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How Do We Accelerate Uptake and Fulfill the Value Potential of Intelligent Energy? Summary The objective of this paper is to identify ways to accelerate the uptake and fulfill the value potential of Intelligent Energy (IE). The paper is coauthored by a cross-industry group drawn from operators, service providers, and product vendors, all of whom have been involved in the IE arena for 10 years or more. We have analyzed past experiences to identify both ways in which IE has been successful and the improvements that could be made to add value across a broader scale amid the challenges of today's commercial environment. In this paper, assessments are given on IE implementations to identify practical ways in which we can expand deployment and deliver results more quickly, including the importance of collaboration and competition in the IE domain, and how longer-term business models and new organizational ideas could improve the industry's uptake of IE. We have identified two areas in which we believe changes to our approach could deliver significant benefits--through the expanded use of integrated work flows and shared subject-matter-expert (SME) services. We discuss the benefits and challenges of this integrated approach to solution design, work processes, technology, skills, and competencies. Field cases from two major operators are given as best-practice examples on advanced use of IE in the oiland-gas industry. Introduction After more than 10 years of IE initiatives, the industry has increasingly published lessons learned from the early years (e.g., Lilleng et al. 2012; de Best and van den Berg 2012; Dickens et al. 2012; Dhubaiki et al. 2013; Lochmann and Brown 2014; Gilman and Nordtvedt 2014). As IE moves into its second decade, the landscape is changing. The decline in oil price, the resulting pressure on costs, and the rise of unconventionals are just some of the changes that present both risks and opportunities for IE to flourish (Pickering and Sengupta 2015). IE solutions during the first decade focused mainly on new technologies, better use of real-time data, new applications to analyze and visualize data, improving the data foundation, and increasing collaboration. Frequently, this has led to an increase in operational complexity with an associated increase in personnel. The financial climate allowed (or perhaps encouraged) us to work in this way, because the high oil price meant that the greatest benefits came from increasing production rather than cutting costs, and adding personnel made sense if they could deliver increased production. Most operators took a technology-driven, functional approach to provide us with improved surveillance, analysis, and collaboration tools. This has driven us mostly toward developing better tools to improve existing work processes.
- Asia > Middle East (0.93)
- North America > United States (0.68)
- Europe > United Kingdom > North Sea (0.28)
- Europe > Norway > North Sea (0.28)
- North America > United States > Mississippi > Mariner Field (0.99)
- Europe > United Kingdom > North Sea > Central North Sea > Moray Firth > Moray Firth Basin > Block 13/22a > Captain Field > Captain Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > Utsira High > PL 501 > Block 16/5 > Johan Sverdrup Field > Zechstein Formation (0.99)
- (40 more...)
- Management > Professionalism, Training, and Education > Personnel competence (1.00)
- Management > Professionalism, Training, and Education > Communities of practice (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Knowledge management (1.00)
- Management > Asset and Portfolio Management > Field development optimization and planning (0.93)
Human-Reliability Analysis for the Petroleum Industry: Lessons Learned From Applying SPAR-H
van de Merwe, Koen (DNV GL) | Hogenboom, Sandra (DNV GL) | Rasmussen, Martin (Norwegian University of Science and Technology) | Laumann, Karin (Norwegian University of Science and Technology) | Gould, Kristian (Statoil)
Summary Human error has proved to be a contributor to a number of major accidents in the petroleum industry. However, quantitative risk analysis (QRA) has only to a limited extent taken into account the contribution of human performance to major accident risk. Human-reliability analysis (HRA) has the potential to overcome this by systematically analyzing human performance for safety-critical tasks. A joint effort between industry and academia is under way in Norway to adapt SPAR-H [the standardized-plant-analysis-risk/HRA (SPAR-H) method], an HRA technique from the nuclear industry, to a petroleum setting (PetroHRA). This paper discusses some of the lessons learned so far in directly applying the technique to a petroleum case study. A case study was performed in which the operator's task was to manually activate the platform's depressurization system upon detection of a hydrocarbon leakage. The factors influencing the performance [performance-shaping factors (PSFs)] of the operator were analyzed, indicating the potential contributors to operator failure. The PSFs were time, stress/stressors, complexity, experience/training, procedures, human/machine interface (HMI)/ergonomics, fitness for duty, and work processes. Numerous issues were identified when directly applying SPAR-H. These were challenges in deciding on the multiplier of the PSFs, the potential for overlap between the PSFs, the industry specificity of the HMI/ergonomics PSF description, and the method's tendency to inflate human-error probabilities (HEPs). A first step to improve the definitions and guidance material for a petroleum-specific SPAR-H was taken in a separate literature study performed by two of the authors of this paper (Rasmussen et al., submitted 2013). It was further shown that it is possible and relatively straightforward to directly apply and integrate SPAR-H in QRA in a petroleum context. In addition, the qualitative outcomes provided a structured and meaningful understanding of human performance previously not available to QRA. Ultimately, this effort contributes to a further integration of HRA and QRA and therewith provides valuable insights about how to manage human performance associated with major-accident risk. With the frequency of major accidents and serious incidents not being significantly reduced, HRA can prove to be a valuable tool for improving process safety.
- Europe > Norway (0.67)
- North America > United States > Texas (0.46)
- North America > United States > California (0.46)
- Management > Professionalism, Training, and Education > Communities of practice (1.00)
- Health, Safety, Environment & Sustainability > Health > Ergonomics (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Knowledge management (1.00)
- Health, Safety, Environment & Sustainability > Safety > Human factors (engineering and behavioral aspects) (0.90)
Summary In oil and gas markets, the relationships between the spot and futures prices reveal important opportunities for value creation. When oil prices are in contango (i.e., when futures prices are higher than the expected future spot prices), it may be profitable for a trader to hold oil in storage and enter into a futures contract instead of selling oil in the spot market. The decision to either sell oil in the spot market or use the storage to sell oil in the future is usually challenging because the future spot prices and futures prices are uncertain. In this paper, we discuss the storage trading decisions by use of a realistic example, and we propose an analysis methodology on the basis of a two-factor price process for modeling spot and futures oil prices. The dynamic decision problem, sell spot or sell forward, is analyzed with a forward dynamic optimization algorithm and the least-squares Monte Carlo simulation.