Artificial Intelligence AI Assisted Thermography to Detect Corrosion Under Insulation CUI

2021 ◽  
Author(s):  
Ayman Amer ◽  
Ali Alshehri ◽  
Hamad Saiari ◽  
Ali Meshaikhis ◽  
Abdulaziz Alshamrany
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Oil And Gas ◽  
Assessment Tool ◽  
Oil And Gas Industry ◽  
Real Field ◽  
Learning Approach ◽  
Real Time System ◽  
Gas Industry ◽  
Corrosion Under Insulation

Abstract Corrosion under insulation (CUI) is a critical challenge that affects the integrity of assets where the oil and gas industry is not immune. Its severity arises due to its hidden nature as it can often times go unnoticed. CUI is stimulated, in principle, by moisture ingress through the insulation layers to the surface of the pipeline. This Artificial Intelligence (AI)-powered detection technology stemmed from an urgent need to detect the presence of these corrosion types. The new approach is based on a Cyber Physical (CP) system that maximizes the potential of thermographic imaging by using a Machine Learning application of Artificial Intelligence. In this work, we describe how common image processing techniques from infra-red images of assets can be enhanced using a machine learning approach allowing the detection of locations highly vulnerable to corrosion through pinpointing locations of CUI anomalies and areas of concern. The machine learning is examining the progression of thermal images, captured over time, corrosion and factors that cause this degradation are predicted by extracting thermal anomaly features and correlating them with corrosion and irregularities in the structural integrity of assets verified visually during the initial learning phase of the ML algorithm. The ML classifier has shown outstanding results in predicting CUI anomalies with a predictive accuracy in the range of 85 – 90% projected from 185 real field assets. Also, IR imaging by itself is subjective and operator dependent, however with this cyber physical transfer learning approach, such dependency has been eliminated. The results and conclusions of this work on real field assets in operation demonstrate the feasibility of this technique to predict and detect thermal anomalies directly correlated to CUI. This innovative work has led to the development of a cyber-physical that meets the demands of inspection units across the oil and gas industry, providing a real-time system and online assessment tool to monitor the presence of CUI enhancing the output from thermography technologies, using Artificial Intelligence (AI) and machine learning technology. Additional benefits of this approach include safety enhancement through non-contact online inspection and cost savings by reducing the associated scaffolding and downtime.

scholarly journals A cyclic learning approach for improving pre-stack seismic processing

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dario Augusto Borges Oliveira ◽  
Daniela Szwarcman ◽  
Rodrigo da Silva Ferreira ◽  
Semen Zaytsev ◽  
Daniil Semin
Keyword(s):  
Machine Learning ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Learning Approach ◽  
Noise Attenuation ◽  
Gas Industry ◽  
Seismic Processing ◽  
Machine Learning Approach ◽  
Testing And Evaluation ◽  
Selection Of

AbstractCurrent seismic processing workflows in the oil and gas industry involve several interactions between different experts to optimize the overall data quality in various tasks, such as noise attenuation, velocity analysis and horizon picking. While many machine learning-based approaches have been proposed to support each of those steps, most of them disregard expert interactions to guide the overall optimization. This paper presents geocycles, a cyclic learning approach that mimics this iterative process, which can be applied to different pre-stack seismic processing tasks. Our method refactor these processes considering training, testing, and evaluation sub-tasks, which allow the selection of samples for greedy sequential processes targeting an overall optimum quality for very large seismic datasets. We present encouraging results showing that a cyclic structure and efficient quality metrics improved overall outcomes in up to 128% for two different seismic processing tasks in comparison to a 1-cycle machine learning approach.

scholarly journals Application of machine learning and artificial intelligence in oil and gas industry

2021 ◽  
Author(s):  
Anirbid Sircar ◽  
Kriti Yadav ◽  
Kamakshi Rayavarapu ◽  
Namrata Bist ◽  
Hemangi Oza
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Gas Industry

Guest Editorial: Artificial Intelligence in the E&P Industry: What Have We Learned So Far and Where to Next?

2021 ◽  
Vol 73 (01) ◽  
pp. 12-13
Author(s):  
Manas Pathak ◽  
Tonya Cosby ◽  
Robert K. Perrons
Keyword(s):  
Artificial Intelligence ◽  
Science Fiction ◽  
Oil Recovery ◽  
Oil And Gas ◽  
Autonomous Systems ◽  
Oil And Gas Industry ◽  
Human Judgment ◽  
Gas Industry ◽  
The Media ◽  
And Function

