Data & Analytics
This research focuses on combining physics-based expert rules with machine learning to improve the detection of failure-related events in electrical submersible pumps.
A combination of physics principles and machine-learning techniques is used in this work to develop a virtual flowmeter for oil-production optimization in electrical-submersible-pump-lifted oil wells, resulting in reliable generalization.
This paper presents the deployment of an artificial intelligence-enabled autonomous gas lift optimization system, integrating real-time centralized advanced process control with cloud-based analytics to enhance artificial gas lift performance in producer wells.
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SponsoredTo optimize decision-making, minimize risk, and create value, oil and gas companies can turn to liberated, contextualized data. For exploration or drilling, liberated, contextualized data can help the upstream industry make trustworthy decisions that save time and costs. This paper explains how. -
SponsoredFor surveying, exploration, analytics, and a whole host of processes, liberated, contextualized data tailored to the environments of E&P subsurface will empower confidence, speed, reliability, agility, and most importantly, innovation. This is how Aker BP is doing it.
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As SPE members deal with challenging and uncertain times, they are reminded that there are a number of programs available to provide support and key resources. Read more to see which ones are useful to you and your career.
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“Dark data” may be a relatively unknown term for many, even though all contribute to its growing presence. It represents data that is accumulated continually by the interconnected systems used every day. A recent survey estimated that an average of 55% of accumulated data is dark and unexplored.
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An estimate says data centers, including edge sites, will soon use four times the energy all data centers used in 2018. Can it be true?
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Support vector machines are powerful for solving regression and classification problems. You should have this approach in your machine-learning arsenal, and this article provides all the mathematics you need to know. It's not as hard you might think.
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When the field emerged at the end of the 20th century, it was hoped that computers would be able to operate on their own, with human-like abilities—a capability known as generalized AI.
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As deep learning matures and moves from the hype peak to its trough of disillusionment, it is becoming clear that it is missing some fundamental components.
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Organizations interested in realizing the benefits of having data at the center of their firm will need to define numerous specific work scopes for the conversion of unstructured to structured data.
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Edge—or, in-field, device-level—computing is being driven by the need for data from individual wells to be analyzed and processed at the wellsite instead of in data centers for early and accurate decision making.
