigned for the mass spectrum of crude oils from the software PetroMS. The data

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were aligned and preprocessed by autoscaling (data mean-centering followed by variance

32

scaling), and were exploited via multivariate statistical analysis using principal component


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analysis (PCA) and hierarchical cluster analysis (HCA). The software Statistica for Windows

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v. 7.0 (StatSoft Inc., USA) was used for processing the data.

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RESULTS AND DISCUSSION

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Distribution of alkanes. Figure 1 displays the distribution of n-alkanes and branched

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alkanes (isoprenoid) in the four samples studied as obtained by GC/MS using the full SCAN

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mode. All the four crude oil blends showed bimodal chromatographic profiles which are

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characteristic of low MW paraffinic oils. Note that the alkanes were found to vary from n-C10

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to n-C35, with n-C15 and n-C17 as the major n-alkanes. For the isoprenoid class, 2,6-

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dimethylundecane;

2,6,10-trimethylundecane;

2,6,10-trimethyldodecane;

2,6,10-

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trimethyltridecane; 2,6,10-trimethylpentadecane; pristane and phytane were clearly identified.

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While the TICC profiles of Figure 1 are similar to each other, they differ from previous

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reports. Rocha and co-workers27 analyzed crude oil samples from the same SE-AL basin, and

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abundant low MW n-alkanes in the n-C13 to n-C19 range were detected only in two of the

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eight analyzed oils. Sousa Júnior and co-workers28 observed even more contrasting GC/MS

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results and an unresolved complex mixture (UCM) for crude oils from the same basin. They

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attributed these findings to the mixing of biodegraded and non-biodegraded oils. Such UCM

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behavior was not observed for the oils studied herein, and the clear bimodal GC/MS

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distribution observed from our data provides no indication of extensive biodegradation of the

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blends. Peters and Fowler3 showed that bimodal n-alkane distributions, particularly those

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skewed toward the n-C23-n-C31 range, are usually associated with terrigenous higher-plant

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waxes, which indicates that the oil has reached the early to middle oil window based on

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thermal maturation.

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Carbon preference index (CPI) and isoprenoids ratio. From the relative areas in

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TICC for the n-alkanes and isoprenoid identified in Figure 1, it was possible to calculate the

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CPI31, Pr/Ph, Pr/n-C17 and Ph/n-C18 ratios for each crude oil blend. These ratios, which are

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summarized in Table 1, can be used to predict organic geochemistry properties for crude oils.

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The CPI > 1 for all crude oils show the predominance of odd n-paraffins, suggesting that

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these samples display medium thermal maturity levels and display characteristics of

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lacustrine oils, associated to the deposition of initial organic matter.32


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The Pr/Ph < 1 ratios for all samples points to crude oils derived from anoxic

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depositional environments.31 According to Didyk and co-workers,33 such crude oils can also

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be classified as coming from marine carbonate and hypersaline environments, due the

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predominance of phytane. The Pr/n-C17 < 1 ratios suggest also an environment of deposition

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related to swampy conditions and water washing, whereas the combination of the Pr/n-C17
1 ratios indicates that the samples studied are indeed a mixture of

7

biodegraded and non-biodegraded oils.

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In previously studies, Asif and co-workers10 correlated Pr/n-C17 versus Ph/n-C18 and

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Pr/n-C17 versus API gravity (or °API) as indicative of biodegradation process in crude oils.

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The value of °API is associated with the density of the crude oil, where oils with lower °API

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are classified as heavy crude oils. The same correlation in Figure 2 (dotted arrow) shows that

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the Pr/n-C17 and Ph/n-C18 ratio increases in oils with higher levels of biodegradation. Such

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ratios for the four crude oils studied herein indicates partial biodegradation with a mixture of

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mature and immature oils. The same correlation can be seen in Figure 2 (filled arrow) with a

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smaller °API and the Pr/n-C17 ratio tending to one, pointing to crude oils with intermediate

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biodegradation levels.

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Terpanes and steranes biomarkers. The n-alkanes, CPI index, °API, and Pr/Ph,

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Pr/n-C17, Ph/n-C18 ratios previously discussed herein have pointed to oils with quite similar

19

geochemical properties. To further test such similarity, geochemical ratios obtained from

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terpanes and steranes biomarkers were calculated. The terpanes and steranes biomarkers were

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identified from the GC/MS data in SIM mode using theoretical references7, 8, 10-12, 34, 35 and

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retention indexes described previously by Hwang36 and Oudot and co-workers.37 Figures 3

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and 4 show the chromatograms used to selectively detect biomarkers for the class of terpanes

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(SIM mode for the ion of m/z 191) and steranes (SIM mode for the ion of m/z 217),

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respectively.

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From the identification of terpanes and steranes biomarkers in Figures 3 and 4, it was

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therefore possible to estimate the levels of thermal evolution and depositional environment of

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such crude oils (Table 2). The Ts/Ts+Tm ratios were lower than 1 suggesting high thermal

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evolution levels for the all four crude oils. Asif and co-workers10 showed that values for

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Ts/Ts+Tm ratio in the range of 0.31-0.73 indicate oils with medium to high thermal evolution

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levels. In a previously study of crude oils from the Sergipe-Alagoas Basin, Lima and co-


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workers34 obtained Ts/Ts+Tm on the range of 0.30-0.83 also indicating medium to high

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thermal evolution levels.

