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

Oil and Gas flow Anomaly Detection on offshore naturally flowing wells using Deep Neural Networks

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe Oil and Gas industry, as never before, faces multiple challenges. It is being impugned for being dirty, a pollutant, and hence the more demand for green alternatives. Nevertheless, the world still has to rely heavily on hydrocarbons, since it is the most traditional and stable source of energy, as opposed to extensively promoted hydro, solar or wind power. Major operators are challenged to produce the oil more efficiently, to counteract the newly arising energy sources, with less of a climate footprint, more scrutinized expenditure, thus facing high skepticism regarding its future. It has to become greener, and hence to act in a manner not required previously. While most of the tools used by the Hydrocarbon E&P industry is expensive and has been used for many years, it is paramount for the industry’s survival and prosperity to apply predictive maintenance technologies, that would foresee potential failures, making production safer, lowering downtime, increasing productivity and diminishing maintenance costs. Many efforts were applied in order to define the most accurate and effective predictive methods, however data scarcity affects the speed and capacity for further experimentations. Whilst it would be highly beneficial for the industry to invest in Artificial Intelligence, this research aims at exploring, in depth, the subject of Anomaly Detection, using the open public data from Petrobras, that was developed by experts. For this research the Deep Learning Neural Networks, such as Recurrent Neural Networks with LSTM and GRU backbones, were implemented for multi-class classification of undesirable events on naturally flowing wells. Further, several hyperparameter optimization tools were explored, mainly focusing on Genetic Algorithms as being the most advanced methods for such kind of tasks. The research concluded with the best performing algorithm with 2 stacked GRU and the following vector of hyperparameters weights: [1, 47, 40, 14], which stand for timestep 1, number of hidden units 47, number of epochs 40 and batch size 14, producing F1 equal to 0.97%. As the world faces many issues, one of which is the detrimental effect of heavy industries to the environment and as result adverse global climate change, this project is an attempt to contribute to the field of applying Artificial Intelligence in the Oil and Gas industry, with the intention to make it more efficient, transparent and sustainable

Predicting Corrosion Damage in the Human Body Using Artificial Intelligence: In Vitro Progress and Future Applications Applications

Artificial intelligence (AI) is used in the clinic to improve patient care. While the successes illustrate the impact AI can have, few studies have led to improved clinical outcomes. A gap in translational studies, beginning at the basic science level, exists. In this review, we focus on how AI models implemented in non-orthopedic fields of corrosion science may apply to the study of orthopedic alloys. We first define and introduce fundamental AI concepts and models, as well as physiologically relevant corrosion damage modes. We then systematically review the corrosion/AI literature. Finally, we identify several AI models that may be Preprint implemented to study fretting, crevice, and pitting corrosion of titanium and cobalt chrome alloys

A survey on industry 4.0 for the oil and gas industry: upstream sector

The market volatility in the oil and gas (O&G) sector, the dwindling demand for oil due to the impact of COVID-19, and the push for alternative greener energy are driving the need for innovation and digitization in the O&G industry. This has attracted research interest from academia and the industry in the application of industry 4.0 (I4.0) technologies in the O&G sector. The application of some of these I4.0 technologies has been presented in the literature, but the domain still lacks a comprehensive survey of the application of I4.0 in the O&G upstream sector. This paper investigates the state-of-the-art efforts directed toward I4.0 technologies in the O&G upstream sector. To achieve this, first, an overview of the I4.0 is discussed followed by a systematic literature review from an integrative perspective for publications between 2012-2021 with 223 analyzed documents. The benefits and challenges of the adoption of I4.0 have been identified. Moreover, the paper adds value by proposing a framework for the implementation of I4.0 in the O&G upstream sector. Finally, future directions and research opportunities such as framework, edge computing, quantum computing, communication technologies, standardization, and innovative areas related to the implementation of I4.0 in the upstream sector are presented. The findings from this review show that I4.0 technologies are currently being explored and deployed for various aspects of the upstream sector. However, some of the I4.0 technologies like additive manufacturing and virtual reality are least explored

