Machine learning use cases in oil and gas?
Video answer: Machine learning in oil and gas
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- Machine learning and other big data applications could save the oil and gas industry as much as $50 billion in the coming decade, according to management consulting firm McKinsey & Company.
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The 7th annual Machine Learning in Oil & Gas Conference will showcase how leading energy operators and service companies are deploying Machine Learning and AI models at scale, increasing productivity and enabling more efficient and environmentally sustainable outcomes.
This uses machine learning in oil & gas to create automated analytical models. This, otherwise known as predictive maintenance, will keep industrial equipment working. The machine learning algorithms used in Predix can process data from equipment sensors. It will then analyse all the relevant information.
Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward.
With AI and machine learning, companies can deliver the returns investors require, improve return on assets, and manage downside risks by turning much of the data already collected into usable and valuable insight. Learn how enterprise AI is revolutionizing the oil and gas sector. Get Started Now.
Machine learning use cases in the Oil & Gas industry have up until recent years been mostly confined to the use of deep learning techniques in seismic interpretation. Presently, with the exponential volume of data being generated by the industry, novel applications are being discovered with increasing frequency.
Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications, (PDF) provides a critical training and resource tool to help engineers understand machine learning theory and practice, particularly referencing use cases in oil and gas.
Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications delivers a critical training and resource tool to help engineers understand machine learning theory and practice, specifically referencing use cases in oil and gas.
Oil, Gas and Utilities Data Analytics and Machine Learning for the Oil, Gas and Utilities Harness the power of big data and AI to fuel innovations in discovery, extraction, and downstream delivery of energy Improve Production and Reduce Downtime with Data Analytics and AI
Various machine learning (ML) applications in the oil and gas industry, including productivity effectiveness, binary classification of ML application in geomechanical log property prediction (shear wave and compression wave travel time predictions), TOC prediction, net present value prediction, frac intensity binary classification, and rate of penetration prediction, have been detailed and illustrated in an easy workflow in Python.