Digital Transformation
Agentic AI could help upstream oil and gas operations reduce emissions by enabling real-time methane detection, optimizing flaring and energy use, and improving carbon capture efficiency.
This article examines how domain experts can use no-code ML platforms to explore decision-relevant problems, validate hypotheses, quickly build prototypes, and engage more effectively with data science teams when solutions transition toward production.
AI-driven analytics and digital platforms are reshaping offshore operations, enabling smarter, faster decision-making.
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A quick look at the term "Fourth Industrial Revolution" and links to related articles. -
Data-driven reservoir modeling is an alternative or a complement to numerical simulation and uses machine learning and data mining to develop full-field reservoir models.
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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.
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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.
