Assessment of Geogenic Contaminants in Water Co-Produced with Coal Seam Gas Extraction in Queensland, Australia : Implications for Human Health Risk

Organic compounds in Australian coal seam gas produced water (CSG water) are poorly understood despite their environmental contamination potential. In this study, the presence of some organic substances is identified from government-held CSG water-quality data from the Bowen and Surat Basins, Queensland. These records revealed the presence of polycyclic aromatic hydrocarbons (PAHs) in 27% of samples of CSG water from the Walloon Coal Measures at concentrations <1 µg/L, and it is likely these compounds leached from in situ coals. PAHs identified from wells include naphthalene, phenanthrene, chrysene and dibenz[a,h]anthracene. In addition, the likelihood of coal-derived organic compounds leaching to groundwater is assessed by undertaking toxicity leaching experiments using coal rank and water chemistry as variables. These tests suggest higher molecular weight PAHs (including benzo[a]pyrene) leach from higher rank coals, whereas lower molecular weight PAHs leach at greater concentrations from lower rank coal. Some of the identified organic compounds have carcinogenic or health risk potential, but they are unlikely to be acutely toxic at the observed concentrations which are almost negligible (largely due to the hydrophobicity of such compounds). Hence, this study will be useful to practitioners assessing CSG water related environmental and health risk.

Engineering thin film semiconductor gas sensors to increase sensitivity and decrease operation temperature

Thin film nanostructured gas sensors typically operate at temperatures above 400°C, but lower temperature operation is highly desirable, especially for remote area field sensing as this reduces significantly power consumption. We have investigated a range of sensor materials based on both pure and doped tungsten oxide (mainly focusing on Fe-doping), deposited using both thermal evaporation and electron-beam evaporation, and using a variety of post-deposition annealing. The films show excellent sensitivity at operating temperatures as low as 150°C for detection of NO2. There is a definite relationship between the sensitivity and the crystallinity and nanostructure obtained through the deposition and heat treatment processes, as well as variations in the conductivity caused both by doping and heat treatmetn. The ultimate goal of this work is to control the sensing properties, including selectivity to specific gases through the engineering of the electronic properties and the nanostructure of the films.

Greenhouse gas emissions from sub-tropical agricultural soils after addition of organic by-products

As the cost of mineral fertilisers increases globally, organic soil amendments (OAs) from agricultural sources are increasingly being used as substitutes for nitrogen. However, the impact of OAs on the production of greenhouse gases (CO2 and N2O) is not well understood. A 60-day laboratory incubation experiment was conducted to investigate the impacts of applying OAs (equivalent to 296 kg N ha−1 on average) on N2O and CO2 emissions and soil properties of clay and sandy loam soils from sugar cane production. The experiment included 6 treatments, one being an un-amended (UN) control with addition of five OAs being raw mill mud (MM), composted mill mud (CM), high N compost (HC), rice husk biochar (RB), and raw mill mud plus rice husk biochar (MB). These OAs were incubated at 60, 75 and 90% water-filled pore space (WFPS) at 25°C with urea (equivalent to 200 kg N ha−1) added to the soils thirty days after the incubation commenced. Results showed WFPS did not influence CO2 emissions over the 60 days but the magnitude of emissions as a proportion of C applied was RB < CM < MB < HC

How innovation and R&D happen in the upstream oil & gas industry : insights from a global survey

Few would disagree that the upstream oil & gas industry has become more technology-intensive over the years. But how does innovation happen in the industry? Specifically, what ideas and inputs flow from which parts of the sector׳s value network, and where do these inputs go? And how do firms and organizations from different countries contribute differently to this process? This paper puts forward the results of a survey designed to shed light on these questions. Carried out in collaboration with the Society of Petroleum Engineers (SPE), the survey was sent to 469 executives and senior managers who played a significant role with regard to R&D and/or technology deployment in their respective business units. A total of 199 responses were received from a broad range of organizations and countries around the world. Several interesting themes and trends emerge from the results, including: (1) service companies tend to file considerably more patents per innovation than other types of organization; (2) over 63% of the deployed innovations reported in the survey originated in service companies; (3) neither universities nor government-led research organizations were considered to be valuable sources of new information and knowledge in the industry׳s R&D initiatives, and; (4) despite the increasing degree of globalization in the marketplace, the USA still plays an extremely dominant role in the industry׳s overall R&D and technology deployment activities. By providing a detailed and objective snapshot of how innovation happens in the upstream oil & gas sector, this paper provides a valuable foundation for future investigations and discussions aimed at improving how R&D and technology deployment are managed within the industry. The methodology did result in a coverage bias within the survey, however, and the limitations arising from this are explored.

Lighting invariant urban street classification

In this paper we propose the hybrid use of illuminant invariant and RGB images to perform image classification of urban scenes despite challenging variation in lighting conditions. Coping with lighting change (and the shadows thereby invoked) is a non-negotiable requirement for long term autonomy using vision. One aspect of this is the ability to reliably classify scene components in the presence of marked and often sudden changes in lighting. This is the focus of this paper. Posed with the task of classifying all parts in a scene from a full colour image, we propose that lighting invariant transforms can reduce the variability of the scene, resulting in a more reliable classification. We leverage the ideas of “data transfer” for classification, beginning with full colour images for obtaining candidate scene-level matches using global image descriptors. This is commonly followed by superpixellevel matching with local features. However, we show that if the RGB images are subjected to an illuminant invariant transform before computing the superpixel-level features, classification is significantly more robust to scene illumination effects. The approach is evaluated using three datasets. The first being our own dataset and the second being the KITTI dataset using manually generated ground truth for quantitative analysis. We qualitatively evaluate the method on a third custom dataset over a 750m trajectory.

Data as an asset : what the upstream oil & gas industry can learn about ‘big data’ from companies like Facebook

The upstream oil & gas industry has been contending with massive data sets and monolithic files for many years, but “Big Data”—that is, the ability to apply more sophisticated types of analytical tools to information in a way that extracts new insights or creates new forms of value—is a relatively new concept that has the potential to significantly re-shape the industry. Despite the impressive amount of value that is being realized by Big Data technologies in other parts of the marketplace, however, much of the data collected within the oil & gas sector tends to be discarded, ignored, or analyzed in a very cursory way. This paper examines existing data management practices in the upstream oil & gas industry, and compares them to practices and philosophies that have emerged in organizations that are leading the Big Data revolution. The comparison shows that, in companies that are leading the Big Data revolution, data is regarded as a valuable asset. The presented evidence also shows, however, that this is usually not true within the oil & gas industry insofar as data is frequently regarded there as descriptive information about a physical asset rather than something that is valuable in and of itself. The paper then discusses how upstream oil & gas companies could potentially extract more value from data, and concludes with a series of specific technical and management-related recommendations to this end.