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The biomarker C29Ts/(H29+C29Ts) ratio also provided an indication of thermal

4

evolution because it is known that C29Ts is more thermally stable than H29.7 This ratio was

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quite similar for all blends (0.27-0.28, Table 2) indicating that all four crude oils exhibited

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medium levels of thermal evolution. Sousa Júnior and co-workers28 have, however, reported

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data pointing to low thermal maturity for crude oils of the Carmópolis field at Sergipe-

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Alagoas basin. Therefore, crude oils from the same field and same basin may present

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different geochemical characteristics, and the exact information is essential to plan the proper

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upstream and midstream process in oil industry.

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It has been reported that S/(S+R)αααC29 ratios (Table 2) with values > 0.55 indicate

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partial degrees of biodegradation.7 The CEOL2 sample showed a S/(S+R)αααC29 ratio
0.55 for other three samples, however, indicate considerable

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steranes biodegradation. According to Peters and co-workers,31 such biodegradation is caused

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by the selective removal of organic epimer αααC29R by bacteria.

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Frequently, the percentage of cholestanes, ergostanes and stigmastanes classes of

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steranes (%C27, %C28 and %C29 αββ) have been used to indicate conditions of deposition

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from organic matter to crude oils.38 From Table 2, the distribution of such compounds

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showed the greatest value for %C29αββ (approximately 50%), indicating for all crude oils a

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terrigenous characteristic for the deposition of organic matter.10,

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aspects were consistent with the origin for the crude oil blends under study, because they all

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came from the process of onshore exploration.

12, 39

These geochemical

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Multivariate analysis applied to biomarkers ratio. The biomarker data so far points

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to subtle differences in geochemical properties for all samples. Multivariate data analysis

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tools were then applied to better assess such similarities using parameters indicative of

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thermal evolution, biodegradation and depositional environments summarized in Table 2. The

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standardization method of data was performed using autoscaling (data mean-centering

29

followed by variance scaling). Note that the geochemical parameters Ts/(Ts+Tm),

30

C29Ts/(H29+C29Ts) and Ts/H30 failed to contribute to the multivariate data analysis and

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thus were excluded.


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Figure 5(A) shows the PCA plot for from the biomarker parameters presented in

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Table 2 for the crude oils blends, with a variance of 57.14% to PC1 and 39.91% to PC2, and

3

a total cumulative variance of 97.05%. Note that to obtain the correlation, parameters with

4

correlation values above ±0.6 were considered in Figure 5(B). From Figures 5(A-B), PC1

5

was found to most significantly contribute to differentiation with a positive weight for the

6

following parameters: Dia 27(S+R)/αααC27 (S+R) in correlation with the crude oil CEOL2,

7

and %C28 and %C29 in correlation with the crude oil CEOL3. However, a more significant

8

discrimination was attained in PC2, where the parameters influenced the formation of two

9

distinct groups. The first group was formed by CEOL3 and CEOL2 samples with a positive

10

correlation with the Dia27(S+R)/αααC27(S+R), %C28 and %C29 parameters. The second

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group was formed by the CEOL1 and CEOL4 samples with a positive correlation with the

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S/(S+R) αααC29, αββ (S+R)/αββ + ααα (S+R) C29 and %C27 parameters. As described in

13

the last section, these parameters are attributed to conditions of organic matter deposition

14

during formation of crude oils.

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We also constructed a HCA dendrogram (Figure 5 (C)) to better visualize contrasting

16

features among the four crude oils blends using the Euclidean distance for Ward's method.

17

The dendogram reinforces the trends pointed by the PCA analysis in Figure 5 (A-B). In

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Figure 5(C) the CEOL4 and CEOL1 samples were shown to be approximately 95% similar in

19

terms of thermal maturity and biodegradation, whereas the CEOL3 samples not presented

20

significant similarity with the another samples. In all, we can conclude from the GC/MS data

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that the four crude oils blends studied here are formed by a mixture of different crude oils

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with distinct geochemical characteristics, despite being from the same field of production and

23

the same basin.

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Petroleomics approach. Figure 6(A) shows the ESI(+) FT-ICR MS for the crude oils

25

blends with high abundant ions centered around m/z 550. From these spectra, the PetroMS

26

software was able to assigned 1610, 1477, 1622 and 2296 different molecular formulas to the

27

CEOL1, CEOL2, CEOL3 and CEOL4 samples, respectively, with an m/z error < 1 ppm. The

28

chemical formulas were considered to correspond to CcHhNxOxSx with c=1-100; h=1-1000

29

and x-1-4. Figure 6B summarizes the abundance of each class. Note that for all crude oils the

30

N-class was far the most abundant, which correspond to mainly pyridines and their

31

derivatives. Minor abundant NO, N2 and N2S2 classes were also detected for all four samples.