Optimizing resilience decision-support for natural gas networks under uncertainty

2019 Summer.Includes bibliographical references.Community resilience in the aftermath of a hazard requires the functionality of complex, interdependent infrastructure systems become operational in a timely manner to support social and economic institutions. In the context of risk management and community resilience, critical decisions should be made not only in the aftermath of a disaster in order to immediately respond to the destructive event and properly repair the damage, but preventive decisions should to be made in order to mitigate the adverse impacts of hazards prior to their occurrence. This involves significant uncertainty about the basic notion of the hazard itself, and usually involves mitigation strategies such as strengthening components or preparing required resources for post-event repairs. In essence, instances of risk management problems that encourage a framework for coupled decisions before and after events include modeling how to allocate resources before the disruptive event so as to maximize the efficiency for their distribution to repair in the aftermath of the event, and how to determine which network components require preventive investments in order to enhance their performance in case of an event. In this dissertation, a methodology is presented for optimal decision making for resilience assessment, seismic risk mitigation, and recovery of natural gas networks, taking into account their interdependency with some of the other systems within the community. In this regard, the natural gas and electric power networks of a virtual community were modeled with enough detail such that it enables assessment of natural gas network supply at the community level. The effect of the industrial makeup of a community on its natural gas recovery following an earthquake, as well as the effect of replacing conventional steel pipes with ductile HDPE pipelines as an effective mitigation strategy against seismic hazard are investigated. In addition, a multi objective optimization framework that integrates probabilistic seismic risk assessment of coupled infrastructure systems and evolutionary algorithms is proposed in order to determine cost-optimal decisions before and after a seismic event, with the objective of making the natural gas network recover more rapidly, and thus the community more resilient. Including bi-directional interdependencies between the natural gas and electric power network, strategic decisions are pursued regarding which distribution pipelines in the gas network should be retrofitted under budget constraints, with the objectives to minimizing the number of people without natural gas in the residential sector and business losses due to the lack of natural gas in non-residential sectors. Monte Carlo Simulation (MCS) is used in order to propagate uncertainties and Probabilistic Seismic Hazard Assessment (PSHA) is adopted in order to capture uncertainties in the seismic hazard with an approach to preserve spatial correlation. A non-dominated sorting genetic algorithm (NSGA-II) approach is utilized to solve the multi-objective optimization problem under study. The results prove the potential of the developed methodology to provide risk-informed decision support, while being able to deal with large-scale, interdependent complex infrastructure considering probabilistic seismic hazard scenarios

Application of software and hardware-based technologies in leaks and burst detection in water pipe networks: a literature review

With the rise of smart water cities, water resource management has become increasingly important. The increase in the use of intelligent leak detection technologies in the water, gas, oil, and chemical industries has led to a significant improvement in safety, customer, and environmental results, and management costs. The aim of this review article is to provide a comprehensive overview of the application of software and hardware-based technologies in leak detection and bursts in water pipeline networks. This review aims to investigate the existing literature on the subject and to analyse the key leak detection systems in the water industry. The novelty of this review is the comprehensive analysis of the literature on software and hardware-based technologies for leak and burst detection in water pipe networks. Overall, this review article contributes to understanding the latest developments and challenges in the application of software- and hardware-based technologies for leak and burst detection in water pipe networks, and serves as a valuable resource for researchers, engineers, and practitioners working in the field of water distribution systems

Green Technologies for Production Processes

This book focuses on original research works about Green Technologies for Production Processes, including discrete production processes and process production processes, from various aspects that tackle product, process, and system issues in production. The aim is to report the state-of-the-art on relevant research topics and highlight the barriers, challenges, and opportunities we are facing. This book includes 22 research papers and involves energy-saving and waste reduction in production processes, design and manufacturing of green products, low carbon manufacturing and remanufacturing, management and policy for sustainable production, technologies of mitigating CO2 emissions, and other green technologies

