Transforming Academic Research: Solving Previously Complex Oil and Gas Problems Using Machine Learning

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Digital transformations are slated to transform the industry by reducing expenditures, improving operations, and providing a granular view of workflows enabling more effective decision-making. In the heart of all these digitization efforts in our industry lies machine learning. Machine learning enables us to build complex models on the data collected, leading to better decisions. In the simplest terms, it is a form of artificial intelligence (AI) which is designed to learn on its own or become better as it is fed more data. These algorithms have the potential to revolutionize our workflow in the future when the applicability of AI increases.


Artificial Intelligence: The Future Of Oil And Gas

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Oil prices have fallen dramatically over last few years, forcing some major oil companies to take drastic actions such as layoffs, cutting investments and budgets, and more. In view of falling oil prices and the resulting squeeze on cash flows, the oil and gas industry has been challenged to adapt and optimize its performance to remain profitable while maintaining a long-term investment and operating outlook. Currently, oil and gas companies find it difficult to maintain the same level of investment in exploration and production as when crude prices were at their peak. Operations in the oil and gas industry today means balancing a dizzying array of trade-offs in the drive for competitive advantage while maximizing return on investment. The result is a dire need to optimize performance and optimize the cost of production per barrel.


Artificial Intelligence in Manufacturing: The Evolution of Industry - CiOL

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Artificial Intelligence is benefiting to various industries including healthcare, education and manufacturing. But what is Artificial intelligence (AI)? In Layman language, a simulator of human intelligence, which makes the decision after analyzing various data utilizing a collection of different intelligent technologies including machine and deep learning, analytics and computer vision. The fourth industrial revolution is employing AI to enhance its overall efficiency. The technology is not only helping to reduce manufacturing cost as well as it is improving productivity and quality. Manufacturing is a capital-intensive process, and once a plant is a set-up, replacing, removing or renovating is exorbitantly expensive. New machines improve performance; reduce redundancies, while improving overall quality metrics. AI is proving an alternative route to achieve all this and at extremely competitive price points. Instead of now replacing machines, manufacturers are adding AI/ML tools to pre-inspect raw materials identify defects, perform quality evaluations, and a lot more.


GE's PREDIX PLATFORM: Looking At The Road Ahead

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The author is a Silicon Valley based IIoT industry analyst & co-founder of ArcInsight Research Partners, a research & advisory group. He has also trained with business strategy consulting firms, with additional qualifications in decision analytics, Bayesian-learning approaches & risk-assessment. I wrote about GE's Predix Platform last year in the context of its immensely successful Minds Machines Conferences where the company showcased a very compelling story about its quiet but steady transformation into a digital industrial company. This is a far cry from creating avatars in a consumer or mobile context. Industrial stakes are very high.


Turning to Machine Learning for Industrial Automation Applications

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At its core, machine learning studies the construction of algorithms and learns from them to make predictions on data by building models from sample inputs. If we further break it down, machine learning borrows heavily from computational statistics (prediction modeling using computers) and mathematical optimization, which provides methods, theory and application data to those models. In essence, it creates its own data models based on algorithms and then uses them to predict defined patterns within a range of data sets. Machine-learning algorithms can be broken down into five types: supervised, unsupervised, semi-supervised, active, and reinforcement, all of which act just like they sound. Supervised algorithms are programmed and implemented by humans to provide both input and output as well as furnishing feedback on predictive accuracy during training.