32

The NOS and O4S classes were only detected for CEOL4, indicating the presence of higher


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MW sulfur compounds. This finding corroborates with a previously study using energy

2

dispersive X-ray fluorescence spectrometry technique.29

3

Figure 6(C) shows the DBE relative abundance distribution plot for the N-class,

4

which are used to indicate the thermal evolution and aromaticity levels in crude oils.40, 41 A

5

higher thermal evolution level over geologic time tends to form aromatic rings, and

6

consequently increases the DBE levels of crude oils. Figure 6(C) shows that CEOL2 and

7

CEOL3 display lower thermal evolution levels as indicated by highest DBE at the 4-12 range,

8

whereas for the CEOL1 and CEOL4 higher DBE numbers were found at the 13-26 range.

9

This finding suggests higher unsaturation levels for the CEOL1 and CEOL4 oils as compared

10

to CEOL2 and CEOL3 oils. The ESI(+) FT-ICR MS data is also in agreement with the

11

GC/MS biomarker results. In general, the four samples oils displayed characteristics of crude

12

oils with high to medium thermal maturity levels which is slightly higher for the CEOL1 and

13

CEOL4 oils.

14

Figure 7(A) shows now the ESI(-) FT-ICR MS data for the crude oils blends, where

15

most abundant anions are centered around m/z 420. From such MS data, a total of 1935,

16

2349, 2326 and 2876 molecular formulas could be assigned for samples CEOL1, CEOL2,

17

CEOL3 and CEOL4, respectively with an m/z error < 1 ppm when considering CcHhNxOxSx

18

formulas with c=1-100; h=1-1000 and x-1-3. Figure 7(B) shows the main classes assigned for

19

the four samples. Note that when ESI(-) was used, O2-class was the major class, which

20

corresponds to fatty acids, naphthenic acids and/or derivatives, followed by the N-class which

21

corresponds to pyrroles and their derivatives. Minor components from the NO, NO2, O and

22

O3-classes could also be assigned for all four blends. According to Pan and co-workers,42 low

23

abundances for the N-class in ESI(-) suggested more biodegraded oils. Since the four crude

24

oils displayed quite contrasting abundances for the N-class during ESI(-), they seem to

25

possess different levels of biodegradation. Higher degree of biodegradation was attributed to

26

the CEOL1 and CEOL4 samples due to lower abundance of N-class. According to Vaz and

27

co-workers,17 biodegraded oils also display lower abundances for the NO-class which is

28

generated from oxidation of the N-class compounds due to aerobic or anaerobic

29

biodegradation. From Figure 7(B) it is clear that this characteristic can be assigned to the

30

CEOL1 and CEOL4 samples. Liao and co-workers21 suggested that the selective removal of

31

N-class and/or the generation of O2-class is more evident when the biodegradation level

32

increase, as observed herein for the CEOL1 and CEOL4 samples.


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The biodegradation level can also be assessed from the oxygen content, with the most

2

biodegraded oils displaying higher abundances for the O2-class and consequently lower

3

abundances for the O-class due the O-to-O2 oxygenation that occurs during organic matter

4

deposition.17, 42, 43 Again, the CEOL1 and CEOL4 oils were, using the O-to-O2 ratio, more

5

biodegraded than the CEOL2 and CEOL3 samples. Higher abundances of the N-class

6

(carbazoles and derivatives) were also observed by ESI(-) FT-ICR MS (Figure 7) for CEOL2

7

and CEOL3 oils, in accordance with higher abundances of the O2-class. The CEOL3 is the

8

most emulsified oil, as showed previously by Santos and co-workers29, with seems therefore

9

to indicate that higher N-class abundances results in higher tendency to form stable

10

emulsions. The trends in biodegradation and thermal maturity suggested by the ESI(±) FT-

11

ICR MS have therefore indeed complemented the geochemical predictions based on GC/MS

12

classical biomarkers.

13

Figure 7 (C) shows plots of DBE versus relative abundance distributions for the O2-

14

class. Note that for all four crude oil samples, O2-class is formed mainly by fatty acids (DBE

15

= 1), naphthenic acids with 1-6 rings (DBE = 2-7) and higher aromatic compounds (DBE >

16

7). The four samples also showed highest O2-class abundances in the lower DBE range, with

17

maximum abundances for DBE = 5 (a carboxylic acid with four rings) for CEOL1, CEOL3

18

and CEOL4 in DBE = 1 for CEOL2 (aliphatic carboxylic acid). Note also that we have found

19

the higher abundances for naphthenic acids with DBE = 2-7.

20

Biodegradation levels were also evaluated via van Krevelen diagrams constructed as a

21

function of carbon number (Cn) for the O2-class (Figure 8), where specific regions for fatty

22

acids (H/C > 2.0), naphthenic acids (H/C from 1.7 to 2.0) and aromatics (H/C < 1.7) can be

23

clearly delineated. Naphthenic acids are predominant for all oils, but such constituents are of

24

highest abundance for CEOL4 as demonstrated by the heat map.

25 26

CONCLUSIONS

27

A comprehensive geochemical characterization of onshore crude oils blends from

28

Sergipe-Alagoas sedimentary basin has been performed using both the classical approach of

29

GC/MS comparison of biomarker profiles or ratios such as those represented by the n-

30

alkanes, isoprenoids, terpanes and steranes, as well as geochemical predictions based on

31

profiles of polar compounds as revealed by ESI(±) FT-ICR MS.


