3,510 research outputs found
Quantification of Uncertainties in Inline Inspection Data for Metal-loss Corrosion on Energy Pipelines and Implications for Reliability Analysis
One of the major threats to the oil and gas transmission pipeline integrity is metal-loss corrosion. Pipeline operators periodically inspect the size of the metal loss corrosion in a pipeline using in-line inspection (ILI) tools to avoid pipe failure which may lead to severe consequences. To predict pipe failure efficiently, reliability-based corrosion management program is gaining popularity as it effectively incorporates all the uncertainties involved in the pipe failure prediction. The focus of the research reported in this thesis is to investigate the unaddressed issues in the reliability-based corrosion assessment to assist in better predicting pipe failure.
First, a methodology is proposed to facilitate the use of RSTRENG (Remaining Strength of Corroded Pipe) and CSA (Canadian standards association) burst pressure capacity models in reliability-based failure prediction of pipelines. Use of RSTRENG and CSA models require the detail geometric information of a corrosion defect, which may not be available in the ILI reports. To facilitate the use of CSA and RSTRENG models in the reliability analysis, probabilistic characteristics of parameters that relate the detailed defect geometry to its simplified characterizing parameters was derived by using the high-resolution geometric data for a large set of external metal-loss corrosion defects identified on an in-service pipeline in Alberta, Canada.
Next, a complete framework is proposed to quantify the measurement error associated with the ILI measured corrosion defect length, effective length, and effective depth of oil and gas pipelines. A relatively large set of ILI-reported and field-measured defect data is collected from different in-service pipelines in Canada and used to develop the measurement error models. The proposed measurement error models associated with the ILI reported corrosion defect length, effective length, and effective depth is the weighted average of the measurement errors of the corresponding Type I and Type II defects and the weighted factor is the likelihood of ILI reported corrosion defect being a Type I defect (without cluster error) or a Type II defect (with clustering error). A log-logistic model is proposed to quantify the weighted factor. The application of the proposed measurement error models is demonstrated by evaluating probability of failure of a real corroded pipe joint through system reliability analysis
Human and environmental exposure to hydrocarbon pollution in the Niger Delta:A geospatial approach
This study undertook an integrated geospatial assessment of human and environmental exposure to oil pollution in the Niger Delta using primary and secondary spatial data. This thesis begins by presenting a clear rationale for the study of extensive oil pollution in the Niger Delta, followed by a critical literature review of the potential application of geospatial techniques for monitoring and managing the problem. Three analytical chapters report on the methodological developments and applications of geospatial techniques that contribute to achieving the aim of the study. Firstly, a quantitative assessment of human and environmental exposure to oil pollution in the Niger Delta was performed using a government spill database. This was carried out using Spatial Analysis along Networks (SANET), a geostatistical tool, since oil spills in the region tend to follow the linear patterns of the pipelines. Spatial data on pipelines, oil spills, population and land cover data were analysed in order to quantify the extent of human and environmental exposure to oil pollution. The major causes of spills and spatial factors potentially reinforcing reported causes were analysed. Results show extensive general exposure and sabotage as the leading cause of oil pollution in the Niger Delta. Secondly, a method of delineating the river network in the Niger Delta using Sentinel-1 SAR data was developed, as a basis for modelling potential flow of pollutants in the distributary pathways of the network. The cloud penetration capabilities of SAR sensing are particularly valuable for this application since the Niger Delta is notorious for cloud cover. Vector and raster-based river networks derived from Sentinel-1 were compared to alternative river map products including those from the USGS and ESA. This demonstrated the superiority of the Sentinel-1 derived river network, which was subsequently used in a flow routing analysis to demonstrate the potential for understanding oil spill dispersion. Thirdly, the study applied optical remote sensing for indirect detection and mapping of oil spill impacts on vegetation. Multi-temporal Landsat data was used to delineate the spill impact footprint of a notable 2008 oil spill incident in Ogoniland and population exposure was evaluated. The optical data was effective in impact area delineation, demonstrating extensive and long-lasting population exposure to oil pollution. Overall, this study has successfully assembled and produced relevant spatial and attribute data sets and applied integrated geostatistical analytical techniques to understand the distribution and impacts of oil spills in the Niger Delta. The study has revealed the extensive level of human and environmental exposure to hydrocarbon pollution in the Niger Delta and introduced new methods that will be valuable fo
The big picture:the future role of gas
There are a plethora of drivers of change in energy systems until 2015. The role of social and political actors is likely to be more noticeable. In Europe, locally, high-impact ideas like green consumerism and limited acceptance of energy systems that result in trade-offs will be important. Nationally, the empowerment of individuals and communities and the politicization of energy-related issues will be drivers of change. Internationally, energy issues will become more important in the foreign and security policies of state and non-state actors
Assessing and augmenting SCADA cyber security: a survey of techniques
SCADA systems monitor and control critical infrastructures of national importance such as power generation and distribution, water supply, transportation networks, and manufacturing facilities. The pervasiveness, miniaturisations and declining costs of internet connectivity have transformed these systems from strictly isolated to highly interconnected networks. The connectivity provides immense benefits such as reliability, scalability and remote connectivity, but at the same time exposes an otherwise isolated and secure system, to global cyber security threats. This inevitable transformation to highly connected systems thus necessitates effective security safeguards to be in place as any compromise or downtime of SCADA systems can have severe economic, safety and security ramifications. One way to ensure vital asset protection is to adopt a viewpoint similar to an attacker to determine weaknesses and loopholes in defences. Such mind sets help to identify and fix potential breaches before their exploitation. This paper surveys tools and techniques to uncover SCADA system vulnerabilities. A comprehensive review of the selected approaches is provided along with their applicability