Artificial intelligence (AI) has captivated the imagination of science-fiction movie audiences for many years and has been used in the upstream oil and gas industry for more than a decade (Mohaghegh 2005, 2011). But few industries evolve more quickly than those from Silicon Valley, and it accordingly follows that the technology has grown and changed considerably since this discussion began. The oil and gas industry, therefore, is at a point where it would be prudent to take stock of what has been achieved with AI in the sector, to provide a sober assessment of what has delivered value and what has not among the myriad implementations made so far, and to figure out how best to leverage this technology in the future in light of these learnings. When one looks at the long arc of AI in the oil and gas industry, a few important truths emerge. First among these is the fact that not all AI is the same. There is a spectrum of technological sophistication. Hollywood and the media have always been fascinated by the idea of artificial superintelligence and general intelligence systems capable of mimicking the actions and behaviors of real people. Those kinds of systems would have the ability to learn, perceive, understand, and function in human-like ways (Joshi 2019). As alluring as these types of AI are, however, they bear little resemblance to what actually has been delivered to the upstream industry. Instead, we mostly have seen much less ambitious “narrow AI” applications that very capably handle a specific task, such as quickly digesting thousands of pages of historical reports (Kimbleton and Matson 2018), detecting potential failures in progressive cavity pumps (Jacobs 2018), predicting oil and gas exports (Windarto et al. 2017), offering improvements for reservoir models (Mohaghegh 2011), or estimating oil-recovery factors (Mahmoud et al. 2019). But let’s face it: As impressive and commendable as these applications have been, they fall far short of the ambitious vision of highly autonomous systems that are capable of thinking about things outside of the narrow range of tasks explicitly handed to them. What is more, many of these narrow AI applications have tended to be modified versions of fairly generic solutions that were originally designed for other industries and that were then usefully extended to the oil and gas industry with a modest amount of tailoring. In other words, relatively little AI has been occurring in a way that had the oil and gas sector in mind from the outset. The second important truth is that human judgment still matters. What some technology vendors have referred to as “augmented intelligence” (Kimbleton and Matson 2018), whereby AI supplements human judgment rather than sup-plants it, is not merely an alternative way of approaching AI; rather, it is coming into focus that this is probably the most sensible way forward for this technology.

Artificial Intelligence and Robotics in the Oil Industry: Will it Take My Job?

2021 ◽  
Author(s):  
Armstrong Lee Agbaji
Keyword(s):  
Artificial Intelligence ◽  
Oil And Gas ◽  
New Technologies ◽  
Oil Industry ◽  
Oil And Gas Industry ◽  
Human Robot Interaction ◽  
Gas Industry ◽  
Deep Dive ◽  
The Future ◽  
And Robotics

Abstract Historically, the oil and gas industry has been slow and extremely cautious to adopt emerging technologies. But in the Age of Artificial Intelligence (AI), the industry has broken from tradition. It has not only embraced AI; it is leading the pack. AI has not only changed what it now means to work in the oil industry, it has changed how companies create, capture, and deliver value. Thanks, or no thanks to automation, traditional oil industry skills and talents are now being threatened, and in most cases, rendered obsolete. Oil and gas industry day-to-day work is progressively gravitating towards software and algorithms, and today’s workers are resigning themselves to the fact that computers and robots will one day "take over" and do much of their work. The adoption of AI and how it might affect career prospects is currently causing a lot of anxiety among industry professionals. This paper details how artificial intelligence, automation, and robotics has redefined what it now means to work in the oil industry, as well as the new challenges and responsibilities that the AI revolution presents. It takes a deep-dive into human-robot interaction, and underscores what AI can, and cannot do. It also identifies several traditional oilfield positions that have become endangered by automation, addresses the premonitions of professionals in these endangered roles, and lays out a roadmap on how to survive and thrive in a digitally transformed world. The future of work is evolving, and new technologies are changing how talent is acquired, developed, and retained. That robots will someday "take our jobs" is not an impossible possibility. It is more of a reality than an exaggeration. Automation in the oil industry has achieved outcomes that go beyond human capabilities. In fact, the odds are overwhelming that AI that functions at a comparable level to humans will soon become ubiquitous in the industry. The big question is: How long will it take? The oil industry of the future will not need large office complexes or a large workforce. Most of the work will be automated. Drilling rigs, production platforms, refineries, and petrochemical plants will not go away, but how work is done at these locations will be totally different. While the industry will never entirely lose its human touch, AI will be the foundation of the workforce of the future. How we react to the AI revolution today will shape the industry for generations to come. What should we do when AI changes our job functions and workforce? Should we be training AI, or should we be training humans?

Evaluation of Machine-Learning Tools for Predicting Sand Production

2021 ◽  
Author(s):  
Afungchwi Ronald Ngwashi ◽  
David O. Ogbe ◽  
Dickson O. Udebhulu
Keyword(s):  
Machine Learning ◽  
Niger Delta ◽  
Oil And Gas ◽  
Back Propagation ◽  
Oil And Gas Industry ◽  
Learning Tools ◽  
Sand Production ◽  
Data Set ◽  
Test Set ◽  
Gas Industry