Page 10 of 21

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Energy & Fuels

1

The combination of the GC/MS and FT-ICR MS data suggested that the four crude oil

2

blends investigated were comprised of paraffinic and lacustrine crude oils with intermediate

3

levels of thermal evolution and biodegradation The geochemical similarity was quite high for

4

the CEOL1 and CEOL4 samples, which are samples with higher thermal maturity levels and

5

more biodegraded than the CEOL2 and CEOL3 crude oil blend, and the data has also showed

6

valuable insight to the field of organic geochemistry. For GC/MS and FT-ICR MS data

7

herein presented should provide key information to understand the behavior of the crude oils

8

from Sergipe-Alagoas sedimentary basin, and may generate important information for the

9

upstream and midstream processes in the Brazilian oil industry.

10 11

AUTHOR INFORMATION

12

Corresponding Author

13

*E-mail: [email protected]. Tel.: +55 79 2105 6654.

14

Notes

15

The authors declare no competing financial interest.

16 17

ACKNOWLEDGMENTS

18

We would like to thank the Fundação de Amparo à Pesquisa do Estado de São Paulo

19

(FAPESP) for the scholarship awarded to J.M.S. (process number 2013/19161-4),

20

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and Conselho

21

Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (Brazilian research councils)

22

for fellowships. The authors are grateful to PETROBRAS/UOSEAL/ENGP/LABF for the

23

financial support through the cooperation term – 0050.0078683.12.9.

24 25 26 27

REFERENCES


Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51

1. Aguiar, A.; Silva Jr, A. I.; Azevedo, D. A.; Aquino Neto, F. R., Application of comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry to biomarker characterization in Brazilian oils. Fuel 2010, 89, (10), 2760-2768. 2. Speight, J. G., The chemistry and technology of petroleum. 4th ed.; CRC Press: New York, 2006; p 1-980. 3. Peters, K. E.; Fowler, M. G., Applications of petroleum geochemistry to exploration and reservoir management. Organic Geochemistry 2002, 33, (1), 5-36. 4. Heckmann, J. R.; Landau, L.; Gonçalves, F. T. T.; Pereira, R.; De Almeida Azevedo, D., Geochemical evaluation of brazilian oils with emphasis on aromatic hydrocarbons. Quimica Nova 2011, 34, (8), 1328-1333. 5. Love, G. D.; Snape, C. E.; Carr, A. D.; Houghton, R. C., Release of covalently-bound alkane biomarkers in high yields from kerogen via catalytic hydropyrolysis. Organic Geochemistry 1995, 23, (10), 981-986. 6. Damas, E. Y. C.; Medina, M. O. C.; Clemente, A. C. N.; Díaz, M. Á. D.; Bravo, L. G.; Ramada, R. M.; De Oca Porto, R. M., Validation of an analytical methodology for the quantitative analysis of petroleum hydrocarbons in marine sediment samples. Quimica Nova 2009, 32, (4), 855-860. 7. Almeida Azevedo, D. D.; Da Silva, T. F.; Da Silva, D. B., Geochemical evaluation of occluded biomarkers in asphaltenic structures. Quimica Nova 2009, 32, (7), 1770-1776+S1-S3. 8. Eiserbeck, C.; Nelson, R. K.; Grice, K.; Curiale, J.; Reddy, C. M., Comparison of GC-MS, GCMRM-MS, and GC×GC to characterise higher plant biomarkers in Tertiary oils and rock extracts. Geochimica et Cosmochimica Acta 2012, 87, 299-322. 9. Nielsen, N. J.; Ballabio, D.; Tomasi, G.; Todeschini, R.; Christensen, J. H., Chemometric analysis of gas chromatography with flame ionisation detection chromatograms: A novel method for classification of petroleum products. Journal of Chromatography A 2012, 1238, 121-127. 10. Asif, M.; Fazeelat, T.; Grice, K., Petroleum geochemistry of the Potwar Basin, Pakistan: 1. Oiloil correlation using biomarkers, δ 13C and δD. Organic Geochemistry 2011, 42, (10), 1226-1240. 11. Escobar, M.; Márquez, G.; Azuaje, V.; Da Silva, A.; Tocco, R., Use of biomarkers, porphyrins, and trace elements to assess the origin, maturity, biodegradation, and migration of Alturitas oils in Venezuela. Fuel 2012, 97, 186-196. 12. Duan, Y., Geochemical characteristics of crude oil in fluvial deposits from maling oilfield of ordos basin, China. Organic Geochemistry 2012, 52, 35-43. 13. Ventura, G. T.; Simoneit, B. R. T.; Nelson, R. K.; Reddy, C. M., The composition, origin and fate of complex mixtures in the maltene fractions of hydrothermal petroleum assessed by comprehensive two-dimensional gas chromatography. Organic Geochemistry 2012, 45, 48-65. 14. Marshall, A. G.; Rodgers, R. P., Petroleomics: The Next Grand Challenge for Chemical Analysis. Accounts of Chemical Research 2004, 37, (1), 53-59. 15. Rodgers, R. P.; Schaub, T. M.; Marshall, A. G., PETROLEOMICS: MS returns to its roots. Analytical Chemistry 2005, 77, (1), 20 A-27 A. 16. Ávila, B. M. F.; Pereira, R.; Gomes, A. O.; Azevedo, D. A., Chemical characterization of aromatic compounds in extra heavy gas oil by comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry. Journal of Chromatography A 2011, 1218, (21), 32083216. 17. Vaz, B. G.; Silva, R. C.; Klitzke, C. F.; Simas, R. C.; Lopes Nascimento, H. D.; Pereira, R. C. L.; Garcia, D. F.; Eberlin, M. N.; Azevedo, D. A., Assessing biodegradation in the llanos orientales crude oils by electrospray ionization ultrahigh resolution and accuracy fourier transform mass spectrometry and chemometric analysis. Energy and Fuels 2013, 27, (3), 1277-1284. 18. Corilo, Y. E.; Vaz, B. G.; Simas, R. C.; Lopes Nascimento, H. D.; Klitzke, C. F.; Pereira, R. C. L.; Bastos, W. L.; Santos Neto, E. V.; Rodgers, R. P.; Eberlin, M. N., Petroleomics by EASI(±) FT-ICR MS. Analytical Chemistry 2010, 82, (10), 3990-3996. 19. Li, M.; Cheng, D.; Pan, X.; Dou, L.; Hou, D.; Shi, Q.; Wen, Z.; Tang, Y.; Achal, S.; Milovic, M.; Tremblay, L., Characterization of petroleum acids using combined FT-IR, FT-ICR-MS and GC-MS:


