Data mining/analysis
In today’s era of asset management, digital twins are changing risk management, optimizing operations, and benefitting the bottom line.
Over decades of exploration and production, the oil and gas sector has accumulated vast amounts of legacy data in various formats. Artificial intelligence and machine learning present an opportunity to transform how this unstructured data is processed and used, enabling significant improvements in operational efficiency and decision-making.
A roundtable discussion during CERAWeek pointed to the necessity of a mindset shift for the oil and gas industry to tap into AI’s true potential.
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Figuring out the right price for an active oil and gas field is tricky business in the shale sector but one producer explains how it uses data analytics to get a clearer picture. -
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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The oil and gas sector is an area primed for transformational development of its operations through new digital infrastructures, GE’s CEO said in an address to technological developers and company partners.
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In a recent GE/Accenture report, surveys show that 81% of senior executives believe that big data analytics is one of the top three corporate priorities for the oil and gas industry through 2018. A striking finding was the sense of urgency felt about implementing data analytics solutions.
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IoT has become a popular phrase in various industries. For upstream oil and gas operators, an IoT infrastructure may present an opportunity improve quality control on their projects, potentially reducing costs and increasing production.
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Optimization of maintenance costs is among operators’ main concerns in the search for operational efficiency, safety, and asset availability. The ability to predict critical failures emerges as a key factor, especially when reducing logistics costs is mandatory.
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The difficulty in applying traditional reservoir-simulation and -modeling techniques for unconventional-reservoir forecasting is often related to the systematic time variations in production-decline rates. This paper proposes a nonparametric statistical approach to resolve this difficulty.
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The oil and gas industry is facing an invasion of data analytics startups who saw a wide-open gap in the market a few years ago when talk of big data first began.
