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

Futures of shipbuilding in the 22nd century : Explorative industry foresight research of the long-range futures for commercial ship-building, using elements of OpenAI.

The shipbuilding industry has historically shaped global trade, logistics, research, and cultural globalization. It was instrumental in exploring and colonizing new continents, thereby significantly shaping our society. Today, it's essential to consider the industry's current transformations and speculate on what shipbuilding might look like in the 22nd century. This study is dedicated to exploring the possible futures of shipbuilding over a long-range time horizon of 70 -100 years. This thesis applied futures research methods to data collected using OpenAI tools and explored possible transformative pathways within the industry. The research offers potential future scenarios and delineates change pathways from external pressures and internal shifts within the shipbuilding system. Additionally, the study highlights the possible applications and implications of utilizing OpenAI technology in a research context. The analysis of shipbuilding incorporates the Multi-Level Perspective (MLP) concept, viewing the industry as a system involving ten groups of key actors. This structure guided the data collection process for the input of the research. The primary research process adheres to traditional futures research methods, which include horizon scanning, systems thinking, scenario building, and causal layered analysis (CLA). Furthermore, the methodology was expanded to incorporate AI-assisted techniques. This includes using AI technology for automated data collection and a separate pathway using ChatGPT-4 for computer-generated scenarios and CLA narratives development. The outcomes from both methodologies are compared, and additional literature research about the applicability and implications of using AI in futures studies. The research has identified critical external drivers of change, originating from fields such as technology, energy, and social development, as well as internal drivers, including biotechnology and diversifying floating structures. The external drivers could influence the future direction of shipbuilding, while the internal factors represent potential changes originating from within the industry. The constructed scenarios are designed to stimulate discussion and provide context for future developmental trajectories of shipbuilding

Human reliability analysis: exploring the intellectual structure of a research field

Humans play a crucial role in modern socio-technical systems. Rooted in reliability engineering, the discipline of Human Reliability Analysis (HRA) has been broadly applied in a variety of domains in order to understand, manage and prevent the potential for human errors. This paper investigates the existing literature pertaining to HRA and aims to provide clarity in the research field by synthesizing the literature in a systematic way through systematic bibliometric analyses. The multi-method approach followed in this research combines factor analysis, multi-dimensional scaling, and bibliometric mapping to identify main HRA research areas. This document reviews over 1200 contributions, with the ultimate goal of identifying current research streams and outlining the potential for future research via a large-scale analysis of contributions indexed in Scopus database

Deterministic and Probabilistic Risk Management Approaches in Construction Projects: A Systematic Literature Review and Comparative Analysis

Risks and uncertainties are inevitable in construction projects and can drastically change the expected outcome, negatively impacting the project’s success. However, risk management (RM) is still conducted in a manual, largely ineffective, and experience-based fashion, hindering automation and knowledge transfer in projects. The construction industry is benefitting from the recent Industry 4.0 revolution and the advancements in data science branches, such as artificial intelligence (AI), for the digitalization and optimization of processes. Data-driven methods, e.g., AI and machine learning algorithms, Bayesian inference, and fuzzy logic, are being widely explored as possible solutions to RM domain shortcomings. These methods use deterministic or probabilistic risk reasoning approaches, the first of which proposes a fixed predicted value, and the latter embraces the notion of uncertainty, causal dependencies, and inferences between variables affecting projects’ risk in the predicted value. This research used a systematic literature review method with the objective of investigating and comparatively analyzing the main deterministic and probabilistic methods applied to construction RM in respect of scope, primary applications, advantages, disadvantages, limitations, and proven accuracy. The findings established recommendations for optimum AI-based frameworks for different management levels—enterprise, project, and operational—for large or small data sets

Pipeline Risk Assessment Using Dynamic Bayesian Network (DBN) for Internal Corrosion

Pipelines are the most efficient mode of transportation for various chemicals and are considered as safe, yet pipeline incidents remain occurring. Corrosion is one of the main reasons for incidents especially in subsea pipelines due to the harsh corrosive environment that prevails. Corrosion can be attributed to 36% amongst all the causes of subsea pipeline failure. Internal corrosion being an incoherent process, one can never forecast exact occurrences inside a pipeline resulting in highly unpredictable risk. Therefore, this paper focuses on risk assessment of internal corrosion in subsea pipelines. Corrosion is time-dependent phenomena, and conventional risk assessment tools have limited capabilities of quantifying risk in terms of time dependency. Hence, this paper presents a Dynamic Bayesian Network (DBN) model to assess and manage the risk of internal corrosion in subsea. DBN possesses certain advantages such as representation of temporal dependence between variable, ability to handle missing data, ability to deal with continuous data, time- based risk update, observation of the change of variables with time and better representation of cause and effect relationship. This model aims to find the cause of internal corrosion and predict the consequence in case of pipeline failure given the reliability of safety barrier in place at each time step. It also demonstrates the variation of corrosion promoting agents, corrosion rate and safety barriers with time

A Fuzzy Cognitive Mapping Approach to Conduct Deficiency Investigation under SIRE 2.0 Inspection

Ship Inspection Report Programme (SIRE) 2.0 has recently become operational as a new vessel inspection regime in the tanker industry. This study proposes a methodology to analyse and address multiple deficiencies observed during SIRE 2.0 inspections. The methodology is structured based on Fuzzy Cognitive Mapping (FCM) to identify and analyse the causes of deficiencies derived from the International Maritime Organization (IMO) classification scheme, including the factors under key dimensions, such as diminished human performance, marine environment, safety administration, and management. An illustrative case study on a set of deficiencies has been conducted to ascertain the utility of the methodology. The results specifically reveal that inadequate situational communication and awareness, inadequate knowledge of ship procedures, regulations, and standards, inadequate supervision, being unaware of role or task responsibility, poor maintenance, etc. are the potential causes that might lead to the occurrence of deficiency items. Considering the dimension-based distributions of causes, the study highlights integrated preventive action recommendations specific to the analysed deficiency cases. Consequently, the study might help tanker shipping companies manage key challenges with SIRE 2.0 implementations

An integrated risk analysis framework for safety and cybersecurity of industrial SCADA system

The industrial control system (ICS) refers to a collection of various types of control systems commonly found in industrial sectors and critical infrastructures such as energy, oil and gas, transportation, and manufacturing. The supervisory control and data acquisition (SCADA) system is a type of ICS that controls and monitors operations and industrial processes scattered across a large geographic area. SCADA systems are relying on information and communication technology to improve the efficiency of operations. This integration means that SCADA systems are targeted by the same threats and vulnerabilities that affect ICT assets. This means that the cybersecurity problem in SCADA system is exacerbated by the IT heritage issue. If the control system is compromised due to this connection, serious consequences may follow. This leads to the necessity to have an integrated framework that covers both safety and security risk analysis in this context. This thesis proposes an integrated risk analysis framework that comprise of four stages, and that build on the advances of risk science and industry standards, to improve understanding of SCADA system complexity, and manage risks considering process safety and cybersecurity in a holistic approach. The suggested framework is committed to improving safety and security risk analysis by examining the expected consequences through integrated risk identifications and identifying adequate safeguards and countermeasures to defend cyber-attack scenarios. A simplified SCADA system and an undesirable scenario of overpressure in the pipeline are presented in which the relevant stages of the framework are applied