A complete online SVM and case base reasoning in pipe defect dection with multisensory inspection gauge

An in-line inspection (ILI) robot has been considered an inevitable requirement to perform non-destructive testing methods efficiently and economically. The detection of flaws that could lead to leakages in buried concrete pipes has been a great concern to the oil and gas industry and water resource-based industry. The major problem is the difficulty in modeling the detection of cracks due to their irregularity and randomness that cannot be easily detected. Consequently, the use of an advanced modality system has emerged. Common defects detection systems favor non-destructive testing methods, which utilize specific sensory data. Only a few systems focus on fusing different types of sensory data. Moreover, the decision mechanism in this system required heavy-power consumption sensors with the configuration from the expertise domain. In addition, the outcome of the decision system is a consequence of rule-based settings rather than a mixture of learned features. This work covers the study of defect detection of non-destructive testing methods using fusion inspection sensors, light detection and ranging (LiDAR), and Optic sensors. The studies on ILI robots are reviewed to construct an efficient gauge. The prototype robot has been designed and successfully operated in a lab-scale environment. Ultimately, the study proposed a replacement for the standard expert system - in the branch of the CBR system, which is the crucial contribution of this thesis. Recent developments in Case-based Reasoning systems (CBR) have led to an interest in favoring machine learning (ML) approaches to replace traditional weighted distance methods. However, valuable information obtained through a training process was relinquished as transferring to other phases. As a result, the complete SVM-CBR system in this thesis concentrates on solving this gap by presenting an effective transferring mechanism from phase to phase. This thesis proposed a full pipeline integration of CBR using the kernel method designated with support vector machine. SVM technique is the primary classification engine for the combined sensory data. Since the system requires a learning SVM model to be invoked in every phase, the online learning mechanism is nominated to update the model when a new case adjoins effectively. The proposed full SVM-CBR integration has been successfully built into a pipe defect detection. The achieved result indicates a substantial improvement in transferring learning information accurately

Pipeline Inspection with Autonomous Swarm Robotics

Underground water pipelines require frequent inspection to prevent decay and avoid costly repairs. The use of robots to inspect pipelines is well documented but the uniquely hostile environment of subterranean water networks means pipes require continual inspection. The idea of autonomous inspection robots that can provide continuous coverage has shown promise, but existing methods do not proactively aim to overcome a variety of diverse challenges. Specifically, extreme variability in the pipe conditions and the dense surrounding earth limit the communication capabilities of the robots, while dynamic water flows and power issues have a detrimental effect on their movement. This thesis presents the implementation of a range of path planning algorithms of varying levels of autonomy as governing swarm behaviours, each with a focus on overcoming some of the specific challenges inherent in underground water networks, with the goal of improving the efficiency with which the swarm can inspect a network. The Greedy Walk uses stochastic processes to plan a locally optimal path, the novel Ad Hoc algorithm aims to provide cyclic coverage, with robots moving as a fluid net throughout the network, and the k-Chinese Postman Problem solution explicitly plans optimal paths round subsections of a network. The thesis examines the performance of these behaviours against existing methods and anticipates obstacles in their real world implementation. The thesis then presents tailored versions of the path planning behaviours that include the introduction of more advanced methods focused on circumventing these issues. Specifically, the algorithms are developed to incorporate Gaussian Process Regression models to analyse strong water flows and use the data to plan intelligently, mitigating the detrimental effects of the flow. The flow analysis also provides a platform from which a novel Simultaneous Localisation and Mapping algorithm is presented, alongside a Multi-Objective Genetic Algorithm with the focus of increasing inspection frequency and conserving robot charge. The thesis shows evidence that an approach to pipeline inspection with autonomous swarm robotics based in path planning algorithms can help overcome the likely physical limitations of real world implementation