Data mining/analysis
This paper presents a robust workflow to identify optimization opportunities in gas lift wells through real-time data analysis and a surveillance-by-exception methodology.
This paper describes a data-driven well-management strategy that optimizes condensate recovery while preserving well productivity.
This work presents the development of fast predictive models and optimization methodologies to evaluate the potential of carbon-dioxide EOR and storage operations quickly in mature oil fields.
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Fed by big data loads from big operators, a university consortium and software firm are each working to make upstream data access as quick and easy as a Google search. -
With new digital platforms and technologies driving the industry in the near future, organizations are examining the ways in which their established work flows may help or hinder their ability to adopt and adapt.
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Data volumes are growing at an exponential rate. How can high-performance computing solutions help operators manage these volumes? Will faster, stronger processors and cloud computing solutions be the answer?
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Oil companies generate an enormous amount of data but are reluctant to share it. But more sharing of information may be required in the future to keep up with a rapidly changing energy landscape.
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High-performance computing is an important piece of the puzzle for operators looking to integrate field models with surface facilities. Next-generation processors and accelerators should help build the systems needed to meet industry's growing demands, but the tools may be reaching their limits.
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The Oklahoma City independent has a new-look portfolio and new operational and financial priorities. And now it has enlisted an energy research firm to leverage advanced analytics and machine learning to help get the most out of its assets.
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The technology is being proven in millions of phones and homes across the world. Now, a small group of software startups wants to introduce chat bot technology to oil and gas professionals.
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An increasingly buzzy term tossed around at industry events, “digital twin” is leveraging data analytics, machine learning, and artificial intelligence to improve efficiencies from design to decommissioning.
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As shale plays are becoming economically viable, operators have fast-adopted best practices to optimize drilling and completion processes to drive down the lifting costs. Adoption of data-driven analytics to improve completion design, drive efficiency, and yield economic gains has been less swift.
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The use of data-intensive decision making and smart risk-management solutions has resulted in the improvement of the ethical foundations underlying the industry. These digital tools and machine-based cognitive processes for risk-avoidance have also helped restore the public’s trust in the industry.
