Digital Transformation
Discover how AI and machine learning are transforming oil and gas field development by reducing subsurface uncertainty, optimizing development decisions, and maximizing long-term reservoir value from concept selection through production.
As upstream operators move beyond isolated experiments, the hardest part of the AI journey is not building a model, it is making it stick.
How 7 decades of AI research, including the emergence of agentic AI, are reshaping oil, gas, renewables, and the grid.
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While sidetracking your career path is not an easy journey, the skills and knowledge gained in petroleum engineering education can be applied in other industries during the downturn. -
AUVs aren’t limited to inspections and pipeline surveys. Deployment of a flotilla of AUVs to work on a project, and the communication among them, may someday lead to a subsea Internet of Things.
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Technology that allows researchers to see stress forming inside rock samples may help unravel some of the mysteries associated with fracture behavior.
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Always recorded but almost never used, the water hammer signal could offer completions engineers another set of insightful data if petroleum engineers can crack its code.
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IoT is the next step in the evolution of the oil and gas industry. Changes have already begun in the field installations, in the corner office, and across the oil and gas value chain.
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Permian Basin producer Callon Petroleum is attributing its data-driven approach to a routine completions practice to improved proppant placement and higher oil production.
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The oil and gas industry has a lot to gain from the adoption of big data analytics as recently highlighted examples from major service company Halliburton demonstrate.
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Through data gathering, machine learning, and the use of a supercomputer, a non-profit organization in Texas is seeking to boost oil and gas production on land owned by the states’ two largest university systems.
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Drones can access remote locations easily and can be used in the upstream and midstream industries for inspections, which can lead to reduced maintenance work.
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A look at the universities, startups, and multinational corporations in Silicon Valley, California, which are applying data science and predictive modeling for oilfield management.
