AI/machine learning
Best practices are not static; they evolve alongside advancements that redefine what is achievable.
New strides in computer vision, well controls indicators, and BOP alignment were showcased at the recent Offshore Technology Conference.
Gautam Swami, manager of corporate R&D at NOV and SPE member, shares his experiences in building a career in oil and gas R&D, discusses how innovation is shaping the industry, and offers guidance to young professionals.
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This paper describes an automated work flow that uses sensor data and machine-learning (ML) algorithms to predict and identify root causes of impending and unplanned shutdown events and provide actionable insights. -
Increasing accuracy in models is often obtained through the first steps of data transformations. This guide explains the difference between the key feature-scaling methods of standardization and normalization and demonstrates when and how to apply each approach.
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Researchers have created software that borrows concepts from Darwinian evolution, including “survival of the fittest,” to build AI programs that improve generation after generation without human input.
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The AI journey starts with a single step, but too many companies take the wrong first step.
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There is often an assumption that big data, together with machine learning, will solve whatever problems asset-heavy industries such as oil and gas face. This is not the case; big data alone isn’t enough. We need something else to solve these problems, and the answer lies in the world of physics.
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AltaML has announced a partnership with engineering and design firm Kleinfelder in which the two companies will pair 3D reality scans of facilities with artificial intelligence to look for potential problems and risks.
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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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For the upstream industry, where improvement in efficiency or production can drive significant financial results, there is no question that the size of the digital prize is huge. So are the challenges.
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Modeling immensely complex natural phenomena such as how subatomic particles interact or how atmospheric haze affects climate can take hours on even the fastest supercomputers. Now, work posted online shows how AI can easily produce emulators that can accelerate simulations by billions of times.
