Dibenzothiophenes, phenyldibenzothiophenes, and benzo[b]naphthothiophenes in crude oils and source rock extracts from the Niger Delta Basin, Nigeria, and their applications as source facies, depositional environments, and thermal maturity indicators

Ogbesejana, Abiodun B.; Bello, Oluwasesan M.; Akintade, Oluwadurotimi O.; Okunola, Oluwole Joshua; Osamudiamen, Paul M.; Kabo, Kamaluddeen S.; Ali, Tijjani; Uduma, Uduma A.

doi:10.1007/s12517-021-06872-3

Cited by 5 publications

(3 citation statements)

References 53 publications

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“…The abundance of benzo[ b ]naphthothiophenes is characterized by the predominance of benzo[ b ]naphtho[2,1]thiophene. This observation is consistent with previous reports on crude oil samples and has been attributed to significant microbial contribution on the source rock that expelled the oils . All crude oils show three isomers of phenyl-dibenzothiophenes (Ph-DBT), which have been unambiguously identified using co-injection of synthesized standards and retention indices .…”

Section: Resultssupporting

confidence: 91%

“…The DBT/Phe values for the oil samples are within the range of 0.006–0.023 (Table S4 of the Supporting Information), suggesting crude oils with shale lithology. The (1+4)-/(2+3)-MDBT values in the oil samples vary between 0.476 and 1.000, also suggesting shale lithology . The abundance of benzo[ b ]naphthothiophenes is characterized by the predominance of benzo[ b ]naphtho[2,1]thiophene.…”

Section: Resultsmentioning

confidence: 95%

“…The relative abundances of particular PAHs can be used for the evaluation of thermal maturity, oil rock lithology, and microbial contribution indicators across crude oils. − Traditionally, alkyl homologues of phenanthrene (Phe), dibenzothiophene (DBT), phenyl dibenzothiophenes (PhDBT), and benzo[ b ]naphthothiophenes (BNT) are used; − a typical caveat during traditional GC–MS analysis is the number of potential interferences. To this end, complementary GC–TIMS–HRMS has the added advantage in achieving accurate estimations by filtering interferences coming from fragments and isobars (see the MetaboScape raw worksheet of the Supporting Information).…”

Section: Resultsmentioning

confidence: 99%

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Characterization of Deasphalted Crude Oils Using Gas Chromatography–Atmospheric Pressure Laser Ionization–Trapped Ion Mobility Spectrometry–Time-of-Flight Mass Spectrometry

2021

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In the present work, a novel workflow based on complementary gas-phase separations is applied to the characterization of deasphalted light (Macondo and Calvert), medium (Duri), and heavy (San Ardo) crude oils. The coupling of gas chromatography (GC), atmospheric pressure laser ionization (APLI), and trapped ion mobility spectrometry−mass spectrometry (TIMS−MS) resulted in the effective separation and candidate assignment of polycyclic aromatic hydrocarbons (PAHs) and similar compounds. The analytical power of GC− APLI−TIMS−TOF MS is based on the separation of the isomeric content using the GC and TIMS (R = 50−90 with S r = 0.18 V/ms) gas-phase separations, followed by the high mass resolution and mass accuracy (<2 ppm) of the TOF MS analyzer. Previously reported PAH-like known compounds (130 compounds) uniquely assigned on the basis of their retention time (RT), collisional cross section (CCS), and mass-to-charge ratio (m/z) showed signature patterns and distinctive diagnostic ratios representative of the thermal maturity, lithology, and microbial contribution to each oil formation. The unsupervised T-Rex 4D analysis of GC−APLI−TIMS−TOF MS generated for the first time an exhaustive list of PAHs and similar unknown components (∼8500), with each component characterized by a RT, CCS, m/z value, and chemical formula and peak areas for each replica analysis (i.e., 12 total, 3× per crude oil). The inspection of the PAHs and similar unknown compounds provided a list of unique identifiers for each crude oil (2−4% of the assigned compounds) as well as molecular components common to all crude oils (∼50% of the assigned compounds). The analytical power of GC−APLI−TIMS−TOF MS is illustrated using unsupervised principal component analysis (PCA), where the four oils can be easily separated in two principal components that account for 70% of the total variance.

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Section: Resultssupporting

confidence: 91%

Section: Resultsmentioning

confidence: 95%