Page 12 of 21

Page 13 of 21

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

Energy & Fuels

Implications for the origin of high acidity oils in the Muglad Basin, Sudan. Organic Geochemistry 2010, 41, (9), 959-965. 20. Tong, J.; Liu, J.; Han, X.; Wang, S.; Jiang, X., Characterization of nitrogen-containing species in Huadian shale oil by electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry. Fuel 2013, 104, 365-371. 21. Liao, Y.; Shi, Q.; Hsu, C. S.; Pan, Y.; Zhang, Y., Distribution of acids and nitrogen-containing compounds in biodegraded oils of the Liaohe Basin by negative ion ESI FT-ICR MS. Organic Geochemistry 2012, 47, 51-65. 22. Colati, K. A. P.; Dalmaschio, G. P.; De Castro, E. V. R.; Gomes, A. O.; Vaz, B. G.; Romão, W., Monitoring the liquid/liquid extraction of naphthenic acids in brazilian crude oil using electrospray ionization FT-ICR mass spectrometry (ESI FT-ICR MS). Fuel 2013, 108, 647-655. 23. Dias, H. P.; Pereira, T. M. C.; Vanini, G.; Dixini, P. V.; Celante, V. G.; Castro, E. V. R.; Vaz, B. G.; Fleming, F. P.; Gomes, A. O.; Aquije, G. M. F. V.; Romão, W., Monitoring the degradation and the corrosion of naphthenic acids by electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry and atomic force microscopy. Fuel 2014, 126, 85-95. 24. National Agency of Petroleum, N. G. a. B., Exploration and Production Database – BDEP. In 2014. 25. Rodrigues, D. C.; Koike, L.; Reis, F. D. A. M.; Alves, H. P.; Chang, H. K.; Trindade, L. A.; Marsaioli, A. J., Carboxylic acids of marine evaporitic oils from Sergipe-Alagoas Basin, Brazil. Organic Geochemistry 2000, 31, (11), 1209-1222. 26. Rodrigues, D. C.; De Vasconcellos, S. P.; Alves, P. B.; Nascimento, L. R.; De Abreu Filho, B. A.; De Oliveira, V. M.; Manfio, G. P.; Dos Santos Neto, E. V.; Marsaioli, A. J., Relationship between cyclohexyl-alkanoic acids and the acidothermophilic bacterium Alicyclobacillus spp.: Evidence from Brazilian oils. Organic Geochemistry 2005, 36, (10), 1443-1453. 27. Rocha, T. A. S., L. A. S.; Lima, S. G.; Filho, M. G. R.; Batista, N. C.; Lopes, J. A. D.; Neto, J. M. M.; Reis, F. A. M., Caracterização de biomarcadores neutros em óleos da bacia petrolífera SergipeAlagoas. In XLVII Congresso Brasileiro de Química, Rio Grande do Norte, Brasil, 2007. 28. Sousa Júnior, G. R.; Santos, A. L. S.; De Lima, S. G.; Lopes, J. A. D.; Reis, F. A. M.; Santos Neto, E. V.; Chang, H. K., Evidence for euphotic zone anoxia during the deposition of Aptian source rocks based on aryl isoprenoids in petroleum, Sergipe-Alagoas Basin, northeastern Brazil. Organic Geochemistry 2013, 63, 94-104. 29. Santos, J. M.; Santos, F. M. L.; Araujo, R. G. O.; Lessa, A. C.; Souza, J. P. D.; Santos, M. J. D.; Wisniewski, A., Chemical aspects of onshore crude oils from the Carmópolis Field, Sergipe-Alagoas Basin, Brazil: A case study on the industrial process for water-oil separation. Energy and Fuels 2015, 29, (3), 1315-1322. 30. March, R. E., An introduction to quadrupole ion trap mass spectrometry. Journal of Mass Spectrometry 1997, 32, (4), 351-369. 31. Peters, K. E. W., C.C.; Moldowan, J.M., The biomarker guide: Biomarkers and isotopes in the environment and human history. Cambridge University 2005; Vol. 2ed. 32. Mello, M. R.; Gaglianone, P. C.; Brassell, S. C.; Maxwell, J. R., Geochemical and biological marker assessment of depositional environments using Brazilian offshore oils. Marine and Petroleum Geology 1988, 5, (3), 205-223. 33. Didyk, B. M.; Simoneit, B. R. T.; Brassell, S. C.; Eglinton, G., Organic geochemical indicators of palaeoenvironmental conditions of sedimentation. Nature 1978, 272, (5650), 216-222. 34. Lima, D. I. L., S.F.; Silva, V.V.; Sant´Ana, A.E.G.; Rebouças L.M.C. , Biomarcadores saturados em óleos da bacia Sergipe-Alagoas, Brasil: Distribuição e concentração. Geochimica Brasiliensis 2007, 21, (1), 99-110. 35. Scholz-Böttcher, B. M.; Ahlf, S.; Vázquez-Gutiérrez, F.; Rullkötter, J., Natural vs. anthropogenic sources of hydrocarbons as revealed through biomarker analysis: A case study in the southern Gulf of Mexico. Boletin de la Sociedad Geologica Mexicana 2009, 61, (1), 47-56.


Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

36. Hwang, R. J., Biomarker Analysis Using GC-MSD. Journal of Chromatographic Science 1990, 28, (3), 109-113. 37. Oudot, J.; Merlin, F. X.; Pinvidic, P., Weathering rates of oil components in a bioremediation experiment in estuarine sediments. Marine Environmental Research 1998, 45, (2), 113-125. 38. Hunt, J. M., Petroleum geochemistry and geology ( textbook). Petroleum geochemistry and geology ( textbook). 1996. 39. Oliveira, C. R.; Ferreira, A. A.; Oliveira, C. J. F.; Azevedo, D. A.; Santos Neto, E. V.; Aquino Neto, F. R., Biomarkers in crude oil revealed by comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry: Depositional paleoenvironment proxies. Organic Geochemistry 2012, 46, 154-164. 40. Hughey, C. A.; Rodgers, R. P.; Marshall, A. G.; Walters, C. C.; Qian, K.; Mankiewicz, P., Acidic and neutral polar NSO compounds in Smackover oils of different thermal maturity revealed by electrospray high field Fourier transform ion cyclotron resonance mass spectrometry. Organic Geochemistry 2004, 35, (7), 863-880. 41. Pereira, T. M. C.; Vanini, G.; Oliveira, E. C. S.; Cardoso, F. M. R.; Fleming, F. P.; Neto, A. C.; Lacerda Jr, V.; Castro, E. V. R.; Vaz, B. G.; Romão, W., An evaluation of the aromaticity of asphaltenes using atmospheric pressure photoionization Fourier transform ion cyclotron resonance mass spectrometry - APPI(±)FT-ICR MS. Fuel 2014, 118, 348-357. 42. Pan, Y.; Liao, Y.; Shi, Q.; Hsu, C. S., Acidic and neutral polar NSO compounds in heavily biodegraded oils characterized by negative-ion ESI FT-ICR MS. Energy and Fuels 2013, 27, (6), 29602973. 43. Mapolelo, M. M.; Rodgers, R. P.; Blakney, G. T.; Yen, A. T.; Asomaning, S.; Marshall, A. G., Characterization of naphthenic acids in crude oils and naphthenates by electrospray ionization FTICR mass spectrometry. International Journal of Mass Spectrometry 2011, 300, (2-3), 149-157.

25 26 27 28 29 30 31 32 33 34 35 36 37

List of Tables and Figures

38


Page 14 of 21

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Energy & Fuels

1

Table 1. CPI and Pr/Ph, Pr/n-C17, Ph/n-C18 ratios of the four crude oils blends obtained by relative areas in

2

TICC for the n-alkanes and isoprenoid using GC/MS Sample

CPI*

Pr/Ph

Pr/n-C17

Ph/n-C18

CEOL1

1.13

0.70

0.61

1.19

CEOL2

1.15

0.78

0.67

1.10

CEOL3

1.15

0.82

0.72

1.19

CEOL4

1.15

0.71

0.59

1.16

3 4

Table 2. Geochemical parameters (based on the peak heights from GC/MS data in SIM mode) from the class of

5

terpanes and steranes biomarkers for the crude oils blends of the Oiteirinhos II Sation, Carmópolis-SE Sample Parameters Ts/(Ts+Tm)a C29Ts/(H29+C29Ts)b Ts/H30c S/(S+R) αααC29d αββ (S+R)/αββ + ααα (S+R) C29e Dia 27(S+R)/ααα C27 (S+R)f %C27g %C28h %C29i

