Risk Assessment and Management of Petroleum Transportation Systems Operations

Petroleum Transportation Systems (PTSs) have a significant impact on the flow of crude oil within a Petroleum Supply Chain (PSC), due to the great demand on this natural product. Such systems are used for safe movement of crude and/or refined products from starting points (i.e. production sites or storage tanks), to their final destinations, via land or sea transportation. PTSs are vulnerable to several risks because they often operate in a dynamic environment. Due to this environment, many potential risks and uncertainties are involved. Not only having a direct effect on the product flow within PSC, PTSs accidents could also have severe consequences for the humans, businesses, and the environment. Therefore, safe operations of the key systems such as port, ship and pipeline, are vital for the success of PTSs. This research introduces an advanced approach to ensure safety of PTSs. This research proposes multiple network analysis, risk assessment, uncertainties treatment and decision making techniques for dealing with potential hazards and operational issues that are happening within the marine ports, ships, or pipeline transportation segments within one complete system. The main phases of the developed framework are formulated in six steps. In the first phase of the research, the hazards in PTSs operations that can lead to a crude oil spill are identified through conducting an extensive review of literature and experts’ knowledge. In the second phase, a Fuzzy Rule-Based Bayesian Reasoning (FRBBR) and Hugin software are applied in the new context of PTSs to assess and prioritise the local PTSs failures as one complete system. The third phase uses Analytic Hierarchy Process (AHP) in order to determine the weight of PTSs local factors. In the fourth phase, network analysis approach is used to measure the importance of petroleum ports, ships and pipelines systems globally within Petroleum Transportation Networks (PTNs). This approach can help decision makers to measure and detect the critical nodes (ports and transportation routes) within PTNs. The fifth phase uses an Evidential Reasoning (ER) approach and Intelligence Decision System (IDS) software, to assess hazards influencing on PTSs as one complete system. This research developed an advance risk-based framework applied ER approach due to its ability to combine the local/internal and global/external risk analysis results of the PTSs. To complete the cycle of this study, the best mitigating strategies are introduced and evaluated by incorporating VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and AHP to rank the risk control options. The novelty of this framework provides decision makers with realistic and flexible results to ensure efficient and safe operations for PTSs

Stratigraphic interpretation of Well-Log data of the Athabasca Oil Sands of Alberta Canada through Pattern recognition and Artificial Intelligence

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Automatic Stratigraphic Interpretation of Oil Sand wells from well logs datasets typically involve recognizing the patterns of the well logs. This is done through classification of the well log response into relatively homogenous subgroups based on eletrofacies and lithofacies. The electrofacies based classification involves identifying clusters in the well log response that reflect ‘similar’ minerals and lithofacies within the logged interval. The identification of lithofacies relies on core data analysis which can be expensive and time consuming as against the electrofacies which are straight forward and inexpensive. To date, challenges of interpreting as well as correlating well log data has been on the increase especially when it involves numerous wellbore that manual analysis is almost impossible. This thesis investigates the possibilities for an automatic stratigraphic interpretation of an Oil Sand through statistical pattern recognition and rule-based (Artificial Intelligence) method. The idea involves seeking high density clusters in the multivariate space log data, in order to define classes of similar log responses. A hierarchical clustering algorithm was implemented in each of the wellbores and these clusters and classifies the wells in four classes that represent the lithologic information of the wells. These classes known as electrofacies are calibrated using a developed decision rules which identify four lithology -Sand, Sand-shale, Shale-sand and Shale in the gamma ray log data. These form the basis of correlation to generate a subsurface model

Knowledge formalization in experience feedback processes : an ontology-based approach

Because of the current trend of integration and interoperability of industrial systems, their size and complexity continue to grow making it more difficult to analyze, to understand and to solve the problems that happen in their organizations. Continuous improvement methodologies are powerful tools in order to understand and to solve problems, to control the effects of changes and finally to capitalize knowledge about changes and improvements. These tools involve suitably represent knowledge relating to the concerned system. Consequently, knowledge management (KM) is an increasingly important source of competitive advantage for organizations. Particularly, the capitalization and sharing of knowledge resulting from experience feedback are elements which play an essential role in the continuous improvement of industrial activities. In this paper, the contribution deals with semantic interoperability and relates to the structuring and the formalization of an experience feedback (EF) process aiming at transforming information or understanding gained by experience into explicit knowledge. The reuse of such knowledge has proved to have significant impact on achieving themissions of companies. However, the means of describing the knowledge objects of an experience generally remain informal. Based on an experience feedback process model and conceptual graphs, this paper takes domain ontology as a framework for the clarification of explicit knowledge and know-how, the aim of which is to get lessons learned descriptions that are significant, correct and applicable

A Fuzzy – Based Methodology for Aggregative Waste Minimization in the Wine Industry

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Target Detection: New Techniques for Defence Applications

Discusses the machine interpretation of remotely-sensed in terms of human concepts by combining the techniques of artificial intelligence, pattern recognition and image analysis. Describes the solution to cognitive problem in registration, material classification, region extraction, structural analysis, semantic reasoning and problem solving architecture. The paper also discusses briefly the weapon-borne recognition systems using simulation and prediction models to hypothesise target appearance observed by sensors

Modification of fuzzy logic rule base in the optimization of traffic light control system

Road intersections, bad roads, accidents, road construction works, emergencies, etc. are some of the primary causes of high traffic congestions in urban areas. In an attempt to solve some of these problems, traffic wardens and traffic light control systems are employed at road intersections to ensure that deadlocks are avoided. However, the use of traffic warden is associated with weariness which can lead to poor judgement in allocating the right of way to motorist. An alternative approach is to employ the use of Traffic light control system in the management of the increased traffic congestion that is always experience in urban areas. The use of dynamic phase scheduling traffic control system has proven more efficient as compared to the static phase scheduling traffic control system. In this paper, an attempt was made to improve upon an earlier optimized traffic light control system developed using simulation of urban mobility (SUMO) in conjunction with fuzzy inference system which played the role of optimizing the traffic light control system. The modified fuzzy rule based gave a superior average waiting time of 72.07% improvement as compared to an earlier average waiting time improvement of 65.35%. This is an indication that amongst other factors, the size of the fuzzy rule base plays a significant role when fuzzy logic is employed in the optimization of traffic light control systems.Keywords: Fuzzy Logic Controller, Dynamic Automated Traffic Light System, Static Automated Traffic Light Syste