Use of mixed-type data clustering algorithm for characterizing temporal and spatial distribution of biosecurity border detections of terrestrial non-indigenous species
Appropriate inspection protocols and mitigation strategies are a critical component of effective biosecurity measures, enabling implementation of sound management decisions. Statistical models to analyze biosecurity surveillance data are integral to this decision-making process. Our research focuses on analyzing border interception biosecurity data collected from a Class A Nature Reserve, Barrow Island, in Western Australia and the associated covariates describing both spatial and temporal interception patterns. A clustering analysis approach was adopted using a generalization of the popular k-means algorithm appropriate for mixed-type data. The analysis approach compared the efficiency of clustering using only the numerical data, then subsequently including covariates to the clustering. Based on numerical data only, three clusters gave an acceptable fit and provided information about the underlying data characteristics. Incorporation of covariates into the model suggested four distinct clusters dominated by physical location and type of detection. Clustering increases interpretability of complex models and is useful in data mining to highlight patterns to describe underlying processes in biosecurity and other research areas. Availability of more relevant data would greatly improve the model. Based on outcomes from our research we recommend broader use of cluster models in biosecurity data, with testing of these models on more datasets to validate the model choice and identify important explanatory variables
Quantitative Oil Source-Fingerprinting Techniques and Their Application to Differentiating Crude Oil in Coastal Marsh Sediments
Oil source-fingerprinting is an environmental forensics technique that uses analytical chemistry to determine the origin of oil residues in environmental samples by comparison to a known or suspected source oil. Currently, the only standardized method for oil source fingerprinting is a qualitative approach that is very effective in almost every oil spill response situation. However, the need for quantitative oil source-fingerprinting methods to complement the qualitative determinations is extremely desired. The research herein aims to utilize data generated by gas chromatography/mass spectrometry (GC/MS) methodologies to test two different quantitative techniques: diagnostic biomarker ratio analysis and chemometrics. The most common crude oil constituents used for oil source-fingerprinting are the oil biomarker compounds. Oil biomarkers are polycyclic aliphatic hydrocarbon molecules typically resistant to environmental weathering (i.e., biological and physiochemical transformations). They are universal in crude oils and most petroleum products, and impart unique ratios in oils of different maturities and geographic sources. Diagnostic biomarker ratio analysis will be used to establish a suite of diagnostic biomarker ratios with statistical limitations that can differentiate oil from the Deepwater Horizon oil spill, or Macondo 252 (MC252) oil, from other South Louisiana crude oils. This technique is not limited to MC252 oil. Diagnostic ratios can be determined and tested for any source oil. Current published research has documented weathering of several of the biomarker compounds used for oil source-fingerprinting. Any weathering of MC252 oil residues in the environment will adversely affect the diagnostic biomarker ratio analysis. Therefore, a more advanced quantitative technique, chemometrics, will use pattern recognition algorithms to determine the innate similarity of environmental oil residues to MC252 oil
Synthetic models of distribution gas networks in low-carbon energy systems
L'abstract è presente nell'allegato / the abstract is in the attachmen
Literature review on the smart city resources analysis with big data methodologies
This article provides a systematic literature review on applying different algorithms to municipal data processing, aiming
to understand how the data were collected, stored, pre-processed, and analyzed, to compare various methods, and to select
feasible solutions for further research. Several algorithms and data types are considered, finding that clustering, classification,
correlation, anomaly detection, and prediction algorithms are frequently used. As expected, the data is of several types,
ranging from sensor data to images. It is a considerable challenge, although several algorithms work very well, such as Long
Short-Term Memory (LSTM) for timeseries prediction and classification.Open access funding provided by FCT|FCCN (b-on).info:eu-repo/semantics/publishedVersio
Spatially-resolved Assessment of Land and Water Use Scenarios for Shale Gas Development: Poland and Germany
The analysis presented in this report focuses specifically on two issues of potential concern with respect to shale gas development in EU member states using hydraulic fracturing technologies: pressure on freshwater resources, and land use competition. Potential alternative technologies, such as “dry fracking”, are not considered, because they are still at the research and development stage.
We reviewed available literature in order to identify important variables that may influence the land and water requirements associated with shale gas development. We further derived a range of representative values spanning worst-, average- and best-case scenarios for each variable. We then coupled specific technology scenarios (incorporating these variables) regarding water and land use requirements for shale gas development from 2013-2028 with spatially-resolved water and land availability/demand modeling tools (i.e. using the European Land Use Modelling Platform (LUMP)). Scenario analyses (intended to represent worst-, average- and best-case assumptions) were subsequently implemented that incorporate a subset of the identified variables for shale gas development in the Lower Paleozoic Baltic-Podlasie-Lublin basin in Poland and for Germany as a whole from 2013-2028.
In addition, we undertook a screening-level risk assessment of potential human and ecosystem health impacts attributable to accidental or operational release of chemicals used in hydraulic fracturing of shale formations, as well as the average gaseous emissions (per active well) associated with shale gas development activities that might be anticipated within a shale play. Finally, we developed a qualitative discussion of necessary considerations to support future air quality impact assessments for shale gas development activities.JRC.H.8-Sustainability Assessmen
- …