6 7 8 9 10 11 12 13 14 15 16 17 18 19

CEOL1

CEOL2

CEOL3

CEOL4

0.45 0.27 0.56 0.70 0.73 1.34 29.49 20.10 50.41

0.44 0.28 0.58 0.40 0.63 1.97 29.35 20.42 50.23

0.45 0.27 0.56 0.69 0.74 1.51 26.99 22.10 50.91

0.44 0.28 0.58 0.85 0.76 1.49 29.59 20.07 50.34

a

Ts = C27 18α(H)-22,29,30-Trisnorneohopane; Tm = C27 17α(H)-22,29,30-Trisnorhopane

b

C29Ts = 18α(H)-30-norneohopane C29; H29 = 17α,21β(H)-30-norhopane

c

Ts = C27 18α(H)-22,29,30-Trisnorneohopane; H30 = 17α, 21β(H)-hopane

d

αααC29(S) = 5α,14α,17α (H) - Stigmastane 20S; αααC29(R) = 5α,14α,17α (H) - Stigmastane 20R

e

αββC29 (S+R) = 5α,14β,17α (H) - Stigmastane 20S + 5α,14β,17α - Stigmastane 20R; αααC29(S+R) = 5α,14α,17α -

Stigmastane 20S + 5α,14α,17α - Stigmastane 20R f

Dia 27(S+R) = 13β,17α – Diacholestane 20S + 13β,17α – Diacholestane 20R; αααC27 (S+R) = 5α,14α,17α - Cholestane

20S + 5α,14α,17α - Cholestane 20R g

[C27 5α,14α,17α - Cholestane 20S + C27 5α,14α,17α - Cholestane 20R / C28 5α,14β,17β - Ergostane 20R + C28

5α,14β,17β - Ergostane 20S + C29 5α,14β,17α - Stigmastane 20R + C29 5α,14β,17α - Stigmastane 20S] x 100 h

[C28 5α,14β,17β - Ergostane 20R + C28 5α,14β,17β - Ergostane 20S / C28 5α,14β,17β - Ergostane 20R + C28 5α,14β,17β

- Ergostane 20S + C29 5α,14β,17α - Stigmastane 20R + C29 5α,14β,17α - Stigmastane 20S] x 100 i

[C29 5α,14β,17α - Stigmastane 20R + C29 5α,14β,17α - Stigmastane 20S / C28 5α,14β,17β - Ergostane 20R + C28

5α,14β,17β - Ergostane 20S + C29 5α,14β,17α - Stigmastane 20R + C29 5α,14β,17α - Stigmastane 20S] x 100.

20


Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1 2

Figure 1. Total ion current chromatogram (TICC) of four crude oils obtained in the SCAN mode, showing the

3

distribution in terms of n-alkanes (n-C10-35) and specific isoprenoids (a: 2,6-dimethylundecane; b: 2,6,10-

4

trimethylundecane; c: 2,6,10-trimethyldodecane; d: 2,6,10-trimethyltridecane; e: 2,6,10-trimethylpentadecane;

5

Pr: pristane and Ph: phytane).


Page 16 of 21

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Energy & Fuels

1 2

3 4

Figure 2. (A) Pr/n-C17 versus Ph/n-C18 and (B) Pr/n-C17 versus °API as biodegradation indicative for crude oils

5

blends studied.

6 (x100,000) 1.75 191.00 (1.00)

CEOL 1

1.1

(x100,000) 191.00 (1.00)

CEOL 3

1.0 1.50

0.9 0.8

1.25

0.7 1.00

0.6

10

0.75

1 0.50

5 2

3

4

6

10

0.5

5

9

7

0.4

8

11 12

1

3

2

4

9

7 6

8

1112

0.3

13 14

15

13 14

15 0.2

0.25

0.1

(x100,000) 191.00 (1.00)

36.0

37.0

38.0

39.0

40.0

41.0

42.0

43.0

44.0

45.0

46.0

47.0

CEOL 2

1.3 1.2

(x100,000) 191.00 (1.00)

36.0

37.0

38.0

39.0

40.0

41.0

42.0

43.0

44.0

45.0

46.0

47.0

CEOL 4

1.4 1.3 1.2

1.1

1.1

1.0

1.0

0.9

0.9 0.8 0.8 0.7

10

0.6

10

0.7 0.6

0.5

1

0.4

5 3

2

4

9

7

6

5

0.5

11 12

8

0.3

1

0.4

15

13 14

3

2

4

9

7 6

8

1112

15

13 14

0.3

0.2

0.2

0.1

0.1 36.0

37.0

38.0

39.0

40.0

41.0

42.0

43.0

44.0

45.0

46.0

47.0

36.0

37.0

38.0

39.0

40.0

41.0

42.0

43.0

44.0

45.0

46.0

47.0

7 8

Figure 3. GC/MS in SIM mode for the ions of m/z 191 (terpanes biomarkers) for the crude oils blends of the

9

Oiteirinhos II Sation, Carmópolis-SE. 1: 18α(H)-22,29,30-Tris-nor-neo-hopano; 2: 17α(H)-22,29,30-Tris-nor-

10

hopano; 3: 17α,21β(H)-30-nor-hopano; 4: 18α(H)-30-nor-neo-hopano; 5: 17α, 21β(H)-hopano; 6: 17β, 21α (H)-

11

hopano; 7: 17α, 21β(H)-29-homo-hopano; 8: 17α, 21β(H)-29-homo-hopano; 9: 17α, 21β(H)-29-bis-homo-

12

hopano; 10: 17α, 21β(H)-29-bis-homo-hopano; 11: 17α, 21β(H)-29-tris-homo-hopano; 12: 17α, 21β(H)-29-tris-

13

homo-hopano; 13: 17α, 21β(H)-29-tetrakis-homo-hopano; 14: 17α, 21β(H)-29-tetrakis-homo-hopano; 15: 17α,

14

21β(H)-29-pentakis-homo-hopano.


