A Graphical Adversarial Risk Analysis Model for Oil and Gas Drilling Cybersecurity

Oil and gas drilling is based, increasingly, on operational technology, whose cybersecurity is complicated by several challenges. We propose a graphical model for cybersecurity risk assessment based on Adversarial Risk Analysis to face those challenges. We also provide an example of the model in the context of an offshore drilling rig. The proposed model provides a more formal and comprehensive analysis of risks, still using the standard business language based on decisions, risks, and value.Comment: In Proceedings GraMSec 2014, arXiv:1404.163

Machine learning effects on the norwegian oil and gas industry

The downturn in the Norwegian oil industry in recent years has led to a revaluation of the sector. Out of this turmoil, a new surge of innovation appeared. This paper explores the innovation effects machine learning (ML) technology has brought to the Norwegian oil and gas industry (NOGI) using a qualitative approach through conducting semi-structured qualitative interviews. These interviews focus on five unique perspectives within the industry. These perspectives represent the unique interplay between private and public actors on the Norwegian continental shelf (NCS). The interviews discuss the value of big data, the use of ML in optimizing extraction processes and finding more sustainable approaches to detecting oil and gas. After presenting the five perspectives in the analysis, similarities and differences are discussed in light of the role the actors i.e. the companies play on the NCS. Interviewees expressed their enthusiasm and aversions about using new technologies to secure competitive advantages, despite most companies developing similar uses of ML. Throughout the analysis, background information from website searches and analyses are used to provide context for the interview data. The results show that the use of data, advanced analytics and various forms of ML create opportunities to fundamentally reimagine how and where work gets done and that there are possibilities of finding safer, more cost efficient and more sustainable approaches to the work currently being done through ML in the NOGI. The study shows that ML has brought disruptive innovation to the NOGI that enhances competitive advantages

Analysing long-term opportunities for offshore energy system integration in the Danish North Sea

Acknowledgment The authors gratefully acknowledge the financial support of the Danish Hydrocarbon Research and Technology centre (DHRTC) for funding this research in the context of the “Alternative use of offshore infrastructures and reservoirs” program. Any remaining errors are the authors’ responsibility.Peer reviewedPublisher PD

Condition Assessment, Remaining Useful Life Prediction and Life Extension Decision Making for Offshore Oil and Gas Assets

Offshore oil and gas assets are highly complex structures comprising of several components, designed to have a lifecycle of about 20 to 30 years of working under harsh operational and environmental conditions. These assets, during their operational lifetime, are subjected to various degradation mechanisms such as corrosion, erosion, wear, creep and fatigue cracks. In order to improve economic viability and increase profitability, many operators are looking at extending the lifespan of their assets beyond the original design life, thereby making life extension (LE) an increasingly critical and highly-discussed topic in the offshore oil and gas industry. In order to manage asset aging and meet the LE requirements, offshore oil and gas operators have adopted various approaches such as following maintenance procedures as advised by the original equipment manufacturer (OEM), or using the experience and expertise of engineers and inspectors. However, performing these activities often provides very limited value addition to operators during the LE period of operation. This paper aims to propose a systematic framework to help operators meet LE requirements while optimizing their cost structure. This framework establishes an integration between three individual life assessment modules, namely: condition assessment, remaining useful life (RUL) prediction and LE decision-making. The benefits of the proposed framework are illustrated through a case study involving a three-phase separator system on a platform which was constructed in the mid-1970s in West Africa. The results of this study affirm the effectiveness of this framework in minimizing catastrophic failures during the LE phase of operations, whilst ensuring compliance to regulatory requirements

Understanding AI Application Dynamics in Oil and Gas Supply Chain Management and Development: A Location Perspective

The purpose of this paper is to gain a better understanding of Artificial Intelligence (AI) application dynamics in the oil and gas supply chain. A location perspective is used to explore the opportunities and challenges of specific AI technologies from upstream to downstream of the oil and gas supply chain. A literature review approach is adopted to capture representative research along these locations. This was followed by descriptive and comparative analysis for the reviewed literature. Results from the conducted analysis revealed important insights about AI implementation dynamics in the oil and gas industry. Furthermore, various recommendations for technology managers, policymakers, practitioners, and industry leaders in the oil and gas industry to ensure successful AI implementation were outlined. Doi: 10.28991/HIJ-SP2022-03-01 Full Text: PD

LowEmission Annual report 2022

publishedVersio

Optimization of maintenance performance for offshore production facilities

Master's thesis in Offshore technologyNew technologies are becoming advanced and complex for offshore production facilities. However this advancement and complexity in technology creates a more complicated and time consuming forensic processes for finding causes of failure, or diagnostic processes to identify events that reduce performance. As a result, micro-sensors, efficient signaling and communication technologies for collecting data efficiently, advanced software tools (such as fuzzy logic, neural networks, and simulation based optimization) have been developed, in parallel, to manage such complex assets. Given the nature and scale of ongoing changes on complexities, there are emerging concerns that increasing complexities, ill-defined interfaces, unforeseen events can easily lead to serious performance failures and major risks. To avoid such undesirable circumstances, „just-in-time‟ measures of performance to ensure fully functional is absolutely necessary. The increasing trend in complexity creates a motivation to develop an integrated maintenance management framework to get real-time information to solve problems quickly and hence to increase functional performance (help the asset to perform its required function effectively and efficiently while safeguarding life and the environment). Establishing “just-in-time” maintenance and repairs based on true machine condition maximizes critical asset useful life and eliminates premature replacement of functional components. This thesis focuses on developing an integrated maintenance management framework to establish „just-in-time‟ maintenance and to ensure continuous improvements based on maintenance domain experts as well as operational and historic data. To do this, true degradation of components must be identified. True level of degradation often cannot be inferred by the mere trending of condition indicator‟s level (CBM), because condition indicator levels are modulated under the influence of the diverse operating context. Besides, the maintenance domain expert does not have a precise knowledge about the correlation of the diverse operating context and level of degradation for a given level of condition indicator on specific equipment. Efforts have been made in here to identify the true degradation pattern of a component by analyzing these vagueness and imprecise knowledge. Key words: effective and efficient maintenance strategy, ‘just-in-time’ maintenance, condition based maintenance, P-F interval

NAVIGATING TOWARDS A DIGITAL ECOSYSTEM: THE CASE STUDY OF OFFSHORE INFRASTRUCTURE INDUS- TRY

While Information Systems (IS) researchers have studied the role of digital platform governance mechanisms in digital ecosystems formation, less is known about the dynamics shaping nascent eco-systems formation prior to platfromization. This is particularly important in understanding transitions towards digital ecosystems in established industrial settings. Applying the theoretical lens of ambidex-terity, we investigate a case study of establishing a digital ecosystem in the offshore infrastructure in-dustry. Our findings indicate that although firms seek transition towards operating in a digital ecosys-tem, the uncertainties of the situation ahead make it difficult for them to know just how such a digital form of organizing would look. To this end, we identify three digital ecosystem strategies companies pursue during nascent digital ecosystems formation: data structure control, data flow control and data content control. Based on these, we offer three contributions to existing digital ecosystems literature

Strategic and Tactical Crude Oil Supply Chain: Mathematical Programming Models

Crude oil industry very fast became a strategic industry. Then, optimization of the Crude Oil Supply Chain (COSC) models has created new challenges. This fact motivated me to study the COSC mathematical programming models. We start with a systematic literature review to identify promising avenues. Afterwards, we elaborate three concert models to fill identified gaps in the COSC context, which are (i) joint venture formation, (ii) integrated upstream, and (iii) environmentally conscious design