Assessing spatial distribution and joint uncertainty of TPH-fractions: Indicator kriging and sequential indicator simulation

2007 ◽  
Vol 87 (5) ◽  
pp. 551-563
Author(s):  
Carol Luca ◽  
Bing C Si ◽  
Richard E Farrell
Keyword(s):  
Spatial Distribution ◽  
Petroleum Hydrocarbon ◽  
Indicator Kriging ◽  
Hydrocarbon Contamination ◽  
Contaminated Sites ◽  
Sequential Indicator Simulation ◽  
Maximum Probability ◽  
Dependent Case ◽  
Indicator Simulation ◽  
Independent Case

Petroleum hydrocarbon (PHC) contamination is one of the most common contaminants in soils and remediation of PHC-contaminated sites requires methods for characterizing the spatial distribution of PHC on a site. Few studies have compared the performance of indicator kriging (IK) and sequential indicator simulation (SIS) in site characterization of petroleum-contaminated sites, or the application of these methods given the fraction based guidelines. The objectives of this study were to determine if IK and SIS indicate similar contaminated areas and to examine how the probability of exceeding thresholds changes when multiple fractions are considered simultaneously. An abandoned refinery near Kamsack, Saskatchewan, characterized by clay-textured soils was sampled and analyzed for PHC fractions (F2 and F3). The probability of a location exceeding a fraction’s remediation criteria was determined using IK and SIS. Based on critical probability thresholds, IK indicated a greater area was contaminated by F2 (6.3%) and F3 (0.8%) than SIS (4.5 and 0.6%, respectively). When the remediation criteria for both F2 and F3 were considered simultaneously, “dependent” and “independent” cases were examined. The dependent case assumed perfect correlation and used the maximum probability of either F2 or F3 as the new estimate. The independent case assumed no correlation and evaluated the probability of F2 > 2500 mg kg–1 or F3 > 6600 mg kg–1. The dependent case resulted in a smaller contaminated area than the independent case in both IK and SIS. On this site the differences between the two methods were small, although IK did smooth the distribution. Key words: Sequential indicator simulation, indicator kriging, geostatics, petroleum hydrocarbon contamination, uncertainty

Sequential indicator simulation and indicator kriging estimation of 3-dimensional soil textures

Soil Research ◽  
2009 ◽  
Vol 47 (6) ◽  
pp. 622 ◽  
Author(s):  
Y. He ◽  
D. Chen ◽  
B. G. Li ◽  
Y. F. Huang ◽  
K. L. Hu ◽  
...  
Keyword(s):  
Structural Characteristics ◽  
General Pattern ◽  
Regional Scale ◽  
Vertical Direction ◽  
Absolute Error ◽  
Indicator Kriging ◽  
Soil Profiles ◽  
Sequential Indicator Simulation ◽  
Auto Correlation ◽  
Indicator Simulation

The complex distribution characteristics of soil textures at a large or regional scale are difficult to understand with the current state of knowledge and limited soil profile data. In this study, an indicator variogram was used to describe the spatial structural characteristics of soil textures of 139 soil profiles. The profiles were 2 m deep with sampling intervals of 0.05 m, from an area of 15 km2 in the North China Plain. The ratios of nugget-to-sill values (SH) of experimental variograms of the soil profiles in the vertical direction were equal to 0, showing strong spatial auto-correlation. In contrast, SH ratios of 0.48–0.81 in the horizontal direction, with sampling distances of ~300 m, showed weaker spatial auto-correlation. Sequential indicator simulation (SIS) and indicator kriging (IK) methods were then used to simulate and estimate the 3D spatial distribution of soil textures. The outcomes of the 2 methods were evaluated by the reproduction of the histogram and variogram, and by mean absolute error of predictions. Simulated results conducted on dense and sparse datasets showed that when denser sample data are used, complex patterns of soil textures can be captured and simulated realisations can reproduce variograms with reasonable fluctuations. When data are sparse, a general pattern of major soil textures still can be captured, with minor textures being poorly simulated or estimated. The results also showed that when data are sufficient, the reproduction of the histogram and variogram by SIS was significantly better than by the IK method for the predominant texture (clay). However, when data are sparse, there is little difference between the 2 methods.