Energy & Fuels

1.4

(x100,000) 217.00 (1.36)

(x10,000) 217.00 (1.57)

CEOL 1

1.3

8.0

6

1.1

6

1

1

1.0

CEOL 3

9.0

1.2

7.0

0.9

6.0

0.8

2

7

0.6 0.5

3 4

0.4

14

9

5

7 12

4.0

10 11

8

2

5.0

12

0.7

10 8

3 4

3.0

13

11

14

9

5

0.3

13

2.0

0.2 1.0 0.1 34.50

34.75

35.00

35.25

35.50

35.75

36.00

36.25

36.50

36.75

37.00

37.25

37.50

37.75

38.00

38.25

38.50

38.75

39.00

39.25

(x100,000) 1.1 217.00 (1.53)

39.50

39.75

40.00 34.50

34.75

35.00

35.25

35.50

35.75

36.00

36.25

36.50

36.75

37.00

37.25

37.50

37.75

38.00

38.25

38.50

38.75

39.00

39.25

39.50

39.75

40.00

(x100,000) 217.00 (1.69)

CEOL 2

1.0

CEOL 4

1.3 1.2

0.9

6

1

6

1

1.1 1.0

0.8

0.9 0.7

2

12 0.5

3 4

11

8

12 10

0.5

14

9

5

0.3

7

0.7 0.6

10

0.4

2

0.8

7

0.6

3 4

0.4

14

8 13

9

5

13

11

0.3 0.2 0.2 0.1

0.1 34.75

35.00

35.25

35.50

35.75

36.00

36.25

36.50

36.75

37.00

37.25

37.50

37.75

38.00

38.25

38.50

38.75

39.00

39.25

39.50

39.75

40.00 34.50

34.75

35.00

35.25

35.50

35.75

36.00

36.25

36.50

36.75

37.00

37.25

37.50

37.75

38.00

38.25

38.50

38.75

39.00

39.25

39.50

39.75

1 2

Figure 4. GC/MS in SIM mode for the ion of m/z 217 (steranes biomarkers) for the crude oils blends of the

3

Oiteirinhos II Sation, Carmópolis-SE. 1: 13β, 17α – Diacolestano 20S; 2: 13β, 17α – Diacolestano 20R; 3: 5α,

4

14α, 17α - Colestano 20S; 4: 5α, 14β, 17β - Colestano 20R; 5: 5α, 14β, 17β - Colestano 20S; 6: 5α, 14α, 17α -

5

Colestano 20R; 7: 5α, 14α, 17α - Ergostano 20S; 8: 5α, 14β, 17β - Ergostano 20R; 9: 5α, 14β, 17β - Ergostano

6

20S; 10: 5α, 14α, 17α - Ergostano 20R; 11: 5α, 14α, 17α - Estigmastano 20S; 12: 5α, 14β, 17α - Estigmastano

7

20R; 13: 5α, 14β, 17α - Estigmastano 20S; 14: 5α, 14α, 17α - Estigmastano 20R. 2.5 2.0

1.0

(A)

(B) %C28

CEOL3

1.5

Dia 27(S+R)/aaa C27 (S+R)

CEOL2 %C29

0.5 Factor 2 : 39.91%

1.0 PC2: 39.91%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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0.5 0.0 -0.5

0.0

CEOL1

-1.0

aßß-0.5 (S+R)/aßß + aaa (S+R) C29 S/(S+R) aaaC29

CEOL4

-1.5

%C27

-2.0 -1.0 -2.5 -4

-3

-2

-1

0

1

2

3

4

-1.0

-0.5

PC1: 57.14%

0.0 PC1: 57.14%

(C) CEOL1

CEOL4

CEOL2

CEOL3

0

8

20

40

60

80

(Dlink/Dmax)*100


100

120

0.5

1.0

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Energy & Fuels

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Figure 5. (A) PCA score plot to PC1 versus PC2 for the crude oil blends (A) and (B) geochemical correlation

2

parameters (B); (C) Dendrogram of HCA analysis using the Euclidean distance for Ward's method.

3 4

Figure 6. (A) ESI(+)-FT-ICR mass spectra, (B) classes distributions and (C) DBE distributions for the N-class.


Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1 2

Figure 7. (A) ESI(-)-FT-ICR mass spectra, (B) classes distributions and (C) DBE distributions for the O2-class.

3


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Energy & Fuels

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Figure 8. Van Krevelen ESI(-)-FT-ICR MS diagrams of H/C versus carbon number to class-O2 and the regions

3

for fatty acids, naphtenic acids and aromatics compounds for the crude oils blends.

4


Advanced Aspects of Crude Oils Correlating Data of Classical

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