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Discover what oil and gas majors are doing to optimise their assets, reduce non-productive time and lower operational costs. The article shares interesting example of the use of deep-learning models for optimized drilling and production, predictive maintenance solutions to forecast equipment breakdowns before they can have an adverse impact on their KPI and bottom line to adopting AR/VR for subsurface studies, training, maintenance and planning.



Plantservice editor shares six case studies illustrating some of the numerous ways predictive maintenance and Prescriptive maintenance are transformative. Clear benefits in preventing costly unplanned downtime and lower costs have improved financial justification and driven adoption in a wide range of industries.

Oil & Gas Engineering


Dr Kanokogi from Yokogawa shares how harnessing data analytics and AI can augment decision making when dealing process abnormalities in petrochemical plants. Two case studies illustrate the power of Artificial intelligence in identifying the main factors contributing to abnormal situations and pointing out the specific sensors indicating the causes. The operators can then concentrate on much smaller number of manageable elements to solve the problem.

Plant Services


In every industry and across the world, companies driven to improve reliability at their facilities are testing and investing in new ways to optimize their assets. Their efforts are paying off in greater uptime, efficiency, safety, and cost savings. Experts at seven different industrial companies describe their PdM and RxM progress, including successes that are helping keep their programs alive. Here are their stories, followed by some examples of AI enabling solutions.

Hart Energy


SparkCognition VP of Oil & Gas explains how AutoML opens up the full potential of predictive maintenance to the oil and gas industry. Auto ML platforms help organisations scale predictive maintenance projects by ingesting operational sensor data and automatically build machine learning models that predict the operating state and remaining useful life of a given asset. This reduces the time required to create models by orders of magnitude.