scholarly journals Facies and Petrophysical Modeling of Triassic Chang 6 Tight Sandstone Reservoir, Heshui Oil Field, Ordos Basin, China

Lithosphere ◽  
2021 ◽  
Vol 2021 (Special 1) ◽  
Author(s):  
Khawaja Hasnain Iltaf ◽  
Dali Yue ◽  
Wurong Wang ◽  
Xiaolong Wan ◽  
Shixiang Li ◽  
...  
Keyword(s):  
Indicator Kriging ◽  
Multiple Point ◽  
Low Permeability ◽  
Petrophysical Properties ◽  
Sequential Indicator Simulation ◽  
Tight Sandstone ◽  
Petrophysical Modeling ◽  
Indicator Simulation ◽  
Sandstone Reservoir

Abstract Tight sandstone reservoirs are widely distributed worldwide. The Upper Triassic Chang 6 member of the Yanchang Formation is characterized by low permeability and porosity. The facies model offers a unique approach for understanding the characteristics of various environments also heterogeneity, scale, and control of physical processes. The role of subsurface facies features and petrophysical properties was unclear. Notable insufficient research has been conducted based on facies and petrophysical modeling and that demands to refine the role of reservoir properties. To tackle this problem, a reservoir model is to be estimated using various combinations of property modeling algorithms for discrete (facies) and continuous (petrophysical) properties. Chang 6 member consists of three main facies, i.e., channel, lobe main body, and lobe margin facies. The current research is aimed at comparing the applicability and competitiveness of various facies and petrophysical modeling methods. Further, well-log data was utilized to interpret unique facies and petrophysical models to better understand the reservoir architecture. Methods for facies modeling include indicator kriging, multiple-point geostatistics, surface-based method, and sequential indicator simulation. Overall, the indicator kriging method preserved the local variability and accuracy, but some facies are smoothed out. The surface-based method showed far better results by showing the ability to reproduce the geometry, extent, connectivity, and facies association. The multiple-point geostatistics (MPG) model accurately presented the facies profiles, contacts, geometry, and geomorphological features. Sequential indicator simulation (SIS) honored the facies spatial distribution and input statistical parameters. The porosity model built using sequential Gaussian simulation (SGS) showed low porosity (74% values <2%). Gaussian random function simulation (GRFS) models showed very low average porosity (8%-10%) and low permeability (less than 0.1 mD). These methods indicate that Chang 6 member is a typical unconventional tight sandstone reservoir with ultralow values of petrophysical properties.

scholarly journals Microbial Degradation of Petroleum Hydrocarbon Contaminants: An Overview

2011 ◽  
Vol 2011 ◽  
pp. 1-13 ◽  
Author(s):  
Nilanjana Das ◽  
Preethy Chandran
Keyword(s):  
Organic Contaminants ◽  
Petroleum Hydrocarbon ◽  
Inorganic Compounds ◽  
Cost Effective ◽  
Petroleum Products ◽  
Hydrocarbon Contamination ◽  
Contaminated Sites ◽  
Cell Protein ◽  
Limited Effectiveness ◽  
Complete Mineralization

One of the major environmental problems today is hydrocarbon contamination resulting from the activities related to the petrochemical industry. Accidental releases of petroleum products are of particular concern in the environment. Hydrocarbon components have been known to belong to the family of carcinogens and neurotoxic organic pollutants. Currently accepted disposal methods of incineration or burial insecure landfills can become prohibitively expensive when amounts of contaminants are large. Mechanical and chemical methods generally used to remove hydrocarbons from contaminated sites have limited effectiveness and can be expensive. Bioremediation is the promising technology for the treatment of these contaminated sites since it is cost-effective and will lead to complete mineralization. Bioremediation functions basically on biodegradation, which may refer to complete mineralization of organic contaminants into carbon dioxide, water, inorganic compounds, and cell protein or transformation of complex organic contaminants to other simpler organic compounds by biological agents like microorganisms. Many indigenous microorganisms in water and soil are capable of degrading hydrocarbon contaminants. This paper presents an updated overview of petroleum hydrocarbon degradation by microorganisms under different ecosystems.

