Digital Oil Field
This paper explores how artificial intelligence (AI), cognitive models, and integrated digital tools can transform readiness from periodic drills into a continuous, measurable capability.
This paper presents a competency-centered, data-driven approach implemented to strengthen control-room emergency-response capability through a cloud-hosted, scenario-based virtual plant simulator.
This paper presents an autonomous, data-driven solution designed specifically for intermittent well optimization.
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The two technology startups aim to bring scale to the visual side of oilfield automation with a new deal that will cover 90% of US energy assets. -
The company plans an innovative application of oceanographic instrumentation to maximize recovery at its Johan Sverdrup oil field in the North Sea.
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The need to understand the future trends of the oil industry has never been greater than it is today.
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In this study, the authors investigated a fully data-driven approach using artificial neural networks (ANNs) for real-time virtual flowmetering and back-allocation in production wells.
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The authors detail the development of a technique based on surface-to-borehole controlled-source electromagnetics (CSEM), which exploits the large contrast in resistivity between injected water and oil to derive 3D resistivity distributions, proportional to saturations, in the reservoir.
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In this paper, the authors describe a project to design, field trial, and qualify an alternative solution for real-time monitoring of the oil rim in carbonate reservoirs that overcomes these disadvantages.
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This industry is one often considered reactive and overly tradition-bound. These new technologies, however—and, more importantly, the drive of these researchers to harness their capabilities—prove that petroleum engineers remain at the forefront of innovation and discovery.
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The world’s largest oilfield services firm and one of the largest providers of SCADA systems have formed a joint venture to create Sensia, an integrated automation suite.
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Inspection data management system software can help companies bolster their mechanical integrity programs, but choosing the wrong software can have a lasting impact on a company’s operations. So, what goes into finding the right software for your needs?
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For too long, owner/operators have managed operational profitability using paper-based processes or monthly reporting cycles. In the new technological climate, this approach has been proven to be less effective. Enter the industrial Internet of things.
