Data mining/analysis
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.
Technology and partnerships play a pivotal role in how the oil industry finds and produces energy from frontier regions and brownfields, both now and in the future.
The authors make the case that data science captures value in well construction when data-analysis methods, such as machine learning, are underpinned by first principles derived from physics and engineering and supported by deep domain expertise.
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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. -
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.
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Digitalization in the oil and gas industry has been the focus of much discussion, but little has been written on the slow rate of adoption. This paper outlines some of the barriers the industry faces as it assimilates into Industry 4.0—automation and data integration in manufacturing.
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The 5-year-old software startup is getting noticed by the oil and gas industry for its ability to accelerate analytics projects by taking care of all the tedious work involved with data wrangling.
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Well-placement optimization is one of the more challenging problems in the oil and gas industry. Although several optimization methods have been proposed, the most-used approach remains that of manual optimization by reservoir engineers.
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Companies such as Google, Schlumberger, Shell, and Encana are working to turn the industry’s rat’s nest of data into a goldmine of insight, analysis, and technology.
