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Catalytic hydropyrolysis (HyPy) is a powerful analytical technique for fragmenting macromolecular organic matter, such as kerogen (insoluble sedimentary organic matter), and releasing covalently-bound molecular constituents including branched and cyclic biomarker hydrocarbons. Here we illustrate our molecular approach to paleobiology with lipid biomarker data collected from rock bitumens and kerogens hosted within sedimentary units of the Neoproterozoic Huqf Supergroup, South Oman Salt Basin, Sultanate of Oman. We emphasize that parallel analyses of free and bound biomarker pools affords more confidence that we have correctly identified syngenetic compounds. One enigmatic class of compounds that is prominent in many late Proterozoic and Cambrian sedimentary rocks and oils, including from the Huqf Supergroup, is a series of C14-C30 mid-chain methylalkanes which were originally denoted X-peaks. Despite their abundance in the Precambrian rock record, little is known about the organisms responsible for their biosynthesis. Here we propose a possible origin of X-peak methylalkanes from colorless sulfur bacteria (a very heterogeneous group of chemolithotrophic γ-proteobacteria). In modern marine settings, these bacteria are abundant mat formers wherever a sedimentary sulfide-rich horizon intersects the seafloor producing a steep geochemical redox gradient. This condition may have been met more commonly on shallow marine shelves in late Neoproterozoic basins and these benthic mats may have acted as environmental buffers consuming hydrogen sulfide. If our hypothesis is correct, proliferation of sulfide-oxidizing benthic microbial mats, commencing in the late Cryogenian in South Oman Salt Basin, implies unique and specific benthic conditions during the evolution of the earliest metazoans.

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[1] From 5 June to 15 September 2010, a multidisciplinary marine survey was undertaken onboard the M/V Ryan Chouest in the region of the BP Deepwater Horizon incident site in the Gulf of Mexico. The primary objective of the survey was the continuous monitoring of hydrocarbon abundance from sea surface down to a maximum depth of 120 m. Compound abundances were inferred using a hydrocarbon sensor array with associated vertical cast system. In order to better understand the potential inputs from natural seepage in the vicinity of the spill, a Simrad EK60 high-resolution split beam echo sounder, operated at 38 kHz, was included in the survey between 7 July and 15 September 2010. During this period, three fields of natural seeps characterized by hydroacoustic flares were studied in detail. These seep fields are at water depths of approximately 430 m, 880 m, and 1370 m. They are associated with extensive cold seep systems. In particular, the area around Seep Field 1 (the vicinity of Deepwater Horizon) seems to present a vast area of active natural seepages in the Gulf of Mexico. The repeat surveys at two of the fields suggested that the cold seep systems here were active, with expulsions of hydrocarbons into the water column, at least during the periods of our acoustic surveys. Multiple lines of evidence gathered during the survey indicated that the observed hydroacoustic flares at the three fields identified consisted of oily bubble streams of gases of thermogenic origin. However, direct observation and sampling are required to reveal the precise nature of the flares. In the deep water Gulf of Mexico, the formation of a hydrate rim around bubbles seems to be a very important mechanism for the long transport of methane and oil in the water column.

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This paper presents a system and method developed to identify a source oil's characteristic properties by testing the oil's dissolved components in water. Through close examination of the oil dissolution process in water, we hypothesise that when oil is in contact with water, the resulting oil-water extract, a complex hydrocarbon mixture, carries the signature property information of the parent oil. If the dominating differences in compositions between such extracts of different oils can be identified, this information could guide the selection of various sensors, capable of capturing such chemical variations. When used as an array, such a sensor system can be used to determine parent oil information from the oil-water extract. To test this hypothesis, 22 oils' water extracts were prepared and selected dominant hydrocarbons analyzed with Gas Chromatography-Mass Spectrometry (GC-MS); the subsequent Principal Component Analysis (PCA) indicates that the major difference between the extract solutions is the relative concentration between the volatile mono-aromatics and fluorescent polyaromatics. An integrated sensor array system that is composed of 3 volatile hydrocarbon sensors and 2 polyaromatic hydrocarbon sensors was built accordingly to capture the major and subtle differences of these extracts. It was tested by exposure to a total of 110 water extract solutions diluted from the 22 extracts. The sensor response data collected from the testing were processed with two multivariate analysis tools to reveal information retained in the response patterns of the arrayed sensors: by conducting PCA, we were able to demonstrate the ability to qualitatively identify and distinguish different oil samples from their sensor array response patterns. When a supervised PCA, Linear Discriminate Analysis (LDA), was applied, even quantitative classification can be achieved: the multivariate model generated from the LDA achieved 89.7% of successful classification of the type of the oil samples. By grouping the samples based on the level of viscosity and density we were able to reveal the correlation between the oil extracts' sensor array responses and their original oils' feature properties. The equipment and method developed in this study have promising potential to be readily applied in field studies and marine surveys for oil exploration or oil spill monitoring.