scholarly journals Characterization of microbial response to petroleum hydrocarbon contamination in a lacustrine ecosystem

2021 ◽  
Author(s):  
Emilio D’Ugo ◽  
Milena Bruno ◽  
Arghya Mukherjee ◽  
Dhrubajyoti Chattopadhyay ◽  
Roberto Giuseppetti ◽  
...  
Keyword(s):  
Oil Spill ◽  
Petroleum Hydrocarbon ◽  
Oil Spills ◽  
Oil Pollution ◽  
Primary Source ◽  
Hydrocarbon Contamination ◽  
Petroleum Hydrocarbon Contamination ◽  
Microbial Response ◽  
Pollution Event ◽  
Hydrocarbonoclastic Potential

AbstractMicrobiomes of freshwater basins intended for human use remain poorly studied, with very little known about the microbial response to in situ oil spills. Lake Pertusillo is an artificial freshwater reservoir in Basilicata, Italy, and serves as the primary source of drinking water for more than one and a half million people in the region. Notably, it is located in close proximity to one of the largest oil extraction plants in Europe. The lake suffered a major oil spill in 2017, where approximately 400 tons of crude oil spilled into the lake; importantly, the pollution event provided a rare opportunity to study how the lacustrine microbiome responds to petroleum hydrocarbon contamination. Water samples were collected from Lake Pertusillo 10 months prior to and 3 months after the accident. The presence of hydrocarbons was verified and the taxonomic and functional aspects of the lake microbiome were assessed. The analysis revealed specialized successional patterns of lake microbial communities that were potentially capable of degrading complex, recalcitrant hydrocarbons, including aromatic, chloroaromatic, nitroaromatic, and sulfur containing aromatic hydrocarbons. Our findings indicated that changes in the freshwater microbial community were associated with the oil pollution event, where microbial patterns identified in the lacustrine microbiome 3 months after the oil spill were representative of its hydrocarbonoclastic potential and may serve as effective proxies for lacustrine oil pollution.

scholarly journals Chemical and Physical Analysis of a Petroleum Hydrocarbon Contamination on a Soil Sample to Determine Its Natural Degradation Feasibility

Inventions ◽  
2020 ◽  
Vol 5 (3) ◽  
pp. 43
Author(s):  
Karuna Arjoon ◽  
James G. Speight
Keyword(s):  
Contaminated Soils ◽  
Petroleum Hydrocarbon ◽  
Fossil Fuels ◽  
Chemical Properties ◽  
Cost Effective ◽  
Polluted Soil ◽  
Hydrocarbon Contamination ◽  
Contaminated Site ◽  
Physical Analysis ◽  
Oil Composition

Crude oil is the world’s leading fuel source and is the lifeblood of the industrialized nations as it is vital to produce many everyday essentials. This dependency on fossil fuels has resulted in serious environmental issues in recent times. Petroleum contaminated soils must be treated to ensure that human health and the environment remain protected. The restoration of petroleum-polluted soil is a complex project because once petroleum hydrocarbon enters the environment, the individual constituents will partition to various environmental compartments in accordance with their own physical–chemical properties; therefore, the composition and inherent biodegradability of the petroleum hydrocarbon pollutant determines the suitability of a remediation approach. The objective of this study was to assess the prospective of bioremediation as a feasible technique for practical application to the treatment of petroleum hydrocarbon-contaminated soils, by trending the changes in the properties of the petroleum due to biodegradation. Each polluted soil has particularities, thus, the bioremediation approach for each contaminated site is unique. Therefore, hydrocarbon-contaminated sites that have remained polluted for decades due to lack of proper decontamination treatments present in this part of the world would benefit from cost effective treatments. Most bioremediation case studies are usually based on hypothetical assumptions rather than technical or experimental data; providing data that show the capabilities of biodegradation of indigenous microbes on specific oil composition can lead to the creation of strategies to accelerate the biological breakdown of hydrocarbons in soil.

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