Abstract Data analytics has only recently picked the interest of the oil and gas industry as it has made data visualization much simpler, faster, and cost-effective. This is driven by the promising innovative techniques in developing artificial intelligence and machine-learning tools to provide sustainable solutions to ever-increasing problems of the petroleum industry activities. Sand production is one of these real issues faced by the oil and gas industry. Understanding whether a well will produce sand or not is the foundation of every completion job in sandstone formations. The Niger Delta Province is a region characterized by friable and unconsolidated sandstones, therefore it's more prone to sanding. It is economically unattractive in this region to design sand equipment for a well that will not produce sand. This paper is aimed at developing a fast and more accurate machine-learning algorithm to predict sanding in sandstone formations. A two-layered Artificial Neural Network (ANN) with back-propagation algorithm was developed using PYTHON programming language. The algorithm uses 11 geological and reservoir parameters that are associated with the onset of sanding. These parameters include depth, overburden, pore pressure, maximum and minimum horizontal stresses, well azimuth, well inclination, Poisson's ratio, Young's Modulus, friction angle, and shale content. Data typical of the Niger Delta were collected to validate the algorithm. The data was further split into a training set (70%) and a test set (30%). Statistical analyses of the data yielded correlations between the parameters and were plotted for better visualization. The accuracy of the ANN algorithm is found to depend on the number of parameters, number of epochs, and the size of the data set. For a completion engineer, the answer to the question of whether or not a well will require sand production control is binary-either a well will produce sand or it does not. Support vector machines (SVM) are known to be better suited as the machine-learning tools for binary identification. This study also presents a comparative analysis between ANN and SVM models as tools for predicting sand production. Analysis of the Niger Delta data set indicated that SVM outperformed ANN model even when the training data set is sparse. Using the 30% test set, ANN gives an accuracy, precision, recall, and F1 - Score of about 80% while the SVM performance was 100% for the four metrics. It is then concluded that machine learning tools such as ANN with back-propagation and SVM are simple, accurate, and easy-to-use tools for effectively predicting sand production.

A Comprehensive Review of the Fourth Industrial Revolution IR 4.0 in Oil and Gas Industry

2021 ◽  
Author(s):  
Cenk Temizel ◽  
Celal Hakan Canbaz ◽  
Hakki Aydin ◽  
Bahar F. Hosgor ◽  
Deniz Yagmur Kayhan ◽  
...  
Keyword(s):  
Industry 4.0 ◽  
Industrial Revolution ◽  
Oil And Gas ◽  
Petroleum Industry ◽  
Oil And Gas Industry ◽  
Learning Technologies ◽  
Real Field ◽  
Gas Industry ◽  
History Of ◽  
History Of Use

Abstract Digital transformation is one of the most discussed themes across the globe. The disruptive potential arising from the joint deployment of IoT, robotics, AI and other advanced technologies is projected to be over $300 trillion over the next decade. With the advances and implementation of these technologies, they have become more widely-used in all aspects of oil and gas industry in several processes. Yet, as it is a relatively new area in petroleum industry with promising features, the industry overall is still trying to adapt to IR 4.0. This paper examines the value that Industry 4.0 brings to the oil and gas upstream industry. It delineates key Industry 4.0 solutions and analyzes their impact within this segment. A comprehensive literature review has been carried out to investigate the IR 4.0 concept's development from the beginning, the technologies it utilizes, types of technologies transferred from other industries with a longer history of use, robustness and applicability of these methods in oil and gas industry under current conditions and the incremental benefits they provide depending on the type of the field are addressed. Real field applications are illustrated with applications indifferent parts of the world with challenges, advantages and drawbacks discussed and summarized that lead to conclusions on the criteria of application of machine learning technologies.

Proposing a Quick Assessment Tool for Heavy Vehicle Commercial Drivers’ Fatigue in Oil and Gas Industry of Malaysia

2017 ◽  
Vol 23 (8) ◽  
pp. 7742-7745
Author(s):  
Ahmad Shahrul Nizam B Isha ◽  
Asrar Ahmed Sabir ◽  
Zulkipli Bin Ghazali ◽  
Md Akhir Bin Mohd Sharif ◽  
Siti Zaharah Ishak
Keyword(s):  
Oil And Gas ◽  
Assessment Tool ◽  
Oil And Gas Industry ◽  
Heavy Vehicle ◽  
Gas Industry ◽  
Commercial Drivers ◽  
Quick Assessment

scholarly journals NDT application for detection of corrosion under insulation in Vietnam

2021 ◽  
Vol 11 (1) ◽  
pp. 48-54
Author(s):  
The Man Nguyen ◽  
Duc Vinh Vu
Keyword(s):  
High Temperature ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Piping System ◽  
Gas Industry ◽  
Non Destructive Evaluation ◽  
The Cost ◽  
Fluid Leak ◽  
Non Destructive ◽  
Corrosion Under Insulation

: In the oil and gas Industry, insulation materials can be used widely for piping system, tank and vessel in either low or high temperature applications. CUI can cause equipment degradation, fluid leak, which lead to explosion or environmental pollution and the cost will very expensive. Therefore, CUI need to be detected early to prevent damage. Through experiment, Center for Non-Destructive Evaluation (NDE) studied on establishing and appliying 4 NDT procedures for CUI examination on typical petroleum piping using in Vietnam. A discussion is presented below

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