Learn what's new and what's news in today's digital landscape

What companies use to power their decisions, and how it can impact your business



Using machine learning, researchers at Texas A&M University have developed an algorithm that automates the process of determining key features of simple hydrocarbon reservoirs. Simulating the geology of the underground environment using reinforcement Learning can greatly facilitate forecasting of oil and gas reserves, predicting groundwater systems and anticipating seismic hazards.

Enterprise AI


Learn how Shell, C3.ai, Microsoft and Baker Hughes choose to solve complex industrial problems with smarter collaborations. The Open AI Energy Initiative aims to grow AI use across the energy and process manufacturing industries by creating new AI and physics-based models, monitoring, diagnostics and more to help solve critical industry needs.



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.

Maritime Logistics


ABS classification society SVP discusses how a digital strategy applied to an FPSO can impact its entire value chain, from equipment and inventory to operational efficiency, including optimization of inspections and onboard activities. Coupling these digital solutions with traditional risk-based inspection and maintenance planning techniques has shown a 10:1 ROI opportunity over the total asset life due to optimized repair and inspection 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.

Algorithm X lab


It’s a matter of time before Machine learning is heavily embedded in every oil & gas project. AI and data analytics technology is perfectly adapted to augment engineers in tackling the complexities of Oil and Gas projects from geological modelling, drilling operations to asset operation and maintenance. Predictions by McKinsey suggests that adopting AI and machine learning in oil & gas supply chains could gain $50 billion in savings, increasing profits.

Oil & Gas Engineering


CEO of Deepwater Subsea shares how his firm help its oil & gas clients avoid BOP downtime and incidents. Using Seeq software’s unique analytics capabilities, SMEs at the monitoring center apply their domain expertise to help detect BOP system anomalies. What’s more is that this innovative service allowed the operators to obtain approval from Bureau of Safety and Environmental Enforcement to extend BOP testing intervals from 14 to 21 days, saving them $10 million annually per rig.



Shell’s Data Science Manager shares with AI expert Bernard Marr what it takes to successfully scale a data strategy within large organisation. The key is to think about project application that are going to have a significant business impact immediately and use that to inform the KPIs that really matter to the business. Then, line up to that, which data really matters and how to invest in data quality, data standards, and technology to support doing this at scale.



Well trajectory planning is a high-stake and complex multi-disciplinary work activity for Oil & Gas operators. White space energy co-founder Norbert Dolle proposes a collaborative game-based approach to well trajectory planning supported by Artificial Intelligence. Using AI as a virtual player, this approach leads to a complete overview of the option space, and associated trade-offs across different business drivers, at a fraction of the time that it takes manual workflows to give similar insight.



This article intends to provide business leaders in the oil and gas space with an idea of what they can currently expect from AI in this industry. 4 Software vendors share how they helped early adopter operators implemented advanced predictive analytics, which combines engineering, data science, and computing power to enable engineers forecast yields, predict failure events and maximize industry assets.

Offshore Engineering


Eric Haun shares how industry innovators are leveraging advanced sensor technologies, big data, and real-time analytics to foresee and prevent costly component failures. According to a study published in 2018 by the Oil and Gas Authority, Technology Leadership Board, the adoption of data analytics and digital technologies for asset maintenance and operations could increase production and lower maintenance costs, presenting a value of £1.5 billion ($1.8 billion) annually to the UK continental shelf (UKCS) alone.



Raghav Bharadwaj debates how AI-enhanced data search and discovery might soon be applied in the oil and gas industry across many business functions, such as production and maintenance. There are several marquee commercial offerings using NLP, that enable oil and gas companies to organize their internal data in ways which make it easy for employees to search and discover patterns that could optimize their business processes and solve issues more efficiently.