Needs and opportunities: nondestructive evaluation for energy systems

Advanced manufacturing and new energy systems are presenting a wide variety of challenges for nondestructive testing and evaluation (NDT/NDE). This paper discusses the state of the art, needs and opportunities for NDE to provide reliable, effective and economic inspection and monitoring for energy systems. It introduces issues of materials, defects and allowables, the evolution of advanced NDT and NDE and then considers examples of NDE for energy systems. These include applications in the petrochemical industry, advanced and additive manufacturing, solar cells, wind turbines, nuclear systems and some underlying issues of large scale composites, pipes and concrete

A Comprehensive Review of the Dynamic Applications of the Digital Twin Technology Across Diverse Energy Sectors

The energy supply sector, encompassing vital components such as generation, transmission, and distribution, holds a pivotal role in satisfying the energy demands of modern society. Its intricate web of technologies and infrastructure ensures the reliable provision of electricity from diverse fuel sources, bolstering economic advancement and enhancing overall living standards worldwide. In the context of ongoing global energy transitions, the energy sector assumes a critical role in addressing the escalating demand for alternative energy sources and adapting resource allocation strategies to align with evolving societal energy requirements. Nonetheless, the energy supply sector confronts formidable challenges, including infrastructure degradation and grid instability. Not only that, but the demand of energy supply is also expected to rise by 50% by 2050. To counter these issues and enable predictive maintenance and grid optimization, digital twin solutions have emerged as a necessity. This is particularly significant as industry integrates renewable and non-renewable energy sources while managing risks in a dynamic energy landscape that undergoes constant transformation. This paper presents a comprehensive analysis of the myriad applications, benefits, and impediments associated with digital twin technology within the energy supply sector. Employing a methodological framework grounded in systematic reviews, detailed case studies, and extensive data analysis, this review article utilizes illustrative diagrams and visual aids to enhance clarity and comprehension. These pedagogical tools elucidate essential concepts for the deployment of digital twin technology in the energy supply industry. The analysis reveals that 4.81% (35 out of 727) of the reviewed papers explored the application of digital twins in various energy sectors. The review paper yields several significant findings, including a meticulous synthesis of existing literature, an in-depth examination of case studies, an exploration of emerging trends, and the provision of informative visual aids. These collective insights offer a comprehensive grasp of the application and impact of digital twin technology in the energy supply sector

GIS-and Web-based Water Resource Geospatial Infrastructure for Oil Shale Development

The Colorado School of Mines (CSM) was awarded a grant by the National Energy Technology Laboratory (NETL), Department of Energy (DOE) to conduct a research project en- titled GIS- and Web-based Water Resource Geospatial Infrastructure for Oil Shale Development in October of 2008. The ultimate goal of this research project is to develop a water resource geo-spatial infrastructure that serves as “baseline data” for creating solutions on water resource management and for supporting decisions making on oil shale resource development. The project came to the end on September 30, 2012. This final project report will report the key findings from the project activity, major accomplishments, and expected impacts of the research. At meantime, the gamma version (also known as Version 4.0) of the geodatabase as well as other various deliverables stored on digital storage media will be send to the program manager at NETL, DOE via express mail. The key findings from the project activity include the quantitative spatial and temporal distribution of the water resource throughout the Piceance Basin, water consumption with respect to oil shale production, and data gaps identified. Major accomplishments of this project include the creation of a relational geodatabase, automated data processing scripts (Matlab) for database link with surface water and geological model, ArcGIS Model for hydrogeologic data processing for groundwater model input, a 3D geological model, surface water/groundwater models, energy resource development systems model, as well as a web-based geo-spatial infrastructure for data exploration, visualization and dissemination. This research will have broad impacts of the devel- opment of the oil shale resources in the US. The geodatabase provides a “baseline” data for fur- ther study of the oil shale development and identification of further data collection needs. The 3D geological model provides better understanding through data interpolation and visualization techniques of the Piceance Basin structure spatial distribution of the oil shale resources. The sur- face water/groundwater models quantify the water shortage and better understanding the spatial distribution of the available water resources. The energy resource development systems model reveals the phase shift of water usage and the oil shale production, which will facilitate better planning for oil shale development. Detailed descriptions about the key findings from the project activity, major accomplishments, and expected impacts of the research will be given in the sec- tion of “ACCOMPLISHMENTS, RESULTS, AND DISCUSSION” of this report

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

Energy Efficiency

Energy efficiency is finally a common sense term. Nowadays almost everyone knows that using energy more efficiently saves money, reduces the emissions of greenhouse gasses and lowers dependence on imported fossil fuels. We are living in a fossil age at the peak of its strength. Competition for securing resources for fuelling economic development is increasing, price of fuels will increase while availability of would gradually decline. Small nations will be first to suffer if caught unprepared in the midst of the struggle for resources among the large players. Here it is where energy efficiency has a potential to lead toward the natural next step - transition away from imported fossil fuels! Someone said that the only thing more harmful then fossil fuel is fossilized thinking. It is our sincere hope that some of chapters in this book will influence you to take a fresh look at the transition to low carbon economy and the role that energy efficiency can play in that process

Digital Twins in Agriculture: Orchestration and Applications

Digital Twins have emerged as an outstanding opportunity for precision farming, digitally replicating in real-time the functionalities of objects and plants. A virtual replica of the crop, including key agronomic development aspects such as irrigation, optimal fertilization strategies, and pest management, can support decision-making and a step change in farm management, increasing overall sustainability and direct water, fertilizer, and pesticide savings. In this review, Digital Twin technology is critically reviewed and framed in the context of recent advances in precision agriculture and Agriculture 4.0. The review is organized for each step of agricultural lifecycle, edaphic, phytotechnologic, postharvest, and farm infrastructure, with supporting case studies demonstrating direct benefits for agriculture production and supply chain considering both benefits and limitations of such an approach. Challenges and limitations are disclosed regarding the complexity of managing such an amount of data and a multitude of (often) simultaneous operations and supports