An optical analysis of the organic soil over an old petroleum tar deposit

- 1 - An optical analysis of the organic soil over an old petroleum tar deposit 1 2

4 Museum National d’Histoire Naturelle, CNRS UMR 5176, 4 avenue du Petit Château, 5 91 800 Brunoy, France 6 7 Abstract 8 9 We analysed by an optical method (the small volume method) the composition 10 and the vertical distribution of an organic soil which accumulated over an old petroleum 11 tar deposit. The study site was an oil refinery now colonised by woody vegetation since 12 the time of a...

Some Investigation on Bioremediation of PahsContaminated Soil in Iran Tar Refinery

The main purpose of this research is studying the possibility of bioremediation of polycyclic aromatic hydrocarbons (PAHs) contaminated soils in Iran’s tar refinery area located in Isfahan. For many years a great quantity of tar produced by neighboring metallurgical plant has been stored in lagoons as natural reservoirs, causing the contamination of soil in this area. Microorganisms capable of ...

Species assemblages and diets of Collembola in the organic matter accumulated over an old tar deposit

The Viability of Oil Extraction from Trinidad Tar Sands by Radio Frequency Heating: A Simulation Approach

Trinidad has tar sand resources of about 2 billion barrels of oil on land in the Parrylands/Guapo and Brighton areas. With an oil price of over USD 25 per barrel, the commercial extraction of oil from Trinidad tar sands is viable, but it requires a careful study. The relatively small extent of this tar sand (about 10,000 acres and with depths varying from surface to less than 500 feet) and with...

Neural Network Meta-Modeling of Steam Assisted Gravity Drainage Oil Recovery Processes

Production of highly viscous tar sand bitumen using Steam Assisted Gravity Drainage (SAGD) with a pair of horizontal wells has advantages over conventional steam flooding. This paper explores the use of Artificial Neural Networks (ANNs) as an alternative to the traditional SAGD simulation approach. Feed forward, multi-layered neural network meta-models are trained through the Back-...